An Essay Concerning Artificial Intelligence, Intuition and The Mind

Intuition may be a pattern recognition algorithm

Abraham Thomas, Copyright 1997 (Republished with permission from the author)

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This essay offers an unusual profile of the mind. It is based on a novel insight concerning intuition, a little known and mysterious mental faculty. The profile begins with an overview of some of the current problems faced by science in understanding the mind. It outlines seven specific issues, which shroud major aspects of human intelligence in mystery. It goes on to explain a new algorithm, the logic of which appears to point to answers to these very puzzles. (An algorithm solves a problem in a finite number of steps, by executing a set of instructions in a specific order). This algorithm uses a simple but unconventional logic in an expert system which diagnoses diseases.

This logic has classic grace and exceptional power. It appears to have immediate pertinence to the speed and subtlety of the intuitive process. The mind instantly identifies a single thought, in context, from a lifetime of memories. The act is equivalent to a search process which instantly locates a single needle on a vast beach. The logic of the algorithm may make such an achievement feasible. The ingenuity of the logic enables one to imagine a viable process which can convert a network transmitting nerve impulses into a real-time system with knowledge, feelings, consciousness and awareness.

Based on this critical insight, this essay presents a hypothesis concerning the mind. It suggests where human memory may be stored, how memory can be recalled, how objects and events may be recognised, and how the mind may control the body. The thesis suggests how emotions, judgement and will may finally manipulate the system.


Barriers to understanding the mind. How does the mind internally represent information? How does it instantly isolate a single pattern from a mass of interviewing patterns? How does it handle “uncertainty”? How does it achieve this in an astronomically large search space? How is such speed achieved despite slower neuronal transmissions? Does it use a reasoning process? Where is memory stored? A brief survey of these issues.

A new algorithm. Describes an algorithm, which successfully diagnoses diseases. Essentially, it reverses the logic of the search process from selection to elimination, to achieve remarkably speedy results.

Instant recognition. When presented with unique links, the algorithm achieves instant recognition in massive search spaces. It logically handles uncertainty, avoids stupid questions and is holistic. It also ignores the age-old reasoning chains of science, travelling a new avenue in the application of inductive logic.

The nerve cell and recognition. Currently, nerve cells are believed to be computational devices. A new recognition role is suggested for neurons. They may recognise incoming patterns. Recognition may explain such phenomena as the modification of pain, the focus of attention, awareness and consciousness.

Memory. Recognition is “the establishment of an identity”. It may be achieved by comparing the features of an entity to those in memory. Recognition may mandate memory. Nerve cells may carry such memory. Feelings may be nerve impulses, the recognition of which may provide context for the recall of memory.

Recognition of objects. Nerve channels project from point to point, observing neighbourhood relationships. Such mapping may suggest a matrix type transmission. Intuition may be the instant recognition of such cyclic transmitted pictures. Cortical association regions recognise objects and may transmit pictures, for recognition by the system.

Motor control. Instant intuitive recognition of pictures may empower motor control functions. Persisting iterating patterns may form the basis for achieving objectives. Such goal patterns may be triggered by feelings. Habitual activities may be recalled through intuitive and iterative pattern recognition by the cerebellum.

Event recognition. Intuitive iterating patterns are suggested as enabling the recognition of events. Event recognition may be the key to complex thought processes. Event recognition may automatically trigger feelings.

The goal drive. Iterating goal patterns may provide basic drives and long term goals and may represent the “purpose” of the system. Purpose is set by the current feeling. The will of the system may be decided by the limbic system which may determine the “current feeling”.

The mind. Consciousness may be an independent intelligence, which expresses judgment and will, and resides in a restricted group of nerve channels. The limbic system may over rule will to determine the current feeling and hence set goals for the system.

An expert system shell. Details of the design of an AI shell program, which can be utilised to create expert systems. Explains simple method of knowledge input. Suggests areas in which expert systems can be helpful.


Barriers to Understanding the Mind

Artificial Intelligence awaits a breakthrough. This essay concerns Artificial Intelligence, pattern recognition and the concept of mind. The first of these, the term “Artificial Intelligence” (AI) originated in the early sixties, representing, at the time, an ambitious effort to define human intelligence for simulation by machines. The AI effort has succeeded in solving many problems which were believed to require intelligence, including those in information processing, pattern recognition, game playing and medical diagnostics. Yet, several decades later, as continuing research unravels the awesome complexity of the mind, the scientific community has serious doubts as to whether true AI can ever be developed. AI faces a series of hurdles in defining human intelligence. A new view from a different perspective may overcome some of these restraints.

The problem of internal representation. The primary restraint is the mystery surrounding the internal language of the mind. An information processing system may receive data as language, formulae, or even digital readouts. The system must translate these into its own internal representation. Computers manage with the digital format. These are stored in memory, recalled, processed and then translated into an acceptable output mode. In AI, problems are translated into specialised languages. Problem specific languages assist programs to play chess, or diagnose diseases. This need for specialised languages partitions AI solutions into compartments. There is no single way in which problems can be represented in AI to tackle chess, diagnostics, chemical analysis and banking. While the ultimate goal of AI may be to become a single equivalent to human intelligence, its own languages fail to communicate with each other. As opposed to this, the internal language used by the mind appears to fathom the whole world as we know it. This mystery is sought to be addressed in this essay, using the logic of a new algorithm. The logic may point to a single internal representation, for use by the mind. This may be its own interior language of communication.

Pattern Recognition. The second issue that has baffled AI researchers is the problem of how to identify a problem as belonging to the field of mathematics, vision, or game playing, even before attempting to solve it. With its abstract qualities, one can see difficulties in identifying a problem. Let alone identify a problem, AI efforts have failed to even identify a tangible physical object, such as a face. Today, in spite of huge advances in technology, a computer cannot identify a particular face as belonging to a particular person. The difficulty is that all recognisable objects and events in our environment have innumerable shared qualities. For a computer, they form trillions of patterns, which overlap each other. Establishing the identity of a single pattern among a range of overlapping patterns is called pattern recognition. The recognition of a known face is a pattern recognition task. In AI, a computer algorithm may follow a logical procedure to solve this problem. A pattern recognition algorithm may attempt to establish the identity of a seen pattern through a sequence of logical steps. It may seek to identify a seen face as one belonging to a known person.

An exact match an impossibility. Current AI algorithms attempt to identify a pattern by matching its characteristics strictly with that of a known pattern. The characteristics of known patterns can be stored in the memory of computers for recall. Consider the problems in the recognition of a face. There are billions of faces in the world. They share thousands of common features. The characteristics of colour, skin texture, facial features and makeup overlap each other on a virtually infinite scale. People age, grow beards or change appearances with moods. The changes caused by light and shade add further complexity. In such an environment, where patterns themselves have millions of shifting characteristics, it is virtually impossible to find an exact match even if patterns are matched at the microscopic level of detail. This essay suggests an algorithm which can establish the identity of a pattern in such a complex and changing environment.

The problem of uncertainty. The third issue which has posed problems for AI programs is the factor of “uncertainty”. Computers work with a “Yes or No” logic. A characteristic belongs to a pattern, or it does not. A pattern can be selected, or rejected on this basis. Unfortunately many characteristics have vague relationships to patterns. They are only sometimes present. “Fuzzy logic” attempts to handle vagueness by giving grades to a characteristic, such as short, medium height, tall and very tall. While this helps to define a characteristic in greater detail, it fails to handle identification of a person who sometimes wears spectacles. A computer can match “wears glasses”, or “does not wear glasses”. It cannot handle both. Unfortunately most patterns have such variable qualities. This essay attempts to show how such uncertainty can still help pattern recognition.

Instant identification of context. The fourth issue, which has frustrated AI research is the inadequacy of available tools to gauge the awesome size of the search space. When an AI program attempts machine translation of a word in context, it must store contextual data and recall this through a search process. It is like searching for a needle on the beach. The mind instantly identifies context. Every seen object or event fetches its own contextual background. When the word “pool” is used with “swim”, it suggests one meaning and quite another when used with “cartel”. As we read, specific meanings, which exactly suit the context, are instantly recalled. The mind holds a lifetime of memories and associative thoughts. Yet it instantly identifies a single contextual meaning from such a gargantuan search space. Computers seek an item in memory through a serial match. One characteristic of the perceived object is compared with the characteristic of an item in memory. If this matches, the second characteristic is compared and so on, in a systematic search.

An intractable search problem. The search space is enormous. In AI, a systematic search brings related problems as to where to begin a search, and the direction of the search. “Heuristics” is a term used for determining a search direction. If one is searching for a needle on the beach, heuristics would suggest a search to the North to locate it. But such solutions work only in small search spaces. In spite of many attempted shortcuts, all such search algorithms eventually face the problem of a “combinatorial explosion”. The back and forth search paths become intractably prolonged and cumbersome. While it takes milliseconds for the mind to locate a memory in context, the AI search and match algorithm would take years, if it was to recall a single memory from a lifetime of memories. This essay suggests an algorithm which can make instant identification practical for the mind in the context of a large search space.

A slower processing mechanism. The fifth puzzle is that the human nervous system is known to process data far slower than a computer. (1) While messages in integrated circuits travel at the speed of light, nerve impulses travel just a few yards per second. While computers process information in millions of cycles per second, the mind runs at between 50 and 10,000 cycles per second. When one considers the enormous size of the memory bank of the mind, how does a slower processing system achieve such incredible speed in locating one memory from trillions of memory traces? This process of instant identification is usually called intuition, a hitherto unexplained and mysterious capability of the mind. Parallel processing by the billions of nerve cells in the nervous system does explain some of the complexity of the mind. Even then, no known search algorithm can achieve such precision with such speed. This essay suggests a search algorithm which could be used by the mind to practically achieve the speed of intuition, even within the limitations of the slower processing speeds of the mind.

No chain of reasons. The sixth issue is the mystery surrounding the reasoning processes of the mind. AI programs attempt to give “backward chaining”. When a solution is offered for a problem, step by step reasoning is provided for the final conclusions. A chain of reasons links the premise to the conclusion. Yet, the average person detects a mistake in the syntax of a sentence, without necessarily knowing anything about nouns, verbs, prepositions, deep structure, or other intricacies of grammar. When a person pays attention to a sentence, errors are detected, without always knowing why they are errors. Thus the reasoning processes used by AI do not appear to be the methods used by the mind. This essay suggests that the mind may be constructed around a pattern recognition model, which does not apply reasoning chains to draw its conclusions.

Where does memory reside ? The seventh issue that has baffled scientific research is the scarcity of data concerning the location of human memory. (2) Classic experiments carried out in the early part of this century on the memories of rats concluded that no particular location of the brain stored memories and that memories were somehow stored in a distributed fashion across the entire network. Current theory supports this hypothesis that memory is a network phenomenon. Research from the seventies in “neural networks” suggested that a network could be induced to carry a memory through their tendency to balance the relationships between various nodes. By providing “weightage” to nodes, it was possible for units of memory to be stored. Such an explanation implied that the nodes were devices which received inputs, carried out certain computation and sent out nerve signals. Opposing this theory, this essay suggests a recognition rather than a computational role for nerve cells. In the process, the paper suggests a location for human memory.

A New Algorithm

Recognition and intelligence. Consider the process of reading. The words are just black and white patterns on paper. Recognition of the patterns conveys the purpose of the author to the reader. A single message on paper can move an army. The act of recognition of the patterns on the paper provides a powerful, but invisible link. If we did not comprehend the recognition process, the arrival of a march order would appear to have a puzzling response. The nervous system appears a mysterious network, with billions of inter-linked communicating nodes. The process of becoming conscious, or of paying attention appear as baffling activities of the system, without any rational explanation. This essay shows how instant recognition of patterns by neural processes can reasonably trigger intelligent activity in real time. Recognition appears to be the key to intelligence.

The Intuitive Algorithm (IA). While the geography and functions of the human nervous system are well known and well documented, the mind remains a mysterious entity. The key insight to the answers suggested in this essay come from a diagnostic expert system which uses a new pattern recognition algorithm. It logically achieves virtually instant recognition in a large search space – the suspected quality of intuition. A similar logic can enable intuition to achieve the equivalent of instantly finding a needle on the beach. It removes the mystery surrounding intuition. It can be viewed as a practical process which can identify a single item from an astronomically large database. It grants the mind the ability of timely recognition in context. The insight opens to view the awesome range and power of an intelligently interactive mind. The concept begins with the expert system. It uses a singular algorithm. Let us call it the Intuitive Algorithm (IA).

The conventional expert system. When presented with a list of indicated symptoms, a diagnostic expert system identifies a disease. Its database contains hundreds of diseases and their symptoms, including many commonly shared symptoms. If a disease is a pattern, the objective is to identify a single pattern in a collection of interweaving patterns. As explained before, traditional expert systems achieve this with an open ended search, based on indicated symptoms. The database is searched for a disease that exhibits the first symptom. The first located disease having the first symptom is tested for the second symptom. If the test fails, a new disease with the first symptom is located and the second symptom is again tested. Each new symptom brings new diseases into evaluation. The search ends when all the presented symptoms match the indicators of a single disease.

The IA process. IA uses a different approach in a logical search of a database. Each disease is stored with one of three (“Yes” (Y), “Neutral” (U), or “No” (N) ) relationships to each symptom question. Y means a positive link – the symptom is always present in the disease. U means the symptom is sometimes present. And N means the symptom is absent for the disease. After each answer to a presented symptom question, the Y/U/N relationships of all diseases are tested in a single step, just the way all cells in a spreadsheet are instantly recalculated. The Y/U/N relationships are entered specifically for their negative impact. An “Yes” answer eliminates all “N” diseases. If the problem is unilateral, all bilateral eye diseases are eliminated. A “No” answer eliminates all “Y” diseases. If visual acuity is not affected, all eye diseases which impact on visual acuity are eliminated. IA also purges questions which have “Y’ relationships only to eliminated diseases. The questioning process begins with the question which has the maximum number of “Y” relationships. It ends when the presented symptoms eliminate all but a single disease. Specific questions can then confirm the diagnosis. If all diseases are eliminated, the conclusion is that the presented symptoms do not match any disease in the database. For IA, it is then an unknown disease. Such a problem solving approach gives IA some exceptional capabilities.

IA circumvents “stupid questions”. Normal search algorithms serially seek to match a symptom with a single disease. IA narrows the search faster by evaluating the entire database concerning the current answer. IA is holistic. Doctors know that the lack of a particular symptom clearly indicates the absence of a particular disease. So, a subsequent query which suggests the possibility of that disease is a “stupid question”. If a patient reports a lack of pain, a subsequent question posing the possibility of a disease which always presents a powerful pain symptom is, naturally, considered stupid. Such a question annoys the user. With their “back and forth, open ended” serial searches, a traditional expert system is blind to the global impact of a previous answer on subsequent questions. Additional steps are required to correct this defect. IA avoids “stupid questions” by purging all “Y” questions which relate only to diseases eliminated by the process.

IA logically manages “uncertainty”. When a disease exhibits a symptom only occasionally, (a “U” condition), it is retained within the database regardless of whether the answer to the symptom question is “Yes” or “No”. The disease is not eliminated. It remains available for “further consideration”. IA continues the elimination process. Each answer eliminates “Y” or “N” diseases as per the entered relationships, taking IA ever closer to the answer. IA achieves the subtle objective of making a decision on an uncertain piece of information. While the disease with the uncertain condition is “retained”, every answer continues the elimination process. On the other hand, an uncertain condition is “garbage” for a traditional expert system, which cannot “match” a disease which has a “maybe” relationship to a symptom. Since IA does not seek an exact match, it logically handles “uncertainty”. For correctly entered relationships, the IA logic is flawless in diagnosis. Traditional expert systems are slowed down through the exponential growth of their back and forth search steps. They ask a tediously long series of questions, including stupid ones. They fail to handle uncertainty. IA is generations ahead of current expert systems. Doctors certify that IA is fast and never asks stupid questions.

Inductive logic. But, IA follows the logic that a person does not have a particular disease if he does not have a particular symptom. This is not a conventional logical derivation. In any diagnostic process, we can use deductive, or inductive reasoning. In deductive reasoning, a generally accepted principle is used to draw a specific conclusion. All men are mortal. Socrates is a man. Therefore Socrates is mortal. When a person uses a number of established facts to draw a general conclusion, he uses inductive reasoning. For instance, the observation of swans over the centuries has led to the conclusion that all swans are white. This is the kind of logic which is normally used in the sciences. An inductive argument, however, is never final. It is always open to the possibility of being falsified. The discovery of one black swan would falsify “the white swan theory”. Inductive reasoning is always subject to revision if new facts are discovered. The sciences progress through this process of induction and falsification.

Exclusion is also a logical process. Inductive reasoning has traditionally been based on the principle of inclusion. The white swan theory is a result of experience over time. If we saw a white bird, we would move one step forward in identifying it as a swan. But logic is equally sound in exclusion. If the bird was black, we could conclude that it is not a swan. Subsequent discovery of a black swan would make this induction wrong. But, if the reasoning that all swans are white was true, then the induction that a black bird is not a swan would be equally true. The white swan theory can logically lead to both conclusions. In a similar manner, if a symptom is always present for a particular disease, inductive logic also implies that an absence of the symptom excludes that disease from further consideration. This is not a conventional conclusion, but is accurate and unassailable.

IA avoids an exact match and uses elimination. A conventional search algorithm seeks an exact match between indicated symptoms and the symptoms in memory for a known disease. The objective of IA is not to find an exact match, but to eliminate those diseases which fail to meet the search criteria. Both “Yes” and “No” answers are specifically encoded to eliminate unrelated diseases. Consider a patient with a disease, who approaches a computer diagnostic session. Let us say the computer has a list of 200 diseases, which can be identified by 1000 symptom specific questions stored in the system. (Many diseases will share common symptoms). In practice, on an average, each disease may answer “Yes” to 20 of the 1000 questions.

More clues in elimination. But, upto 200 “Yes” answers may justify the elimination of the disease, since most symptoms will promptly point to specific groups of diseases, excluding others. The conventional expert system looks only for “Yes” answers. It will match the answers for the disease of the patient to just 20 of the 1000 questions. For this patient, 980 answers will not take the search forwards. But for IA, every “Yes” answer can eliminate up to 20 percent of the diseases. Elimination of a disease also removes its related questions. The elimination process will yield speedy results even for “No” answers. IA will identify the disease long before the 20 relevant questions for the disease are exhausted by swiftly purging any remaining alternatives. In pattern recognition, an elimination procedure is unbelievably faster than one which seeks an exact match.

Instant Recognition

A logic for instant recognition. The speed of the elimination process is even more striking for IA in a special situation. When IA identifies a special condition, its recognition process is virtually instantaneous. Its memory stores the relationships of all diseases to symptoms. Suppose only one disease has a “Y” relationship and all others, an “N” relationship to an exceptional symptom. The symptom is unique to the disease. Then, an “Yes” answer to this symptom eliminates all “N” diseases, leading immediately to recognition. The symptom indicates the disease. It is recognised in a single step of massive elimination. The process is logical. It evaluates every disease in its database against a single clue from one symptom. A doctor may walk into a surgery and instantly attend to a patient suffering from a heart attack. He may not even ask a question. With minimum visual clues, he instantly identifies a single disease from his “known database” of thousands of diseases. He instantly recognises a single pattern in a maze of interweaving patterns. IA may be imitating the logic of this recognition process.

Unique features can identify a pattern. The IA logic does not seek an exact match, but concentrates on the elimination of alternate possibilities. Elimination is most effective when there are unique features. It is a practical strategy for recognition in nature. All the recognised objects in our environment are unique. Despite millions of shared characteristics, they also have individual qualities. Even where patterns shift constantly, some characteristics remain stable. Consider a face in a newspaper cartoon. It contains the barest minimum of information – a few lines which define the edges of facial features. But a public figure is identified by just the curve of a nose. The context of being in the newspaper eliminates all ordinary people. The turn of the nose eliminates all politicians with straight noses. Unique features and elimination can determine the outcome. Massive amounts of data are not evaluated. A few clues. Recognition is virtually instant. Elimination based on uniqueness can achieve logical and acceptable recognition.

IA imitates parallel processing. With the discovery of the spreadsheet, it became possible for computers with single processors to imitate one characteristic of parallel processing. Even if a spread sheet has thousands of cells, a single entry in one cell is instantly reflected in all the related cells. Thousands of serial calculations appear to the user as a single parallel calculation. Logically, the spreadsheet can have billions of cells and a sufficiently powerful processor can still deliver this result. The spreadsheet is holistic, since every cell reflects the current re-calculated position. IA is similar. By evaluating the results of a single answer on all the diseases in its database, it is holistic and imitates parallel processing. Logically, IA too can produce instant recognition in any size of search space. Any unique symptom can enable IA to instantly identify one among several thousand diseases. If IA is to attempt a problem on the scale of the human nervous system, the only limitation will be the practical problem of data entry.

IA compared to intuition. Consider the steps followed by IA. It stores details of all diseases, their characteristics and the relationships between them in memory. It receives inputs concerning symptoms through “Yes/No” answers. It simulates parallel processing to globally evaluate the current input. It is encoded negatively to use all inputs to eliminate unrelated diseases. If an input indicates any unique symptom, it achieves instant recognition by eliminating all except the related disease. It follows an algorithm which results in instant identification. Compare IA to the recognition process of the mind. When a face is familiar, there is instant recognition. Let us call it intuition. Such recognition, of thousands of such objects, is repeated by people world-wide millions of times every day. Like most other events in nature, such a process must follow an orderly set of instructions to achieve results in a finite number of steps. In essence, intuition must also follow an algorithm.

Memory and relationships. A comparison of IA with the current knowledge of the mind, reveals some similarities and several unexplained enigmas. This essay attempts to fill in the gaps to create a composite view of the mind. Firstly IA stores the names of all diseases in memory. It is logical to assume that the mind stores data on all known faces in memory. But the mechanics of memory remains unknown. This essay suggests a sound possibility. Secondly, IA infers that certain symptoms are present, or absent, based on simple “Yes/No” answers to queries. There is considerable evidence that the mind isolates thousands of characteristics of any seen object. Obviously, the mind must perceive the characteristics of faces to be present, or absent. Thirdly, IA stores the relationships between symptoms and diseases. In recognising a face, the mind establishes its identity. Identification demands a link between a face and its known characteristics. One must know that the face is oval, or round. It is reasonable to presume that the mind must have such links. But how the mind stores such links remains a mystery. This essay suggests how nerve cells can establish and store such relationships.

Nerve cells eliminate alternative possibilities. Fourthly, IA encodes a negative relationship between diseases and their symptoms. It is deliberately coded to eliminate. Deliberate elimination of alternatives is a well documented feature of the nervous system. (3) Nerve cells have a powerful system of parallel inhibition of surrounding neurons when a particular group of neurons start to send information. This inhibition is strongest for those immediately adjacent to the excited neurons. Throughout the nervous system there are neural circuits which switch off other circuits when their own areas are energised. There is evidence that the mind carries such systematic elimination beyond logic. This is illustrated in the popular vision experiment, where a drawing can be interpreted as a vase, or two faces facing each other. The mind eliminates one interpretation to recognise the other – a vase, or two faces. Evidently each recognition path acts powerfully to inhibit the other. Recognition is firmed up by eliminating even logical alternative solutions.

The coding of elimination by nerve cells. The mind is known to have specialised networks which perform unique functions. There is a network to identify the edges of a seen object. Another to detect the beginning and end of movements by muscles. This essay gives some examples of how such intelligence can be achieved through recognition based on the memory codes of neurons. In fact, the key theme of this essay is that such recognition can give intelligence to a network. Such a tool can give neural networks the capability of achieving a variety of intelligent tasks. It is assumed that neurons may be suitably coded, to facilitate elimination of less viable alternatives. This essay does not suggest any probable process the mind may use to determine such elimination. But, elimination, as a neural process, remains a well documented and practically experienced event.

Parallel links for speed. Definitive research suggests that the brain simultaneously isolates every incoming sensory image into myriad characteristics. (4) The visual image alone is divided into several hundred million separate characteristics of light, shade, colour, outline and movement. We do not know how all this information gets organised and processed. But, each nerve cell in the system is known to have a hundred to a quarter of a million links with other cells. (5) The average nerve cell is known to respond within about 5 milliseconds of receiving a message. Since all cells work in parallel, any message received by any cell can reach any other cell in the system within just five or six steps – in just one fiftieth of a second. Currently, science does not know how such a process can rapidly transfer information in the system. Recognition may be provide the pivotal link. It can link every cell to the system. If so, every cell in the network can recognise and respond to every flash of incoming information. If we assume a recognition role for the nerve cell, global interpretation of incoming information and instant response becomes feasible for the system.

IA imitates intuition. IA has classic simplicity and power in its logic. The elimination process is logical. It is discrete and does not leave a fuzzy answer. Yet it has the ability to evaluate possibilities with vague qualities. If a face is known to occasionally wear spectacles, all faces which never wear spectacles can be eliminated. A vague characteristic is productive for IA. As opposed to this, a search and match algorithm finds the “occasional use” type of information futile. IA logic is holistic, since it evaluates its entire database, with each input. Every answer updates its perspective, by eliminating all elements that fail the search criteria. Every answer narrows its focus. It creates in IA the equivalent of “global awareness” of the mind. As against this, a search and match algorithm ambles about in the vast search space without a clue as to the global picture and appears stupid. Finally, IA instantly identifies a pattern, if it indicates even a single unique quality, through simultaneous elimination. In conclusion, IA is logical. It imitates intuition in being holistic, avoiding “stupid questions”, handling uncertainty and in providing instant recognition.

The Nerve Cell and Recognition

A nerve cell has many inputs and a single output. A cell is the basic unit of all living tissue. In the human body, there are specialised cells called neurons, which transfer information rapidly from one part of the body to another through electrical nerve impulses. Each of the one hundred billion or so nerve cells has many inputs and a single output. (6) A typical neuron has thousands of minute threadlike growths called “dendrites” which conduct impulses towards the cell body. A central “cable” called an “axon”, conducts impulses away from the cell body. The output of every cell in the entire nervous system is an “all, or nothing” impulse, called an action potential, despatched through its axon. A neuron receives many inputs and dispatches a single output.

Neuron believed to be a computational device. Current research views this output of the cell as a computational message. (7) The voltage of a neuron at any given moment, is presumed to reflect all the summation activities of a thousand inputs. As the inputs arrive, they are supposed to be rapidly added to or subtracted from the total neuron voltage. It is presumed that if the stimulus is strong enough to breach a critical threshold level, an action potential is fired. Other neural network theories assume complex calculations, giving weightages across neurons. Current scientific theory assumes that nerve cells use some form of computation, meaning mathematical, especially numeric methods.

“>Nerve cells may not compute. They may recognise. IA points to intuition as a process, which acts through elimination based on simultaneous recognition of millions of separate characteristics. It has been reasoned that, at the seminal level, recognition may be accomplished by a nerve cell. There are many supporting arguments for this thesis. “Recognise” means “to establish an identity”. Mathematical computational ability does not focus on the identity of a node. Weightages may give greater identity, but fail to give a node a singular quality, which can be recognised by millions of other nodes. Yet, there is experimental evidence that a single nerve cell may inhibit the actions of millions of other cells. If addition or subtraction is the principle, it is hard to justify the idea that the firing of a single nerve cell among thousands of others can add up to trigger an action potential in an axon. You cannot add “1” to “-1000” and get “+1”. If recognition is the key, even a single microscopically small input from a single cell can trigger recognition and inhibition of a whole battery of cells.

The nerve cell may operate a form of Boolean Logic. Each nerve cell may be functionally competent to recognise a single event. It may fire a volley of impulses when the event is recognised. The all or nothing response of the nerve cell may be a form of Boolean logic. In Boolean algebra, all objects are divided into separate classes, each with a given property. Each class may be described in terms of the presence or absence of the same property. An electrical circuit, for example, is either on or off. Boolean algebra has been applied in the design of binary computer circuits and telephone switching equipment. These devices make use of Boole’s two-valued (presence or absence of a property) system. Firing by each neuron may represent the presence, or absence of a distinct property. The entire nervous system may recognise an input from a cell as a perception of the presence of a property. Alternatively, the system may recognise firing by a cell and respond with a specific activity, such as a muscle movement.

Recognition at the input level. For sensory inputs, the firing of a nerve cell is known to indicate recognition. The entire in formation input into the human nervous system is through cells called receptors which convert sensory information into nerve impulses. (8) Chemoreceptors in the nose and tongue report on molecules which provide information on taste and smell. Other receptors are massed together to form sense organs such as the eye and the ear. There are receptors which report on pressure, touch, pulling and stretching. Nociceptors report on cutaneous pain. Peripheral nerves connect these sensory receptors to the central nervous system. At the entire input level, nerve impulses indicate recognition of the occurrence of millions of isolated events. The whole system recognises the firing by each one of these cells as the perception of a single microscopic event. At the input level, the firing of a cell indicates an act of recognition and not one of computation.

Motor events at the output level. At the output level, individual nerve impulses control motor outputs. There are motor areas in the cortex, the wrinkled surface layer of the cerebral hemispheres of the human brain. (9) Careful electrical stimulation of these areas send nerve impulses which invoke flexion or extension at a single finger joint, twitching at the corners of the mouth, elevation of the palate, protrusion of the tongue and even involuntary cries or exclamations. The nerve fibres carrying inputs to and outputs from the cortex pass through the thalamus, a major neural junction in the brain. This junction plays a key role in this explanation of the activities of the mind. The nerve impulses passing through follow a form of Boolean logic. They report the presence or absence of individual events, or activate or are quiescent to isolated motor functions. Each action potential indicates, at the input and output levels, the perception or the triggering of a property – a distinctive event.

Nerve cells cannot add apples to pears. At the input and output levels, the firing of a nerve cell indicates an event. Current theory admits the Boolean function at these levels. But scientists imagine computation by nerve cells at subsequent levels, where these messages are interpreted and transmitted further. While it has a single “all or nothing” output, a typical neuron receives thousands of inputs from other nerve cells. Numeric computation (adding, subtracting, dividing, or multiplying) of widely varying inputs is quite improbable. The inputs are distinctly different events such as sound, light, pressure, or smell. The outputs are complex muscle movements. It is wildly chaotic to include all this into an integrated computation. It is like adding apples to pears, or subtracting the sense of touch from the sense of pain. It is more realistic to assume that a pain cell recognises touch and reacts by despatching or inhibiting a pain message. Recognition can evaluate varied inputs and trigger an appropriate output. Recognition may provide the key to understanding intelligence.

Recognition the first step to intelligence. Throughout the nervous system there are networks of cells, which appear to act intelligently. These events have been assumed to be some form of network intelligence – a mysterious mental capability. But such intelligence can be explained if we assume that nerve cells recognise incoming information and respond with action potentials through their axons. A typical unexplained act of intelligence is the baffling capability of the mind to modify the sensation of pain on its route to the cortex. The sensation of pain is known to be reported, enhanced or suppressed, under varying conditions. Consider the following explanation. A neuron which reports cutaneous pain may receive inputs from its primary pain sensory neuron (P), along with other dendritic inputs from neighbouring (sympathetic) pain (SP) and touch sensory (T) cells. The cell may report pain and sympathetic pain. It may ignore the sense of touch to report pain. It may also inhibit sympathetic pain giving priority to the sense of touch. In such a context, the cell responses to the listed inputs may be as follows:

P – Fire. Reports pain.

SP – Fire. Reports sympathetic pain.

P+T – Fire. Ignores touch and reports pain.

SP+T – Inhibit. Suppresses sympathetic pain to highlight touch.

In reporting, or suppressing sympathetic pain, the cell may be selectively responding to combinations of nerve impulses received at different dendritic inputs. It may be recognising unique combinations to trigger its own interpretation of a single event.

An executive attention centre. The recognition model can also illuminate the puzzling process of paying attention. (10) William James, in one of the best writings on the mind, suggested that attention is “the taking possession by the mind, in clear and vivid form, of one out of what seem several simultaneous objects or trains of thought. Focalisation, concentration of consciousness are its essence”. The focus of attention is believed to be the key in trying to understand the concept of consciousness. Research has revealed some facts concerning attention. (11) PET scans create images of brain activity by detecting the presence of glucose in blood flow to nerve cells in the brain. When particular cells are more active, there is more glucose in the local blood flow. The scans detect increased presence of glucose to construct a three dimensional model of the brain on a computer screen showing greater activity with brighter colours. Recent research using PET scans have revealed activity in an executive attention centre (EAC) in the cortex, when people focus attention. This area of the cortex lights up when a person pays attention to a sensory input. Mystery remains as to how activity in this region can enable the system to pay attention.

Directing attention. The process of paying attention can be shown to act through selective recognition by nerve cells. Touch sensory receptors in the skin are known to fire impulses, when pressure is applied on the skin. Such messages are relayed to the cortex in several stages. Consider a relay neuron which transmits impulses from a touch sensory receptor on the shoulder to the cortex. Let us assume that, among its many inputs, this reporting neuron receives impulses from EAC through a single dendrite. The reporting neuron may normally be inhibited to prevent an overload of sensory data to the cortex. The signal from EAC may be recognised by the neuron as an instruction to re-transmit received messages. When it recognises the input from EAC, the reporting neuron may transmit received impulses from the receptor to the cortex. These impulses simultaneously further inhibit neighbouring sensory neurons, thus highlighting the message. By sending nerve impulses to the distinct neurons, EAC may create awareness of the pressure of cloth on the shoulder.

Awareness and consciousness. This reasoning points to increased awareness as a process, which causes inhibited sensory neurons to fire. Recognition of EAC impulses by reporting neurons may focus attention by creating localised awareness. Attention may become the process of increasing awareness in a local sensory region. Signals from EAC may act by causing inhibited sensory relays to fire. Such control fibres may be linked to the entire sensory system to enable EAC to focus the attention of the mind on any sensory input. A similar group of fibres may constitute a consciousness channel, which may aid the mind to be generally aware of sensory inputs. Impulses in this channel may instruct inhibited sensory neurons to begin reporting sensory events, to wake us into consciousness, with global awareness. When this channel is inhibited, there may be no sensory awareness. The channel may be inhibited when we sleep. A consciousness channel may wake us up, just as EAC focuses attention. Currently, awareness, attention, and consciousness remain mysterious processes, which stand in the way of an understanding of the mind. If we accept the possibility that individual nerve cells perform acts of recognition at the most rudimentary levels, we may explain many such intelligent activities of the mind.


Current knowledge regarding memory is limited. There is little current knowledge about how memory is stored in the brain. (12) Some researchers suggest that memory is stored in specific sites and others that memories involve network functions, with many regions working together. This essay suggests a method for the storage of human memory and a mechanism of its recall. This explanation forms an enabling requirement to support the insight that instant recognition is a key function of the mind. This follows the hypothesis that nerve cells act as primary recognition devices at the most fundamental level. Such a premise can explain how memory enables nerve cells to support intelligent networks, recognition of entities and habitual motor functions. This view of memory structure is vital for all the functions of the mind, as described in this essay. This section provides an overview of how a nerve cell may store a memory and how the nervous system may recall a memory.

Recognition requires memory. At the input and output levels, the firing by a nerve cell signifies a finite event. Receptor cells interpret these sensory inputs and send impulses. These impulses are relayed to the cortex in several stages. At an intermediate stage, a cell may receive messages from multiple locations representing multiple categories of such information. The modification of the sensation of pain, or the focusing of attention were suggested to act through the recognition of incoming messages by reporting cells. This essay suggests that a cell fires when it receives a distinct pattern which it recognises. To “recognise” is to establish an identity. The identity of any entity can be established only when it has a known relationship to certain characteristics. Knowledge requires consistency. If a cell knows a relationship, it must fire every time the relationship is recognised. So, the cell must store a memory of this relationship, if it is to recognise it. If a cell has the power of recognition it, must have a memory. It is suggested that such memory may be an ability to selectively recognise different combinations of incoming nerve impulses.

The structure of memory. A nerve cell, with say, 26 dendritic inputs coded from A to Z may have a memory for combinations of simultaneous inputs, such as CDE, DXZ, etc. The neuron can be said to store a memory for each combination, if it fires (or is inhibited) on receiving simultaneous impulses at C, D and E, or at D, X and Z. Each combination becomes a relationship which the cell remembers. Each cell has a functional specialisation. When it fires, it reports, or triggers a finite and unique event. The combination represents the relationship of this event to other events (CDE, or DXZ) it perceives. As suggested earlier, the pain reporting neuron fires for pain (P), sympathetic pain (SP) or pain and touch (P+T). It is inhibited by (SP+T). Each remembered combination becomes a unit of memory, which triggers a dependable response from the cell.

A massive memory. Perception of each unit of memory may cause the cell to fire, or to be inhibited. 26 characters can be arranged in millions of unique combinations. For a nerve cell with just 26 inputs, there can be millions of such units of memory. The cell may selectively respond to millions of combinations. Recognition on this basis may give massive selective intelligence to the nerve cell. Contemporary research has so far failed to locate a physical location for human memory. The possibility suggested here can point to incredible memory capabilities in individual nerve cells. If an individual cell can have such a large memory, imagine the total memory capacity of 100 billion cells! The concept may also highlight the problem of memory recall. There may be as many units of memory as the number of grains of sand on a beach. The task may truly be the equivalent of locating a needle on a beach.

A memory at a synapse. High frequency stimulation of of the dendrites of a neuron have been known to improve the sensitivity of the synaptic junctions. This phenomenon (13) is called long-term potentiation (LTP). Since such activity is seen to be “remembered” by the cell through greater sensitivity at specific inputs, LTP is considered to be a hopeful direction for research in locating human memory. This essay suggests that memory derives from a pattern recognition function. It may follow from the cyclic recognition of the unique features of the multitudes of dendritic inputs of a neuron. A neuron may become more sensitive to an individual input through LTP. Neurochemicals at the synaptic junctions have also been known to increase such sensitivity. But, memory may derive from the gobal pattern recognised by the nerve cell rather than from a greater sensitivity to a specific dendritic input.

Cell memory feasible. Each microscopic living cell contains the DNA molecule which carries within it the entire blueprint for a human being. Recognition codes in cells interact in the handling of the millions of chemical interactions in the body. The immune system is also known to use powerful code recognition systems. Under the circumstances, it is feasible that the protein neuroreceptors which mediate neuronal interactions (or the innumerable chemical synaptic intermediaries) contain sufficiently powerful memories and code recognition systems for the sustenance of a practically limitless memory in each nerve cell. If such a massive memory exists within each one of billions of nerve cells, there is the possibility of an astronomically large human memory – trillions of trillions of megabytes in computer terms. Acceptance of the presence of such an immense memory may take us a step further in understanding the awesome power of the mind. It may also create a massive barrier to AI in its efforts to imitate human intelligence.

The memory of nerve cells may be for patterns. Recognition requires a memory for the cell. Instead of just 26 inputs, many nerve cells have thousands, or even hundreds of thousands of incoming dendrites. 26 inputs can be represented as characters on a page and each unit of memory as a group of characters, such as ABC or CDE. But, with hundreds of thousands of inputs, the closer equivalent is a pattern of dots on a screen – a picture. With Boolean logic, the pattern would consist of dots, which are either on, or off, with a defined frequency. The memory of a nerve cell would be its ability to store in memory and so recognise multiple patterns of dots – the pattern of incoming dendritic impulses on a cyclic basis. This cyclic pattern of dots is the equivalent of a black and white picture. Recognition of a picture triggers an impulse from the cell, indicating that the current incoming information has relevance to this particular cell. Each nerve cell may have a memory for millions of such pictures, recognising individual pictures to respond with impulses, or with inhibition.

Memory must be recalled in context. Wherever memory may be stored, it concerns a whole lifetime of activity and is available for instant recall. A threatened animal carries a potent memory bank of past perilous experiences. It has memories of initial sensory indications of danger, of muscular responses for battle and of escape routes from the battle zone. With contextual memory recalled within fractions of a second, the whole power of experience is brought to focus on the ongoing task of survival. A contextual filing system for memories is a vital requirement of life. Contextual use of memory existed from the beginning of evolution. (14) In the early aeons, “Nosebrains” recalled memories for smells to decide if an object was edible and to be consumed, or inedible and to be avoided. Smells became the file pockets which triggered physical activity. Simple odour based filing systems in vertebrates evolved to more sophisticated feeling based systems in mammals. Feelings provided context for many subtle shades of activities, including leisure, play, upbringing of the young, and mild hostility, or deadly combat. This essay suggests that feelings may provide the key to the recall of memory.

Feelings and emotions are real. But, for centuries, feelings were discarded by scientists as not being part of the rational modern mind, a throwback from primitive times. It was Charles Darwin who first suggested that emotions have a real world existence, visibly expressed in the behaviour of humans and lower animals. The existence of an emotion could be derived from an angry face, or even a bad feeling in the stomach. Later theory suggested that each emotional experience is generated by a unique set of bodily and visceral responses. Visceral responses switch the nervous system between the sympathetic system which supports energetic activities and the parasympathetic system, which supports relaxation. (15) Subsequently, this view was disputed by W.B. Canon. He countered that emotions do not follow artificial stimulation of visceral responses. Emotional behaviour was still present when the viscera was surgically or accidentally isolated from the central nervous system.

Nerve impulses can represent feelings. This view that emotions have an independent existence is supported by current research. Euphoric states of mind are created by drugs. (16) Electrical excitation of certain parts of the temporal lobe of the brain produces intense fear in patients. Excitation of other parts cause feelings of isolation, loneliness or sometimes of disgust. (17) The feeling of pleasure has been shown to be located in the septal areas of the brain for rats. The animals were observed when they were able to self stimulate themselves, by pressing a lever, through electrodes implanted in the septal area. They continued pressing the lever till they were exhausted, preferring the effect of stimulation to normally pleasurable activities such as consuming food. All experimental evidence over the years suggests that nerve impulses can trigger feelings. This fits in with the reasoning that nerve impulses represent finite events. In such a case, a group of fibres which carry feeling impulses can be viewed as a picture in a channel, representing the real time feelings in the system.

The limbic system – a feeling centre. (18) In 1937 Papez postulated that the functions of central emotion may be elaborated and emotional expression supported by a region of the brain called the limbic system. This system is a ring of interconnected neurons containing over a million fibres. These fibres also pass through the thalamus, the main nerve junction to the cortex mentioned earlier. The limbic system is a feedback ring with impulses travelling in both directions. (19) This essay suggests that the pattern of impulses in this million fibre channel of the nervous system may represents our global feelings – a feeling channel. For a system which is constantly interpreting nerve impulses, the cell of origin of the impulse indicates whether the impulse represents a point of light, a pitch of sound, an element of pain or a twinge of disgust. Feelings are triggered as nerve impulses which represent measurements of the parameters of the system. They are ever present. The pattern in this channel reflects the current feeling and may provide the context for the recall of memories by the mind. Feelings may be expressed as a picture with a million dots. This essay suggests that each subtle variation of the picture could recall a specific memory.

A sensory map on the cortex. It was reasoned that nerve cells store memories in the context of their relationships. Such data must be stored somewhere to be recalled. It is widely known that the brain physically isolates each pixel of sensory information. (20) When light enters the eye, it passes through the lens and focuses its image onto the retina. The light is received by special cells in the retina called rods and cones. Light-sensitive chemicals in the rods and cones react to specific wavelengths of light and trigger nerve impulses. About 125 million rods perceive only light and dark tones in an image. 6 million cones receive colour sensations. The light from a single rod is perceived as a microscopic spot of light when impulses reach the visual cortex. (21) Similarly, the tones heard by the ear reach a region of the cortex called Heschl gyrus. There is a spatial representation with respect to the pitch of sounds in this region. Like a piano keyboard, tones of different pitch or frequency produce signals at measurably different locations of the cortex. Each pixel of sensory information terminates in a specialised complex on the cortex. The entire sensory inputs to the mind impinges as a picture in a region of the cortex. Consider the possibility that the memory of each sensory image is stored exactly where it is received. There is experimental evidence of this possibility.

A Barrel to store memory. Each of the millions of sensory signals is finally known to reach a specialised barrel of cells in the cortex. (22) In 1959 Powel and Mountcastle identified this complex as the elementary functional unit in the cortex. Each unit is unique. It is a vertical column of thousands of nerve cells within a diameter of 200 to 500 microns, extending through all layers of the cortex. Let us call this unit a Barrel. Research has demonstrated the functional specialisation of each Barrel. Each Barrel represents a single pixel of sensory information. The neurons of one Barrel are related to the same receptor field and are activated by the same peripheral stimulus. All the cells of the Barrel discharge at more or less the same latency following a brief peripheral stimulus. The activation of one Barrel indicates the arrival of one finite element of information to the cortex. A single rod reports the incidence of light on a microscopic spot on the retina. The impulses from this cell are carried through the optic nerve to a single Barrel in the visual centre in the cortex. The firing of a Barrel in the primary visual cortex signifies the perception of a point source of light by the mind. This essay reasons that memories may be stored in the same Barrels.

Barrel – logical location for memory. The firing of one Barrel represents a single pixel of the global sensory information. The location of the Barrel defines it as a point of light, a pitch of sound or a pressure point on the skin. The firing of a pattern of Barrels is interpreted by the mind as a sensory image. The Barrels will fire when the image is received. If the same Barrels fire again, a memory of the same image will be recalled. It was reasoned that a memory may be recalled in its context. Feelings may provide that context. Feelings are the logical filing references for the recall of memory. Feelings form a picture in the feeling channel. It was reasoned that nerve cells store memories of relationships. These relationships were stored as pictures. It is now suggested that such a memory may be recorded into a Barrel. The current feeling may be recorded into the memory of all Barrels which receive the current sensory perception. Each Barrel recalls the relationship of this feeling and fires. When this feeling is recalled again, the same Barrels fire and the sensory memory is recalled. For this reasoning to be plausible, feelings must have access to each barrel.

A “non-specific” access. If feelings trigger the firing of Barrels and the resultant recall of memory, then the feeling channel must have access to each Barrel. Current research supports the view that there could be such an access. (23) The nerve cells in the Barrels of the cortical layer are known to have both radial and parallel fibres. Radiating downwards from the cortex are millions of fibres which directly link Barrels through the thalamus to all sensory and motor functions. This link is called the “specific link”. The cortex also has a surface layer which runs a thick network of fibres parallel to the surface. These fibres are also known to be linked to the thalamus. This link is called “non-specific thalamo-cortical link”. The link was recognised when it was discovered that stimulation of the “non-specific nuclei” of the thalamus led to wide-spread “recruiting activity” in the outer layers of the cortex. This essay suggests that this “recruiting activity” could be the process of recalling memory.

Feelings have access to Barrels. The feeling channel in the limbic system passes through the thalamus. The impulses in this channel may be broadcast through the “non specific thalamo-cortical link” to the cortical Barrels. The complex of cells in each Barrel may receive dendritic inputs from the million fibre feeling channel through the surface layer of the cortex. Each Barrel may instantly recognise feeling patterns. Recognition of a feeling may cause a pattern of Barrels to fire. The firing inhibits Barrels with weaker recall. Firm firing by a contour of Barrels recalls the original sensory image. There is evidence that strong feelings result in more powerful memory traces. When strong feelings are experienced during a sensory event, each Barrel stores a more intense feeling pattern. As a result, more Barrels recall the image and a more vivid memory of the event is recalled.

Memory of a flower. As explained earlier, when light from a flower enters the eye, it passes through the lens and focuses its image onto the retina. The light is broken into millions of pixels. The impulses representing each pixel are carried through the optic nerve to a single Barrel in the visual centre in the cortex. Each Barrel is a complex of cells with a vast number of inputs. It is suggested that each such Barrel also receives a feeling image – the feelings experienced when viewing the flower. Each Barrel, which receives a pixel of the image of the flower, records the current feeling picture. Later, if the feeling was strong and is recalled, the Barrel fires. Firing by the relevant Barrels inhibits weaker recognition paths. When all Barrels which recognise this feeling fire, the image of the flower is recalled. (24) This hypothesis concerning the location of sensory memory is also supported by a recent discovery. In 1988, Kosslyn reported that the recall of a visual image involves activity in the same areas where visual perceptions are received. Effectively, the same Barrels fired when an object was perceived and when its memory was recalled.

A gargantuan memory. Consider the impact of this view of memory storage and recall. The mass of nerve cells in each Barrel in the sensory region may store memories of all the feelings one has ever experienced whenever it fired. The recall of any relevant feeling causes the Barrel to fire. If recognition is weak, it is inhibited by the stronger recognition of neighbouring Barrels. Firm firing by a pattern of Barrels recalls a clear sensory image. This implies that any perception is stored as millions of microscopic pixels of the global sensory image, in the context of a relevant feeling. Such a memory would be widely distributed through all sensory Barrels, This could explain why scientists could not remove memory by ablating portions of the brain in their experiments on rats. The findings of Kosslyin that the recall of vision involved activity in the same cortical region as visual perception also supports this view of memory. Such a system could store a lifetime of sensory memories and instantly assemble a single contextual image. This process could explain your ability to recall an image from everything you have ever read, seen, or heard, in the blink of an eye. But, the tendency of the system to inhibit weaker recognition paths may also prevent the recall of weaker memory traces and appear as the fading of memory.

The cell memory can be inherited, or instantly acquired. It is reasoned that the memory required for pattern recognition by nerve cells, as envisaged in this essay, can be both inherited and acquired. Inherited processes may be seen in the visual processing regions of the cortex. The varying attributes of a visual image are analysed in different regions of the visual cortex. One of these locations analyses the orientation of the outlines of a visual image. The   cells   are   arranged   into   distinct   modules,   with orientation selective cells which fire only when an edge or bar in their fields is held at a particular orientation. While all the cells in one column of cells respond to one orientation, and an adjacent column responds to an orientation a few degrees off from the first and so on, till all possibilities are covered. If a column of cells is to select a single orientation, it must receive inputs concerning all orientations and then select one. Selection implies choice. From multiple received pictures, a single row of cells select a single picture. This is a consistent response. Evidently, they remember the picture. Such responses by cells has to be inherited. The recognised pattern may be its inherited memory. Evidence of such automatic responses by many neural systems provides proof of a cell memory for patterns.

Sensory memories are, of course, acquired. As against a wide range of inherited responses by nerve cells, new sensory memories are continually recorded. Every day, events that provoke feelings record thousands of images into memory. When a Barrel fires to recall a new memory, the pattern of feeling impulses which triggered recall has already been recorded afresh into the memory of the complex of cells. Since the cells can be sensitive to inputs from even a single dendrite, the process of recording a memory can be a simple process of recording the current incoming picture on receiving such an instruction from any source. This essay assumes that cells can have both inherited and acquired memories.

Recognition of Objects

Channels carrying pictures. We have assumed that nerve cells recognise received pictures. A feeling channel carrying a picture through a million fibres has also been suggested. There is a vital difference between a picture and a parcel of messages, when transmitted through a bunch of fibres. Take the 32 bit parallel connection in a computer cable connecting two parallel ports. A computer can recognise only two states, on or off, in each of its millions of circuit switches. But when 32 switches are linked together, it can recognise, in a single cycle, over four billion pieces of data. But, such connections must maintain integrity in neighbourhood relationships at the sending and receiving ends. Only cyclic information received simultaneously through all the inter-related switches can be interpreted. Compare this to a glass fibre channel transmitting information. If each fibre carried an individual message, the relative location of the fibres would not matter. But suppose each fibre in the channel carries a single pixel of a picture. Then, if the relative positions of the fibres change between the sending and receiving ends, the picture will be lost. It is in such a context, that the computer cable transmits a primitive 32 dot picture. The relative position of the dots must be maintained at the sending and the receiving ends. The feeling channel may similarly transmit a million dot picture.

Neighbourhood relationships critical for a picture. If a channel is to accurately transmit a picture, the fibres must be projected and “mapped” at the receiving end. Such projection and mapping is done in the nervous system. (25) Throughout their growth, axons extend and map on to specific target regions. Each area of the somato-sensory cortex is proportionally linked to the number of nerve endings in the corresponding part of the body. Similar parallel projections exist in many other regions. Proximity relationships are critical for these connections. They maintain integrity in the relative location of the fibres in a transmission. The principle of projection suggests that relative location has meaning for the nervous system. The transmission is essentially a matrix of precisely located dots. Millions of such dots, in a magazine, are recognised by us as a picture. If the relative locations of dots change, the picture is seen to change. Just as it decodes information from the characters on this page, the mind instantly decodes the information in such a matrix of dots. In this essay, a picture is defined as a cyclic transmitted pattern of dots, in a fixed matrix, which is recognised at the receiving end.

Pictures may be the language of the mind. The nerve fibres reporting pain to the cortex is a pain reporting channel. Each fibre recognises incoming patterns to be inhibited or to report both pain and sympathetic pain. The mass and location of dots in the picture in the channel reports the precise location and severity of perceived pain. This essay suggests that recognition of such pictures is the basic capability of the nerve cell and of the nervous system. More reasons are given to support the view that information may move in the nervous system as such pictures in dedicated channels. The capability of recognising pictures may apply not just to visual images, but to all messages transmitted in the system. The meaning of such messages may be defined as the information carried by them. Un-related, a single pixel of a picture has little meaning. Meaning is derived from its contextual whole. An arrangement of dots can represent a character of text. Higher and higher orders of meaning can be conveyed by a single character, a word, a sentence, and a paragraph of text. A picture is said to carry more meaning than a thousand words. It is at the apex of the hierarchy in the conveyance of meaning. A multi-million dot picture channel can carry an infinite range of information. Neural channels may convey the most powerful meaning at this level. The remainder of this essay assumes that the nervous system transmits such meaning.

From primary to secondary, then to association areas. When we assume that channels in the system carry meaningful pictures, the flow of information reveals awesome order and purpose. The regions and pathways of the human nervous system have been extensively mapped. Each receiving region performs a function and transmits the pictures further. (26) The areas of the cortex which receive sensory information are called the primary areas. They were seen to perceive and recall sensory images. The sensory pictures proceed from primary to secondary areas, which co-ordinate those from similar sensory receptors in the other half of the body. Neuron channels from the primary areas send pictures only to the secondary areas. All secondary areas in both hemispheres of the brain are inter-connected. The secondary areas are known to deal with more complex functions such as binocular vision and stereophonic sound. The pictures proceed from secondary areas to the so called “association areas” of the cortex. These areas receive the consolidated pictures from all secondary regions. The association areas appear to be the principal pattern recognition engines of the mind. They perceive and recognise

Many categories of recognition. Each association area recognises an entity in the context of its received sensory information. (27) The primary somesthetic area of the cortex receives pictures of the sense of touch. If this area is intact and there is damage to the somesthetic association area, a patient can feel a common object, such as a pair of scissors held in the hand, while his eyes are closed, but is unable to identify it. The picture in the somesthetic areas enables the sense of touch and that in the somesthetic association area enables recognition of the touched object. Failure of each association area causes failure of a particular recognition ability. The visual association area impacts on visual recognition. Tactile categorisation affects the recognition of an object by its feel. When the speech association area is damaged, a person knows the object, but is unable to name it. The association areas appear to perform individual acts of recognition.

A picture to represent the recognition of an object. The premise is that pictures transmit information in the system. Pictures imply a distinctive pattern of dots in a fixed matrix. A visual image may be recalled through the firing of the same Barrels in the cortex which received the original image. Groups of Barrels are also known to transfer information between different regions of the cortex. The nerve fibres from the Barrels in the somesthetic association area also can be reasoned to be sending pictures. Damage to this area implies loss of ability of the nerve cells in this region to send these pictures. Subsequent failure to recognise a pair of scissors suggests that this picture represents a pair of scissors to the mind. Such a recognition is a stable repeatable event. For recognition to be stable, this picture must be consistent for this object. If pictures transmit information, the same picture must fire every time scissors are recognised. Each object that is recognised would require equally consistent pictures. This would require a process which imprints such pictures in this channel.

A recognition image. This essay suggests two routes for intelligent activity through instantaneous recognition of patterns by billions of individual nerve cells. One is to recall an image in the exact geographic format in which it is recorded as in the recall of a visual memory. The second is to recall a reference image, imprinted at the point of recognition. A recognition picture fired by the association channel could be any random arrangement of dots. It would be more logical to think that this arrangement is obtained from the system. Since feelings are always present, feeling patterns could provide random reference points. If the geographic map of the association channel duplicates that of the feeling channel, and has a parallel link to it, the channel can fire the same pattern as the feeling experienced when the recognition of an object is first imprinted. Subsequent recognition would fire the same picture and the system would recognise the same object. Stability of recognition may be provided by nerve cells in Barrels which act to trigger inhibition of weaker recognition paths. In conclusion, each time the recognition of an object is imprinted, a picture is imprinted in the association channel. Later when the channel perceives the object, it fires this picture in recognition.

The elimination algorithm to recognise an object. The mind instantly recognises one object from thousands of known objects through a sense of touch. But the number of identifiable objects is finite. Each identification triggers a picture by the Barrels of the somesthetic association channel. The Barrels receive integrated sensory pictures from both halves of the body. They receive the global touch sensory information concerning an object. Assume that a random group (X) of Barrels store the sensory picture at the point of recognition. X, a random reference, is provided by the system. Later, during recognition, all Barrels perceive the object. The X Barrels which recognise some unique element of the object trigger inhibition of Barrels which fail in such recognition. The X picture fires. Firm recognition would imply a consistent firing of X. Active inhibition of all unrelated Barrels would eliminate other categories, leaving X, indicating recognition of a single object. Sometimes the name of a recognised object may remain at the boundary of consciousness, to be lost suddenly. Such “tip of the tongue” feelings may be derived from solutions that are eliminated at the last moment.

“>Elimination from a fixed list. No one will dispute the thesis that the human memory has gargantuan proportions. A search and match algorithm would just go further and further back into memory to identify an object. Such a search would be endless. But the elimination algorithm presumes a fixed list of known objects – a limited memory. From   the   first   recognition   of   its mother by an infant, the mind continually expands its list of differentiated entities. But at any point in time, the list must needs be fixed. It is logical to conclude that the list is finite. Secondly, if the list of known objects was an amorphous mass, the mind would attach little importance to a new addition to the list. But the first recognition of a new category is more vividly remembered than the second or the third. The Xerox machine and the Polaroid camera are typical new objects, which are better remembered. The principle of “positioning” in advertising depends on finding new categories against which products can be “hooked”. The marketing world attaches importance to creating a “new niche” in the customer’s mind, because the context of imprinting a new category is better remembered. The importance attached to the creation of new categories implies a finite list of known categories. Elimination from this list brings the recognised category into focus.

Analytical logic vs. IA pattern recognition. Initial AI efforts assumed that computation was the key to intelligence. That the mind was just a sophisticated calculator. Later, it was acknowledged that any intelligent action requires a knowledge based response. The mind uses a store of knowledge to respond differently to varying circumstances. AI scientists attempt to assemble the knowledge and the related responses using the tools of analytical and inductive logic. Data is chunked into categories which follow particular rules. Known relationships need to be generalised to fit rules for such categories. This need to generalise limits analytical thinking. It cannot cope with infinitely differentiated steps. It misses galaxies of fine detail. So, it fails to identify between charm and dignity, or anger and enmity. It ends up as a subset of the human thought processes. IA suggests a pattern recognition process which also follows the principles of inductive logic. It also uses a store of knowledge to draw conclusions from past experience. While analytical logic requires a series of steps from premise to conclusion, IA pattern recognition leaps logically from perception to conclusion based on unique category links in memory.

Fine logical differentiation. The IA logic implies a multi-million dot recognition picture, which can represent trillions of categories. Each dot in such a picture (a Barrel) results from the evaluation of millions of stored multi-million dot pictures. Such pattern recognition pinpoints millions of categories with precision, by identifying the unique quality of each category. It views huge masses of data, using an astronomically large memory. In such a process, it instantly identifies marble from jade, or tea from coffee. With the capability for massive discrimination, it conquers the subtleties of language, poetry, art, and music. Such pattern recognition handles both analysis and the highest levels of subtlety. While analytical logic fails to evaluate a great painting, pattern recognition identifies it as a work of art. It also instantly recognises the stupid question in an analytical AI process. The only tool available to AI for modelling the knowledge relationships of the mind was a logically analytical one. Unfortunately it was less sensitive, pitifully slow and stupid. This became a barrier to an understanding of the mind. The IA pattern recognition model is logical and capable of fine and massive differentiation. Above all, it functions in real time. It can sift mountains of data in milliseconds. IA can better help to explain the vast capabilities of the human mind.

Motor Control

Mind’s control of the body. A complex mind must interact with the physical world. Nerve impulses must transform thoughts into an actions. The intelligence involved in such a process has appeared mysterious, with almost spiritual overtones. It was as if the body followed the instructions of a phantom spirit, which resided somewhere in the brain. As against such obscurity, this essay has argued that apparently mysterious activities of the intellect can be explained, if we acknowledge the act of recognition. That recognition of pictures by the nervous system is the key element of intelligence. This section suggests how the mind may control actions of the body through such a process. A network of fibres, which convert sensory perceptions into thoughts, may consciously manage fluent physical skills through the phenomenon of recognition by nerve cells.

The motor control process. This analysis of how the mind may control the body begins with an outline of the subordinate motor control system, validating intelligence at this level. It goes on to propose how a pattern recognition system can communicate decisions and objectives at higher levels. An explanation is offered for the logic of consciousness, which is the pre-requisite for purposive activity. It tries to show how feelings can control conscious motor activity. It adds the description of a very special organ which mediates to store and retrieve skilled activities. Such managed recall of skilled physical activity is shown to virtually create the modern human being, with the finely honed skills of a gymnast, or a concert pianist. The explanation hints at the awesome power and finesse of a control system which is empowered by sensitive pattern recognition. It ends with more thoughts on a neural channel which may contextually choose intelligent physical goals for the body.

Habitual and purposive responses to feelings. The recall of a memory and the recognition of an object are instantaneous acts. As opposed to this, motor activities persist over time. Since motor control impulses may fire upto 10,000 times a second, sequences of millions of impulses command the act of writing a letter, or of playing a game. Current knowledge is that these controls have both purposive and habitual components, which interact seamlessly. Experimental evidence has shown that purposive controls come from the cortical areas. Habitual controls are known to come from an organ known as the cerebellum. These two control systems co-operate to achieve the objectives of the mind. It is reasonable to assume that the process achieves the satisfaction of felt needs through physical activity. The million fibre feeling channel was suggested to have access to information on the needs of the system. This chapter suggests how pictures in this channel can control motor activity.

Control and intelligent response. At the highest level, impulses that control muscle movements originate in the Barrels of the motor area of the cortex. Electrical stimulation of the cortical motor areas trigger, through 60,000 or so motor neurons, specific acts of muscle contraction. These are supported by controls from the cerebellum. This organ is known to co-ordinate motor activity. When these signals are despatched to subordinate levels, a single motor neuron processes further lower level information from upto 20,000 dendritic inputs (28) from other neurons. These are supportive controls. In an air liner, a pilot expresses a purpose by moving a cockpit control lever. This act switches in a series of hydraulic and electrical motors which finally achieve the intention of the pilot. There is purposive movement at higher levels and intelligent support at lower levels. Similarly, in the human system, signals from the cortex and the cerebellum provide high level controls. These are converted into smooth activity by the co-ordination of numerous muscle groups. The 20,000 inputs add intelligent support to cortical purpose.

An inherited cell memory for low level intelligence. Current research does not assign a recognition role to neuronal inputs. So 20,000 inputs become a mysterious network which achieves co-ordination. Any contracted muscle remains contracted unless pulled back by an opposing muscle. Smooth activity requires co-ordination between muscles. Such co-ordination is aided by sophisticated receptors which report back with nerve impulses on pressure, stretching of skin and the initiation and cessation of movements. Let us assume that neuronal interactions achieve intelligence through recognition. That each input combination triggers a remembered “fire or inhibit” response. If so, the significance of 20,000 inputs become obvious. Recognition of an impulse indicating the contraction of one muscle requires automatic inhibition of impulses which contract an opposing muscle. Imagine the interactions in the ordinary act of sitting down. It involves numerous muscle groups. Each muscle co-ordinates this simple act with the activities of other muscles and movement related parameters. Changes in the responses of opposing muscles must be immediately reckoned. There may be an inherited mechanical logic in such responses. Each microscopic muscle movement may inform other neurons with nerve impulses. The impulses reaching each input may be unique in its information content. There can be millions of combinations of such inputs. With the intuitive IA process, every decision may inhibit all irrelevant activities to achieve a single choice. Each motor neuron may recall its memory codes to resolve 20,000 independent inputs for a single decision on movement for the next instant. Imagine this to be the inherited intelligence at the lowest level of the system.

A decision must persist. The final co-ordinated output of impulses in the motor channel trigger muscle movements through the 60,000 motor neurons. Pictures are reasoned to be the language of intelligence. The final motor activity is the result of a cyclic output picture in the motor channel. Obviously, the highest cortical levels originate this activity through similar pictures. A cortical purpose picture is intelligently transformed into a motor output picture. It is now suggested that decisions of the system can also be conveyed as pictures. The complexity of such decisions can be imagined. The performance of a concert pianist is a product of such decisions. But, pictures can convey meaning at the highest levels of complexity. Such decision pictures can be recognised by the motor channel to control muscle movement. But, any decision to act is instantaneous. The impact of a decision must persist till its objective is achieved. Muscle movements extend over time, while any decision to act occurs in a flash. If an objective is to be achieved, the muscle must move till the desired action is completed. A decision to sit down must persist till the act of sitting down is over. If a picture represents a decision, the picture must remain until the task is completed.

An iterating decision picture. In a cyclic system, a feasible solution of the need for persistence is for a decision picture to iterate, till the task is completed. In a television set,   the channel number that appears on the screen is a constant iterating image, while images on the remainder of the screen change with each cycle. A stationary symbol is produced in a cyclic system. Any decision, which requires a fixed objective, can be represented by a picture – the channel number. A fixed picture is needed till an objective is achieved. If a television set could recognise the channel number on the screen, it could respond with desired programs in the channel. If the channel number changes, the image events could change. The change in channel number is instantaneous, while the program persists. This essay suggests that a channel, with iterating signals, may convey the decisions of the nervous system to the motor control regions. That such pictures iterate in a goal channel. One practical method for the mind to control the body would be to consciously produce such iterating goal pictures.

The feeling to goal link. Let us assume that feelings represent needs of the system. Its constantly changing patterns represent demands from the system in real time. If impulses in this channel could control motor activity, felt needs could be satisfied. Feeling stimuli, like all nerve impulses, are cyclic patterns, which must trigger decisions. Any decision is an instantaneous event. But it must initiate a persisting activity. Let us assume that feelings trigger goal pictures. These pictures persist till goals are met. Motor activities are again cyclic, needing to be continually triggered. Thus, a goal channel establishes a link between an instantaneous decision following a felt need, and continuing motor activity to meet the need, by providing a persisting objective. The location of the feeling channel has already been indicated. The goal channel is suggested as a necessary adjunct to a cyclic pattern recognition system which achieves continuing intelligent behaviour. A possible physical location for this channel is indicated later in this essay.

The wellspring of consciousness. The mind controls the body when it is conscious. This theory of how the mind can learn to control the body requires an explanation of consciousness. The source of this phenomenon may be evaluated from a special context where a person becomes unconscious. This is known to occur when there is damage to a region of the brain called the reticular formation. Damage to most other regions of the brain, (including the removal of half of the cortex in an operation called hemispherectomy to remove tumours), causes only selective defects. (29) But, serious damage to the reticular formation results in prolonged coma. Cutaneous or olfactory stimuli to the reticular formation is known to restore a person from a fainting fit. Electrical stimulation of the reticular formation is also known to induce sleep in animals. Activity in this region can both raise the levels of consciousness and alertness as well as induce sleep. The reticular formation appears to be critical to consciousness.

A consciousness control channel. (11) It was shown how an executive attention centre could increase awareness by causing inhibited sensory inputs to fire. It was suggested that a similar channel may wake us into consciousness, with global awareness. It is now suggested that the reticular formation has initiating links to the sensory input and goal channels. That these links form a consciousness control channel. That these channels recognise cyclic impulses from the reticular formation and wake us into consciousness. That these control signals provide an ongoing consciousness drive. Cyclic impulses keep us conscious. The consciousness drive opens sensory channels and people become aware of their surroundings. It also triggers activity in the goal channel, which generates pictures defining objectives of the system. Goal pictures are automatically interpreted into motor activity.

Purposive movement through learned pattern recognition. In conscious activity, primitive animals may have inherited links between felt needs and purposive activity. Inherited memories may enable a need felt by a primitive brain to lead to an activity, which automatically satisfies that need. But human systems have highly differentiated purposes and even new ones. New purpose cannot be an inherited memory. Pattern recognition also implies the recognition of a pattern in memory. A pattern recognition system can only recognise a known pattern. Human beings learn through play and experimentation. These can lead to the successful achievement of goals. Feelings related to such successes can become memories for subsequent recall. Imagine that a contextual feeling is recorded against a goal picture which achieved a desired result. If this feeling is recalled later in a similar context, it can trigger the same goal picture, resulting in the needed motor activity. If a goal channel records and recalls successful goal pictures in the context of feelings, feelings can then trigger goals. The goal channel must learn to record contextual feelings against activities which led to successful goals.

Learning purposive movement. Consider this explanation of how the goal channel learns to recognise feelings to trigger goal pictures. It may be a process which begins in the cradle, with the intense activity of an infant. As the baby wakes up, the consciousness drive initiates the goal and sensory input channels. The goal channel produces random images. These symbols trigger erratic hand and leg movements. The active infant sees an object in its field of vision. Its waving hand touches the object. A feeling of satisfaction is experienced. This feeling is recorded in the goal channel against the goal picture which achieved this goal. A subsequent view of the object recalls this feeling. The goal channel recognises the feeling to trigger the goal picture. This picture results in the hand movement. Contextual recall of the feeling enables the child to moves its hand. An ongoing learning process continually adds memories of similar feelings to goals relationships in the goal channel. In a continuing process of repeated play and experimentation, the child learns to move its hand towards seen objects.

An ongoing learning process. Each achieved goal increases control. Pattern recognition permits fine discrimination of feelings to chose ever more precise goals. Practice and millions of similar memory refinements later, the child learns to reach out and grasp a pencil. As it learns to control its movements, the feeling channel takes charge of the goal channel and the random activities of the infant cease. Feelings control the movements of the child. They become purposive. Ultimately, such purpose covers the entire range of human activity, including speech. Speech, in fact, becomes one of the best expressions of an individual’s feelings. Each of these learned activities is the result of the memory of a goal and a learned activity, recalled in the context of a feeling. The cerebellum may store and recall memories of learned activities. Such a store of memory of habitual movements is known to interface seamlessly with purposive cortical movements.

Computation and pattern recognition for movement. It is argued that the body achieves daily routines through the instant recall of memories of habitual movements. A proof of this becomes a powerful support for this theory that pattern recognition, (not computation) is the key to an understanding of the mind. Both computation and remembered control systems can enable a robot arm to touch an object. It can compute the precise location of the object and make a sequence of inter-related decisions. It moves a joint at the shoulder to an optimum position and locks. The movement then switches to the elbow and subsequently to the wrist. Such an activity would be a precise, mechanically computed movement. Alternatively, the arm could be guided directly to the target. The complex joint movements which result can be recorded into the system memory. Subsequently, when the target is indicated to the system, it could recall its memory to follow this “learned” path to reach the object. The first process involves a complex computational capability in the control system and the second, a powerful memory. Pattern recognition implies the use of memory for intelligent activity.

A filing cabinet for habitual movements. There is an organ of the mind which appears to store such memory. The cerebellum is a miniature single purpose brain. It is laid out to assist cortical motor functions. It inserts habitual movements into purposive activity. Electrical stimulation of the proper primary motor areas of the cortex invoke simple actions such as the extension at a finger joint. While cortical control is simple, the cerebellum (30) is “necessary for smooth, co-ordinated, effective movement”. Failure of the cerebellum causes movements of a patient to become jerky. With cerebellar problems, the patient converts a movement which requires simultaneous actions at several joints into a series of movements, each involving a single joint. (31) When asked to touch his nose, with a finger raised above his head, the patient will first lower his arm and then flex the elbow to reach his nose. This problem is called “decomposition of movement”. Such behaviour   is remarkably similar to the actions of a primitive robot. Complex joint movements for directly touching an object are forgotten and cortical purpose just manages a tedious joint by joint movement. Purposive action continues to be achieved without the cerebellum. The patient can still reach the goal. But presence of the organ achieves the same goals smoothly.

Sequential control of motor functions. Research has shown (32) that the cerebellar cortex has all motor control functions with sensory inputs related to motor locations spread over its cortical layer with topographic precision. The cerebellum receives all the information that is needed for motor activity. As it takes over control of habitual motor functions from the cortex, (33) the entire outputs of nerve impulses from the cerebellum are through a type of nerve cells called Purkinje cells. Each Purkinje cell is known to have hundreds of thousands of dendritic inputs, with inputs from global sensory and motor functions. Each input evaluates a single parameter. In 1967, V.Braitenberg suggested the possibility of control of sequential events by the cerebellum. (34) The organ appeared to have an accurate biological clock. Impulses in fibres which link successive Purkinje cells reach the cell dendrites at intervals of one ten thousandths of a second. Alternate rows of Purkinje cells are excited, while in-between rows are inhibited. The cells fire sequentially.

A memory for habits. The cerebellum is known to perform a co-ordinating function. It has access to the entire range of contextual motor control information, an accurate pace setting mechanism and is purposively controlled by the cortex. The only output from the cerebellum are the Purkinje cells. They control habitual motor functions. Such motor activity meets cortical goals. This essay suggests that each Purkinje cell records a microscopic motor activity for a single motor neuron in the context of current global motor control data and the current cortical goal. The cell fires again whenever this picture is recognised. It recalls a memory to generate motor activity. Consider the habitual controls from the cerebellum in the simple act of sitting down. It is, essentially, a complex movement, controlled by both cortical purpose and habitual controls from the cerebellum. The height and position of a chair provide cortical goal information. The cerebellum manages the objectives of many muscle groups to achieve the cortical goal. It is reasoned that, with trillions of contextual pictures in its memory, the Purkinje cells may sensitively recognise each microscopic motor and goal prospect to support habitual acts.

A goal channel into the cerebellum. The cerebellum provides some hints of the existence of a goal channel as suggested in this essay. The cerebellar cortex receives a major input from a nucleus of cells called the olivary nucleus. Fibres from many regions of the cortex reach the inferior olivary complex and are distributed to all parts of the cerebellar cortex. (35) Damage to this group of fibres is equivalent in effect to damage to the cerebellum, causing severe loss of co-ordination of all movements. The cerebellum is known to insert a massive range of learned movements into normal activities. These inserted activities meet cortical goals. It is suggested that these fibres could form a goal channel, which continually informs the cerebellum of the current goals of the system.

A seamless interface. The cerebellum could be acting as a memory store, switching controls between it and the motor areas of the cortex. Neurons in the cerebellum could learn and reproduce remembered movements, becoming inhibited when cortical intercession takes place in any habitual movement. A person who reaches for an object could be moving his hand smoothly to a point close to the object through cerebellar controls with conscious controls taking over for a brief instant to adjust the hand. The cerebellum could again takes over to grasp the object smoothly. The Purkinje cells also have inputs from stretch receptors which report increased muscle tension enabling the organ to hand back habitual movements to purposive cortical controls.

Evaluation of a quarter million parameters for an imperceptible muscle shift. Children take years to learn to walk, run, or ride a bicycle. At ten thousand frames per second for each one of sixty thousand motor neurons they could represent astronomical memory capacities as each action to meet a cortical goal is learned by this organ. Habitual acts are unique to each individual. If they were computed movements, they would have been similar for every one. Being more quixotic and repetitive, it is more probable that these are remembered actions. Even the movements of a skilled gymnast are learned with painstaking practice. It is training (requiring memory) which achieves the unique, but co-ordinated movements of many muscles to precisely meet cortical objectives. This essay reasons that finely discriminative feelings trigger sensitive pictures in the goal channel to recall myriad learned movements from the cerebellum. The Purkinje cells represent a system which evaluates a quarter of a million parameters to generate a one ten thousandths of a second movement of a single muscle. Multiply that by 10,000 decisions every second and again by 60,000 motor neurons. Imagine the power of such a system.

Event Recognition

Intelligence through recognition. Recognition has been proposed as the key to understanding intelligence. It was shown how recognition may cause the feeling of pain to be heightened or suppressed. That it may enable signals from the reticular formation to create consciousness, or those from EAC to focus attention. That recognition by association channels may enable the identification of objects. That recognition by Purkinje Cells may enable the mind of a skilled gymnast to finely control his feats. Recognition, in reality, even provides the links, beyond the level of an individual intellect, for the highest levels of integration of a modern society. The lifeline for today’s technological world is the link created by the recognition of spoken and written messages. If there was no recognition, intelligence would not exist. It is now suggested that, just as it identifies objects, the mind may have a capacity to recognise the events around them. The process of event recognition may produce an altogether nobler level of intelligence.

Event recognition exists. Recognition of a pattern, which triggers a sequence of events is normal for computers. A “sort” command sorts a column of figures. A “copy” command copies a document from one file to another. The computer recognises the command to set in motion a train of events. In a reverse process, a computer in a bank may evaluate a sequence of transactions to trigger an alarm concerning a suspicious, or fraudulent event. A sequence of activities becomes the cause rather than the result of the recognition of a pattern. Computers can be programmed to recognise such events. We know that the mind recognises events with enormous power and subtlety. Words and sentences in language identify and define events in the environment in all their complexity. There is also simultaneous recognition of multiple events. When one drives through traffic, there is awareness of the movements of many objects in the field of vision. Cars move in various lanes. People cross the road. Signals flash. Each event has a distinct context, history and future possibilities. One recognises where a pedestrian comes from and where he is likely to go. Event recognition implies a complex past, a present and a future.

The time span for event recognition. Events are continuously perceived by the mind. It also absorbs complex information through the process of reading. It may be absorbing such information in discrete packets. The structure of language may indicate such a process. Each sentence has an acceptable length and every sentence closes with a period. A working memory (36) may store the first part of a sentence, while sense is made of the second part. While memory capacity is astronomically large, the working memory of the mind is just comfortable with a single telephone number. This may suggest a chunking of information into comparatively manageable segments before it is absorbed. A sentence contains data regarding objects and their static and dynamic relationships. It would be reasonable to assume that the time taken to absorb an average comprehensible sentence spans the time period for an event to be learned for storage in memory for subsequent recall and recognition. While even the instantaneous click of a camera lens is a recognised event, the structure of our literature suggests a period of ten to fifteen seconds for the absorption of a more complex event by the mind. The information in a sentence may be assimilated in this period.

An event picture. While an object can be identified instantly, an event needs to be evaluated over time, to achieve recognition. Even the simple act of running generates a sequence of complex patterns. A sequence of images are needed to represent the action. Even so, the event can still be represented by the simple word “run”. The word covers the sequence of activities. Words and sentences are static images. If they are considered symbols, then events can be identified through them. Symbols can be represented by pictures. Fitting into the IA pattern recognition model of this essay, an event can be represented by a picture. It is reasoned that such pictures may be imprinted in a goal association channel. Compare the process to imprinting an iterating image “run” on every frame of a movie of a running person. Subsequent recall of any single frame of the movie will contain the name of the event. The name symbolises the act and enables recognition. The process imprints an iterating image on every frame of a sequence of images. Subsequent recognition is achieved by identifying any unique quality of any one of these images. The imprinted iterating image identifies the event.

IA for event recognition. In IA, a pattern is recognised by identifying its unique qualities, which are absent in any other known pattern. Events are sequences of patterns. It is suggested that unique qualities differentiate every recognised event at the microscopic level. A fleeting smile is instantly recognised because of some infinitesimally differentiated and unique quality. That quality differentiates a smile from a grin or a smirk. While it may be impossible to define the characteristics, the ability to recognise and differentiate such events remains a normal human ability. It is known that event images are analysed by the mind into thousands of characteristics. If we assume an astronomically large memory, which stores profoundly small variations between the characteristics of events, finite event recognition may be practical for the nervous system. Once this capability is assumed, much of the mystery surrounding thought processes disappear. Most cognitive processes may revolve around the recognition of an event, its recall from memory and a visualisation of its consequences.

A goal association channel. This essay suggests that events may be identified by the mind as pictures in a goal association channel. An iterating image (like a channel number on a TV screen) is suggested, since events cover a sequence of images spread over a period of time. Association channels have access to sensory perceptions. All Barrel may perceive, say, a person sitting down. The goal association channel is suggested to have a parallel link to the goal channel. Certain Barrels (triggered by the current goal picture) may record the sensory image of the event. Subsequently, when the action is again perceived, these Barrels dip into recorded memory to trigger the recognition of the event “Sit”. Barrels which lack the images in memory are inhibited. The event is recognised and an event picture assembled in the channel. Just as a picture can represent many objects, event pictures in the channel may represent multiple events. The equivalent may be visualised as a matrix of “iterating channel numbers” on a single screen, representing several simultaneous events. Each can be represented by an independently changing number. A single picture may identify several events, in parallel. An astronomically large and finely differentiated mass of such pictures may represent our understanding of events.

Assembling event images. A musical composition is also an event. The process of combining such events to create a unique new event in memory was described by Mozart. (37) According to his narration, when feeling well and in good humour, melodies crowded into his mind. He retained those that pleased him. Once he selected a theme for a new composition, another melody came, linking with the first one. Each part fitted into the whole. Mozart continued that, when the composition was finished, he saw it as a beautiful whole in his head. Just as he could see a beautiful picture, he saw the whole composition at “a single glance”. This essay suggests that each melody may be an event, recalled by Mozart, evaluated and stored again into his memory as an event picture. These are again combined into a complex event picture, which covered the entire composition. Even though the composition is played over a long period of time, Mozart saw the complex event “at a glance” as a single picture in his head. This narration may support the concept of pictures representing events. The mind does recognise pictures, or, even, the implications of the word “read”, “at a glance”.

Thinking in symbols. Running, sitting down, or walking are simple events, recognisable from a single picture. Recognition of such events and their linkages is reflected in our language. A combination of multiple events is understood from “John ran home and got into bed”. The mind visualises the events “ran” and “got into” in the context of the objects “John”, “home” and “bed”, to create a new, recognised event. Recognition of each event may fire a picture in a goal association channel. With IA, the mind may have access to its global meaning. Language enables the combination of such symbols into a complex event, which is also assimilated. Language has certain universal elements in its construction. This may imply that the goal association process, which finally comprehends language, is itself divided into different segments, which are then assembled according to specific rules. Nouns, and verbs may be recognised separately and combined according to rules for recognition to generate meaning for the whole. The whole process of comprehending events has been suggested as being the function of a complex goal association channel, with many components.

Event pictures within event pictures. A logical extension of the recognition of events ia an event picture, which stores within it a sequence event pictures. The musical composition of Mozart appears to him as a single composition, which contains many pieces within. A shopping trip can be recalled as a sequence of many sub events within the main event. Such event recognition implies a combination of current inputs with memories from the past. These can represent rising hierarchies of understanding. Since pattern recognition, as envisaged in this essay, permits infinitely differentiated steps, event recognition can explain virtually any type of human intelligence from planning a strategy for war to comprehending the theory of relativity. They are hierarchies of events, which contain millions of images. Since pattern recognition requires only uniquely remembered and not logically connected links, any pattern can be linked to any other in complex associations. Any alteration of a component image leads to a new understanding and a fine difference of infinite subtlety in a new image of recognition.

Inherited emotional event recognition. The management of feelings has played a critical role in this essay. It dealt with the concept that nerve impulses represent different shades of feeling. Feelings include those generated by bodily demands and those fired by intellectual perceptions. Thirst is triggered by a bodily demand. Fear, a sense of isolation, loneliness, or disgust are feelings which result from intellectual perceptions. This essay suggests that certain feelings may be triggered by the recognition of event pictures from the goal association channel. Impulses which represent fear, sorrow, or jealousy may be triggered by specific, recognised categories of events. These impulses may form the signals in the feeling channel. Each Barrel may recognise event pictures specific to an emotion. The Barrels which fire to trigger a feeling of disgust may recognise an event picture which represents a revolting event. Such memories may be inherited at birth in the Barrels of the feeling channel to enable the system to respond with feelings to events.

Event memories. There is reason to believe that our memories of events are recorded as the feelings which we experienced during the event. When we recall a conversation, we can recall what was said, but not the exact words. But we can remember the tone and the meaning. Feelings can convey such meaning. The recall of visual memories was reasoned to be triggered by the Barrels of the visual cortex. That these memories were recalled in the context of feelings. It is suggested that the Barrels of the feeling channel may trigger feelings when events are recognised, or recalled. That the goal association channel recognises events as event pictures. It is suggested that each Barrel in the feeling channel receives and stores memories of these event pictures, which triggered the related emotion. Subsequent recall of the event recalls the related feeling. Since event pictures have been suggested to be iterating images, the sequence of feelings related to an event are recalled when an event is remembered. Iteration may give a time dimension to experienced feelings, enabling the mind to recall events in sequential detail. The feelings, in turn, may recall visual and sensory memories.

Feelings as an accurate record. Modern society communicates a major portion of its sophisticated messages through the medium of language. Text books, novels and scientific articles convey complex meaning to readers. A language, with about quarter of a million words conveys a majority of the information between human beings. It is suggested that the sequence of images in a million dot feeling channel can convey all this and more within the mind. When we narrate an event, the recalled event pictures convey the concept of the event through a sequence of recalled feeling images. The goal association channel recognises and translates it through the speech mechanism into language. As a person expresses the words related to a feeling, the mind has access to its entire memory store in the context of that feeling. IA selects the words which exactly suit the context with precision. When an inner voice speaks the ideas in the mind, the speech mechanism is merely translating the current sequence of feelings.

A summary of the event to feeling link. Event recognition has so far never been suggested by AI research. Infinitely graded categorisation itself has never been a possibility. So, event recognition was never visualised. But IA makes such profoundly sensitive pattern recognition possible. It is but a step further to imagine a time dimensioned pattern recognition system which can recognise events. Human experience and the clarity of language clearly indicate that events can be recognised with precision. This process also may use mind’s language of pictures. Events can be represented through words. Pictures convey more meaning than words. Iterating pictures in a goal association channel may represent events. Events could be absorbed in brief time capsules, as in the time span in which the mind grasps a sentence. They may be recorded for recall as an event picture by the goal association channel. The mind is known to be aware of several simultaneous events. An event picture could represent several such concurrent events just as an ordinary picture could represent many objects. Combinations of events could also become complex event pictures, which represent sophisticated concepts such as war, or democracy. And, finally, event memories may be stored as sequences of feelings in the feeling channel, which could be recalled by the iterating event pictures. The recalled feelings would, in turn trigger the recall of sensory memories of the event.

The Goal Drive

The goal channel. This section elaborates the concept of a goal channel. It has been a suggested as an essential link between two known and experimentally verified entities – the feeling channel and the motor control network. Such a channel is a needed link, if the IA pattern recognition process is offered as a basis for explaining the workings of the mind. The goal channel is presumed to function from certain cortical areas, which are known to trigger sequences of motor activities. The channel would include all those regions which issue motor control instructions. This essay suggests that the purpose of the mind may be an iterating picture in a goal channel. These pictures may essentially represent physical objectives to be achieved by the motor system. These pictures are presumed to be intelligently interpreted by the mind. The motor channel may interpret the goal pictures as instructions for sequences of motor outputs. Immediate purpose may be determined by the current feeling. This feeling is shown, later in this essay, to be a sophisticated intuitive choice by the system. This section explains the likely functions of a goal channel.

“>Knowledge which achieves objectives. This essay assumes that nerve channels have powerful memories. Each channel may store the global knowledge of the system, concerning its specialisation. The channels may use IA to mutually and instantly exchange and interpret intelligent responses. A goal channel may trigger motor activity and represents the intelligent purpose of the system. It is purpose which determines and executes those steps which enable a person to achieve an objective. Purposive activity has three components – the desire, the purpose and the activity which achieves that purpose. An infant’s desire to touch an object is followed by a purpose, which triggers muscle movements to touch the entity. A person may wish to copy a file on a computer. He interprets this purpose to the computer as a typed in “copy” command. The computer then executes a series of steps which achieve his objective. This essay suggests that the goal channel may be a special interface for purpose. It may interpret feelings (the needs of the system) to determine purpose and trigger motor activity. For this, it may store the knowledge of the system concerning groups of motor objectives which achieve each purpose. Such purpose may, ultimately, be the driving force of the system.

Purpose as a route map. A nosebrain, which recognised the smell of an object, issued additional instructions to consume or avoid the food. These instructions were followed by its motor systems. The mammalian feelings system permits a wider range of options. The cerebellum does not provide cortical purpose, but is known to assist in its achievement through sequences of recalled motor activities. Just as a route map recalls the physical directions to reach a destination, the goal channel may recall and set the physical goals which control motor activity to achieve an objective. If the objective is to leave the room, the goal channel may identify the door as a physical goal. The cerebellum may co-operate with muscle movements in a stroll to the door. If the objective is to escape from danger, the channel may contextually select the easiest escape route. During a drive, the goal channel may determine the right turns, in context, to reach a destination. The channel may contextually respond with the next most suitable physical goal for motor activity, to meet a particular objective. This objective may meet the needs of the current feeling. Such physical goals may be in the channel memory in the context of the past achievement of similar objectives.

A goal channel with intuitive intelligence. The channel may have access to an adequate inflow of information to intuitively choose physical goals for the system. Society teaches an individual how to achieve objectives from driving a car to building a house through a range of pre-defined physical activities. The channel may build up a massive memory of physical goals as responses to feelings. The channel may set sequences of physical goals for complex objectives – to flee, attack, or negotiate. The choice of goals in response to feelings may be established at a young age. Such goals may be learned gradually from infancy, forming sequences of physical activities, to be recalled instantly. Many patterns of social interaction may be learned in playing fields, where each feeling may result in a particular fashion of personal contact.

Inherited responses to feelings. A goal picture may have many components. Geographically, the channel may be widely distributed. The levels below the thalamus are known to have substantial powers of self management of basic life support systems, including feeding, drinking, apparent satiation and copulatory responses. The interpretation of feelings and the issue of such control instructions may be perennial elements of a goal picture. Some bodily responses to feelings are automatic. The cerebellum was shown to control habitual movements, under cortical guidance. That the process may be learned by the complex of cells surrounding the Purkinje cells in the cerebellum. It is now suggested that over and above such learned movements, the cerebellum may respond with specific physical activity to the interpretation of feelings by the goal channel. The cold sweat of fear, or the shuddering sobs of sorrow may be the inherited responses triggered through the cerebellum by the goal channel when it recognises specific feelings.

A goal picture may be the primary drive. The purposive element of the channel may provide a mechanical interface between feeling and motor activity. Feelings compel action. The individual may not be conscious of the many small subsidiary motor activities which achieve a goal. The next subconscious objective that meets a feeling may be selected and acted upon without significant conscious input. A child goes into a tantrum. A man commits a violent act. Recognition of an event causes strong feelings to be experienced. These may automatically trigger goal pictures and resultant motor activity. The process may be stopped only if the goal is changed. Once a goal decision is made, the body is compelled to achieve the goal. The concept of a goal channel may explain the powerful drives that impel an individual. Many day to day activities may also involve goals that are constant over hundreds of sleep and waking cycles. Childhood feelings may set long term goals, providing contexts for the launch of current feelings. Such elements of the goal picture may compel one to continue, consistently keeping a focus on primary objectives, over the years.

Feedback loops co-ordinate output. It has been reasoned that the current feeling determines the goals and hence the activity of an individual. From thousands of competing wants, the system must select a single one for action. Intuition, as implied by IA, may be uniquely fit to contextually pick the single most germane selection. This capability is best illustrated in the motor channel. Each one of 60,000 motor neuron has up to 20,000 inputs from other neurons as it travels down the spinal cord. Feed back loops use information from lower levels to modify inputs at higher levels. Every muscle has an opposing one and many muscles must co-ordinate to achieve even the simplest task. Any selection may instantly inhibit conflicting demands. Such decisions occur thousands of times a second. It is logical to conclude that these feed back circuits may have a singular ability to instantly consolidate the backward and forward interactions of millions of simultaneous inputs. Galaxies of parameters may be processed, using phenomenal intelligence concerning their interactive impact. After such assessment, a single final picture delivers smooth muscle movement. It is suggested that a similar process may determine the current feeling of the system.

The limbic system may decide the current feeling. Experimental evidence attributes a significant role for the limbic system in the realm of emotions. This essay suggested that the output of the limbic system may represent the current feeling. It is a ring passing through the thalamus, consisting of over a million fibres, which acts in both directions. A process similar to that in the motor channel may take place in the limbic system. It may evaluate millions of received parameters to determine, instant by instant, the final output. As in the case of the motor channel, where many muscle movements oppose each other, many feelings may also be in conflict with each other. While a person is reading a story, an unexpected sound occurs in the background. The sound generates a feeling. The feeling related to the situation in the story dominates the system. At some point, suddenly, the feeling generated by the background sound obtrudes. This feeling may now set system goals. The attention of the mind now changes focus to the sound. It is suggested that the limbic system may continually process myriad feelings generated by bodily needs and intellectual perceptions to generate the current feeling. This feeling may inhibit conflicting emotions to dominate the system and trigger goal images.

When a wish becomes an act of will. William James (38) narrates the internal conflicts of a person while getting up from a warm bed on a cold winter morning. One lies unable to brace oneself to get out of bed. Then there is a sudden decision. One may think of some thought connected with the day’s activities. He calls it a lucky idea, which “awakens no contradictory or paralysing suggestions, and consequently produces immediately its appropriate motor effects….” Suddenly there are no negative feelings and one gets quickly out of bed. He calls it a shift from “wish” to an act of “will”. It is suggested that the lucky thought may have been triggered by that segment of the goal channel which manages longer term goals. It may have called up images which create a feeling of the need to achieve the day’s duties. The limbic system may evaluate competing feelings, and balance them to determine the current feeling. This “lucky” emotion may inhibit opposing feelings. It may bring appropriate context and memories. The goal channel may recognise the feeling to initiate “appropriate motor effects”. The “act of will” may have been a sophisticated decision by the limbic system.

A subtle feeling to goal relationship. It is suggested that the distinction between feelings and goals may be hard to draw in some areas. Some feelings may be subconscious, with their impact triggering goal pictures and resultant visible activity. Thus the impulses that trigger many feelings, such as curiosity, or playfulness may be subconscious, but may produce resulting activity which meets the parameters of the feeling. The event recognition process is reasoned to have an inherited code to trigger specific feelings when a particular type of event is recognised. As an example, when an object or event evokes interest and cannot be recognised, a feeling of curiosity may be triggered. This feeling may, in turn trigger goal images which facilitate investigation. The person may then follow those activities which assist in recognition of the significance of the event.

The Mind

A composite picture. The IA concept visualises intuition as a process of infinitely graded category recognition, which enables a supersession of the “understanding” of science by the “wisdom” of the mind. Such wisdom is reasoned to be the property of neural channels. The channels are assumed to be electrical circuits with powerful memories, with intuitive intelligence of a very high order. Biochemical messages may further aid this process. The mind does not appear as a single network intelligence. Myriad separate intelligences seem to operate independently from thousands of specialised and geographically identifiable neural channels. These channels may be distinct entities, mutually exchanging and recognising unique and perceptive messages. The picture theory of internal exchange of information is offered as their medium of communication. A holistic, real time interaction is made feasible in such a system by the swiftness of IA. Such circuits may further explain certain mysterious functions, such as drives, consciousness, will and judgement. An attempt is made, in this section, to combine these ideas and functions to present a composite picture of the mind.

Reasoning chains for understanding. Some scientists may dispute the superiority of human wisdom over modern scientific understanding. Science is founded on inductive, analytical logic. Logical analysis chunks information into minimums that fit a specific rule, or reason. Science assembles facts that fit these rules to create understanding. It presumes that any understanding must be built on a logical structure of underlying reasons. Reasoning chains underpin science. A phenomenon is presumed to be understood only when the underlying causes are well defined. But the information inflow into the scientific world overwhelms its capability for providing supporting reasoning chains. Every science has spawned a dozen more. The vastness of the universe, billions of years of history, the complexity of living things and the miniature worlds in the cell and the atom dwarf scientific ability to provide reasons. Over centuries of research, the reasoning chains proposed by the scientific community, even those underlying its most fundamental beliefs, have also been overturned by new discoveries. Reasoning chains have fallen far behind in providing understanding.

A wisdom which supersedes an understanding. Intuition, as implied by IA, uses inductive logic to identify the unique elements which link two patterns through a process of elimination. Such recognition of unique links between complex patterns is reasoned here to be the basis for human wisdom. Intuition instantly recognises the link of the pattern “the eyes” to the pattern “look friendly”. This is human (or even animal) wisdom. As against this, a reasoning chain would be hard pressed to explain the causes, since analysis of the two patterns may yield an astronomically large number of categories with vague relationships. Each element of vagueness further weakens a reasoning chain. The intuitive link, on the other hand, may be based on powerfully accurate and logical perceptions of concrete experience. Instead of seeking underlying reasons, the intuitive process may find unique links from a vast storehouse of experience. Intuition may as often be just as wrong as scientific reasoning. But it girdles a wider horizon and is a powerful weapon for coping with the environment. This essay suggests that the wisdom created by pattern recognition may be superior to the analytical understanding created by science. Science assists such wisdom with reasoning chains. This essay attempts to be one such reasoning chain.

Many intelligences in a federal system. Current neural networks theory may be compared to the effect of ripples created by a sequence of pebbles dropped into a still pool. The ripples interact and can be expected to indicate the global outcome of every dropped pebble. The theory suggests a similar global intelligence, with every portion of the network reflecting every event that occurs in the nervous system. As against such a single intelligence, this essay suggests that many intelligent regions may perform independent functions in the nervous system. Such regions, their functions and the nerve fibre links between them have been extensively charted by science. These regions may communicate internally through intelligent pictures. The evidence for the “picture mode” of transmission is provided by the phenomenon of point to point “mapping” between the myriad neural channels. It was reasoned that the association region may inform the prefrontal regions that a pair of scissors has been recognised through a picture. This message is an independent communication between two finite intelligences and not “signals that balance” an entire network. Medical evidence also supports the concept in this essay of myriad independent intelligences. These intelligent circuits are known to form a hierarchy of interactive subsystems, each demanding only critical inputs from higher levels. The management has been likened to a federal government. At the lowest levels, people manage their affairs by themselves. Higher level decisions are made by the communities, by the state governments and finally, by the central government.

A self managed system at lower levels. This decision making system is revealed in the “homeostasis” of animals in the survival process. Homeostasis (39) is the achievement of a relatively constant state within the body, in a changeable environment. It is naturally maintained. It is brought about by various sensing, feedback and control systems, supervised by a hierarchy of control centres. The concept that these centres mediate these controls is based on a wide base of experimental evidence, gathered by studying the impact of destruction of localised topographical targets in animals. As higher levels are included with the spinal cord below the cut off section, more effective controls are retained. The thalamus is the major nerve junction sitting at the apex of this survival hierarchy. The levels below can sustain a wide range of activities including feeding, drinking, apparent satiation and copulatory responses in a wide range of adverse conditions. Obviously, an incredibly high level of intelligence and self management exists at these lower levels.

Selective awareness. But, are we just mechanically constructed objects which respond with electrical and chemical impulses to the external environment? All of us have a deep down knowledge of being free of the mechanisms that generate the impulses. We can vividly see visual images and powerfully experience a multitude of sensations and feelings. Unlike a television camera or a microphone, we are independently conscious that we are seeing and hearing the world around us. If something is seen, surely there must be someone who sees it – a ghost, or a soul? But, while neural impulses pulse through every part of our body, we have the sensation of seeing only when these impulses impinge on the visual cortex. Nerve impulses in the heschl gyrus alone cause us to hear sounds. Are these portals into the soul? This essay suggests that among the myriad pictures evaluated by the nervous system, consciousness involves a limited group of pictures of which a human being is conscious. Like every group in the nervous system with its own intelligence, the conscious intelligence may be an independent entity, constituting a group of neural circuits. It may feel and act as an independent entity. It is suggested that such an intelligence may operate in the region around the pre-frontal lobe of the cortex.

Pre-frontal regions and a sense of self. The geography of nerve channels pinpoint many functions, which inter-communicate. While all other regions of the cortex interact mostly within finite regions, the prefrontal lobes have abundant connections (40) with the association regions of the three sensory lobes. The association regions are known to perform the most important act of recognising perception. The message of recognition is carried to the prefrontal regions. These connections may be one to one projections. Recalled memories, recognition of multiple objects and complex events may travel as pictures to the prefrontal cortex. This region may be the conscious mind that sees and knows that it sees. Suppose a computer is constructed to receive, categorise and store received sensory images. Suppose parallel processing enables a second internal system to receive all such information, including its own operational parameters. The second system may truthfully say “I can see and hear you. My speech mechanism is functioning at optimum efficiency”. An autonomous intelligence in this region may independently evaluate the system to enhance our impression that we are independent of ourselves. Consciousness and the sense of self may be moulded by the circuits in the pre-frontal regions.

Consciousness may provide context. The conscious mind receives sensory inputs, feels emotions, recalls memories, focuses attention, recognises objects and events, visualises and evaluates alternatives, and wills motor activity. But, while all sensory inputs are monitored, only a small fraction enters consciousness. The motor functions, stored and recalled by the cerebellum, remains subconscious. Even the act of will does not enter consciousness. Only if the mind is questioned does it reveal a decision to sit down, or to go to the water cooler. Many feelings which trigger goal events also may not enter consciousness. From an astronomically large volume of information, and a wide range of options, intuition forces the elimination of all alternatives to pin down a single choice. While the mind may be processing many feelings, the conscious mind may experience only a single dominant feeling. The feeling may provide the context for the recall of a memory of an event. It may provide a file pocket and reference point. A single hook, a focal point is vital for context in recalling memories. The conscious mind may provide a critical focusing point for context. Since the volume of information manipulated by the nervous system is massive, nature may have restricted stored memories to those entering a limited region of consciousness.

Pre-frontal regions pass judgement. It has been suggested that motor activities may be triggered by feelings. Animals are known to sustain a wide range of activities including feeding, drinking, apparent satiation and copulatory responses in a wide range of adverse conditions, in spite of being disconnected from the levels above the thalamus. As such, it is reasonable to presume that a wide range of feelings which trigger these activities may be generated by levels below the thalamus. The prefrontal regions appear to generate a different set of feelings. Some years ago, (41) a procedure called prefrontal lobotomy was applied for patients with intractable pain, or in attempts to modify the behaviour of severely psychotic patients. The surgery disconnected the prefrontal zones from the regions around the thalamus by cutting nerve fibre connections. It was noted that such patients were “tactless and unconcerned in social relationships, with an inability to maintain a responsible attitude”. These patients were seen to “lack judgement”. Presumably, judgement may result from the more intellectual feelings triggered by the prefrontal regions.

Cutting off judgement. The geographic differentiation between perception and action is seen in prefrontal lobotomy. Judgement is a process which evaluates the impact of a proposed course of action. This essay suggests that any proposed action, even a rude one, will trigger a goal picture. A goal picture is a planned event. The event may be recognised by the prefrontal area to generate feelings related to its outcome. Normally, a person recognises the impact of rudeness, to generate a feeling of impropriety. If the limbic system received this message, it may instantly select it as the current feeling. If the current feeling was negative, the rude action would be instantly inhibited. With pre-frontal lobotomy, this feeling may not be conveyed to the limbic system. This essay suggests that event recognition by the prefrontal regions may trigger feelings concerning complex human interactions. Without access to such feelings, the limbic system may permit the execution of tactless actions. While such intellectual feelings may be generated in the region of consciousness, the so called primeval urges may be generated from regions below the thalamus.

When will is bypassed. Even while the system is incredibly sensitive to one’s needs, one is aware of the difference between voluntary and involuntary actions. This essay has suggested that many intelligences operate in the system. The conscious mind may be one of these. It may appear as the “self” and “the master”. The system seeks to be sensitive to “the needs of the master”. But it may not always yield control. Any planned course of action generates a feeling. If it is acceptable to the system, action is triggered. The limbic system may select the current feeling from a range, including the “wishes” of the conscious mind. It may have inherited code recognition parameters, which even prohibit the dominance of self destructive feelings. If a feeling is unacceptable, conscious will may be ignored and the action inhibited. While an individual may “will” the movement of a limb, such will may be over ruled if it does not conform to a “WASP” formula. The action should be Worthwhile, Appropriate, Safe and Practical. One gets up out of bed if one feels it is worthwhile. No ordinary person can will himself to take an action which is inappropriate, unsafe, or impractical. This can seen when a person freezes on the high diving board, in spite of his “wish” to dive.

Limited intellectual control. The outcome of a proposed activity may be instantly transmitted to the pre-frontal regions as a picture in the goal association channel. Recognition triggers related feelings. One wishes to bring one’s knee up. A goal association picture would inform the pre-frontal regions of the outcome of this move. One’s wish, expressed as a feeling, faithfully triggers motor activity through an appropriate goal picture. The knee comes up dependably. But what happens if one had this ridiculous wish while standing in a crowded lift on the way to office? The event recognition picture instantly transmits the impact of this move on a neighbour. The picture would trigger a powerful feeling that it would be inappropriate. This feeling immediately triggers a goal picture which inhibits such motor activity. When one sets out to do anything, one instantly knows of the social impact of that action. This knowledge exists in the prefrontal regions. Evidently, if the pre-frontal regions are disconnected, such controls are disconnected from the system, and one’s activities lack judgement. This may also be the reason why an individual may sense a lack of control. The conscious mind may reside in a region which has only an advisory role, while major decisions are taken elsewhere.

Decision by the system. Let us consider the process that converts a judgement into a motor activity – the decision making process. We don’t merely respond to sensory inputs. Beyond mere recognition and evaluation, we have the powerful ability to initiate, cause, activate, begin, create events. Who initiates all this activity? Is there a free will, which is exercised by the individual to control his actions? This essay suggests that the consciousness drive continually triggers activity in the feeling, awareness and goal channels. The most powerful indicator of a free will is demonstrated by one’s ability to move one’s muscles, or to focus attention. This initiation may be only an automatic mechanism which merely triggers the next highest priority activity of the system, while there is consciousness. The “initiation” could merely be a switching process by the limbic system, which selects the most powerful feeling as the current motor control option. That feeling becomes the will of the mind. The water balance in the body reduces. A feeling of thirst is triggered. The limbic system switches the feeling in as the highest system priority of the moment. A goal picture is triggered. The cerebellum assists the cortical decision in a habitual trip to the water dispenser. A series of motor events meet the goal. Thirst is quenched. A high level goal picture triggers a reminder of the “urgent” file demanding attention. The next feeling arrives to trigger a quick trip back.

“Will” may be an illusion. One can focus attention wherever one wishes. It is an act obviously seen to be willed by an individual. This process is controlled by the executive attention centre (EAC). In reality, the process may be the result of an intuitive search. The creative process demands focus on new contexts to find solutions. An idea or object which becomes the focus of attention may be contextually the most appropriate in the light of the current goal. The goal channel may select the focus. Since it precisely meets one’s objectives, one is deluded into believing that one “willed” the focus of attention. Imagine a slave who is so sensitive to its master’s needs that he meets these instantly. The master may believe that his will controls the slave. The truth may be that the slave is voluntarily following the will of the master. It is one of the key themes of this essay that a pattern recognition system can be so microscopically sensitive to the demands of the nervous system that its need (will) becomes its command. This sensitivity may give one the illusion that one is in command of one’s body. It may be the equivalent of believing that one controls one’s shadow.

Even animals are creative. A search process, which enables the mind to seek information to assist the achievement of goals may be a powerful subconscious process. Konrad Lorenz (1972) describes a chimpanzee in a room (42) which contains a banana suspended from the ceiling just out of reach, and a box elsewhere in the room. “The matter gave him no peace, and he returned to it again. Then, suddenly – and there is no other way to describe it – his previously gloomy face ‘lit up’. His eyes now moved from the banana to the empty space beneath it on the ground, from this to the box, then back to the space, and from there to the banana. The next moment he gave a cry of joy, and somersaulted over to the box in sheer high spirits. Completely assured of his success, he pushed the box below the banana. No man watching him could doubt the existence of a genuine ‘Aha’ experience in anthropoid apes”. This brilliant insight implies that creative effort is not necessarily a human prerogative, but an essential nervous system process existing in all animals.

Creativity as a pattern recognition process. The mind has the unique ability to question itself. What is to be its next course of action to meet a particular goal? The act of selecting an option may be considered a genuine act of will. That act may come from a feeling. An element of uncertainty precedes such a feeling. This is an interim subconscious period of search. It is suggested that there may be an intelligent search process in the nervous system, which continually evaluates alternative contexts against a visualised goal image. Goal pictures control ongoing motor activity. A sequential test of all perceived contexts for an answer to the current objective may merely be another motor activity. Instead of despatching sequential impulses to manage muscle movements, such impulses may manage a continuing test of current context against current goals. Such testing may occur constantly in the subconscious, bringing on the “Aha!” experience of discovery, when a set of imagined events is perceived to meet all the parameters required for achieving a singular goal.

Creativity from an algorithm. The old adage is that a computer can never be original, since it only spews out what has been programmed into it. Computers follow algorithms. Creativity of the human mind has been the most powerful argument against an algorithmic explanation of the mind. But, if a sophisticated computer could keep experimenting in its memory with multitudes of combinations, with the goal of achieving a desired result, it could arrive at a new and original solution. A computer can be programmed to “recognise” an “imagined” event which can achieve a specific goal. The chimpanzee manipulated many images in its mind, chancing on the possibilities following the position of the box below the banana. It instantly perceived the sequence of events which could achieve its goal. As at the time of this writing, the memory capabilities of computers and their capacity for manipulating images are woefully limited. Using its massive memory based on experience, the mind may create myriad images in imagination. Some of these may link in exotic combinations to create brand new inventions. If a prodigious memory and sensitive pattern recognition is assumed for the human system, it may explain the development of imaginative and exciting concepts, products and processes. An algorithmic (and intuitive) recognition process may be primary to this capability.

An Expert System Shell

Artificial Intelligence for accessing data. The use of the personal computer has become a world-wide phenomenon, enabling people everywhere to improve the quality of their work. Initial applications focused on financial accounting, word processing and spread sheets. Recently, the Internet opened opportunities to access information from computer databases in a wide variety of fields. The use of key-words now enable people to locate topics of interest. But, in fields where specialised words are used, the user needs to know the exact word to locate a subject. Expertise is essentially the knowledge of the exact word that defines a problem – such as the name of a disease which exhibits a group of symptoms. Expert systems can locate a problem from a description of such symptoms. They can play a major role in assisting people to locate vitally needed information. But, expert systems should be fast and they should avoid stupid questions.

A wide field of possibilities. Expert systems can assist millions of users to access key information regarding computer software, which grows more complex by the day. The legal aspects of commercial activities cover taxation, company law and constitutional law. Speedy access to particular case laws is a vital need for the legal profession. Computer diagnosis of diseases can assist hospitals, general practitioners and students to find vital information in specialised fields. Expert systems can guide staff in large organisations which have thousands of pages of manuals concerning complex procedures. Diagnostics can assist in problems related to machinery and equipment. In all these fields, existing manuals can be entered into expert systems if only the process was fairly simple and straightforward.

Simplification of procedures. Traditional expert systems require knowledge engineers, who understand the logical reasoning in a diagnostic session and can encode this logic into “If, then, else” rules. When the database is large, questioning priorities may need to be supported by probability estimates of likely enquiries or heuristic assessment of enquiry directions. Such rule based systems also become complex and intractable when the size of the knowledge base expands. This section describes an Expert System Shell based on the Intuitive Algorithm (IA). The IA shell requires merely the categorised entry of data and the design of questions which can identify these categories. The shell isolates categories, taking uncertainty into account – a question may or may not identify a particular category. The shell avoids the perennial AI problem of asking stupid questions. The shell prioritises questions and produces answers based on the IA elimination process.

General terminology. The Shell follows a certain terminology in its diagnostic processes. There are: Objects. Objects have Properties. Properties suffer Alterations. Alterations are induced by Causes. The Relationship between Causes and Alterations form Patterns. Causes, Alterations and the Patterns of their Relationships are stored in the memory of an Expert System. Typical Applications: Object: Person. Property: Health. Alteration: Symptom. Cause: Disease. Objective: Recognise Disease from an evaluation of Symptoms, using the Pattern of their Relationships. Similarly, an Object could be a Legal Entity. Property: Freedom. Alteration: Civil Activity. Cause: Legislation, or Case Laws.

The Shell Program. An Expert inputs Knowledge into the Shell Program to create an User Program. The User inputs Y/N answers to onscreen Alteration Queries which help to identify Causes. The general functions are as follows:

*Type Names. A 40 Character Alteration Type Name and Cause Type Name for data entry reference. For a Medical Program: Alteration Type Name = Symptom. Cause Type Name = Disease. Further references in the Program will be to Symptom and Disease.

*Alterations. A 20 Character Alteration Name. An 80 Character Question to User. Each screen holds 64 Alteration Entries, so that the Expert can have a global view of the questioning process. A 4000 Character description screen permits the end user to obtain details concerning the question covered by the Alteration. All data entry can be edited.

*Causes. A 20 Character Cause Name. An 80 Character Identifying Statement. Each screen holds 64 Cause entries. A 4000 Character description screen permits the end user to obtain details of the Cause. All data entry can be edited.

*Hypertext. The Shell allows the Expert to create hypertext links between Causes, allowing the User to search through the database, by clicking on highlighted words.

*Relationships. The Shell screen permits the entry of the Relationship between an Alteration and a Cause. Yes/No/Maybe entries can be entered with a single keystroke. “Yes” is entered when the Alteration is positively present for the Cause and absence of the Alteration clearly indicates absence of the Cause. “No” is entered when the Alteration is absent for the Cause and presence of the Alteration indicates that this Cause can be eliminated from further consideration. “Maybe” is entered when presence, or absence of the Alteration does not indicate presence or absence of the Cause.

*Preparation of the expert system. The Shell is designed to enable the Expert to view the global range of Causes and design Alteration questions which efficiently slice the matrix of Causes in multiple directions. Other inputs include the Title of the Expert System, Introductory opening screens and Menu screens. Data in the completed program is compressed and the program is compiled producing a .EXE file.

*User interaction. The User is presented with the option to carry out a word search, a menu search, or an expert system search. The expert system choice presents the User with a sequence of questions, with Yes/No/Skip options to arrive at a list of Probable Causes. The User can get further details of each selected Cause to verify the diagnosis. The User can also backtrack the questioning process and alter the Y/N/S entries.

*The process. An “Yes” answer eliminates all Causes which have been entered with a “No” relationship to the Alteration question. A “No” answer eliminates all Causes which have been entered with a “Yes” relationship to the Alteration question. The program chooses questioning priority by selecting Alteration with highest number of “Y” relationships. The program also eliminates all Alteration questions, which have “Y” relationships only to eliminated Causes. When there are less than 4 remaining Causes, the program presents a list of Probable Causes.

Unlimited rules. Since it is not necessary to design complex reasoning chains, there is no theoretical limit to the size of the database which can be handled by the IA system. Each Cause is eliminated based on a logical relationship. Such logical relationships are entered as “rules” in the traditional expert system. While such systems will be prone to error when the number of rules exceed a thousand, the IA system can accurately work with even a hundred thousand rules. This opens the possibility of using AI in voluminous subjects which have never been attempted because of the complexity of rule based expert systems.

Uncertainty. An extremely powerful part of the program is its ability to handle questions which may or may not have an impact on the outcome. A particular symptom may or may not be present for a disease. The program will still eliminate those diseases which have a positive or negative impact, depending on the answer. In spite of the uncertainty, the elimination proceeds with power. The ability to deal logically with uncertainty is an exceptional feature, which is not present in any other type of computer based logic.

Stupid questions. If an answer clearly indicates the absence of a related disease, a further question which indicates the disease is called a “stupid question”. Traditional expert systems struggle with the problem of trying to avoid stupid questions. In the IA system, when a Cause is eliminated, the program also eliminates any Alteration question which has a “Y” relationship only to the Cause. So, the program will never ask a stupid question and the expert does not need to design the program to cover this eventuality.

Commercial value and optimal size. Speedy access to data has a commercial value in all those areas where people routinely use computers. Expert systems which use IA can provide a third level of help for commercial computer programs. The experience of the author is that expert systems, which solve problems in other areas, require an optimal size to be of value. They should not appear to be toys. Speedy access to all the information in a 400 page manual may not enthuse users. They may consider such information to be basic. A 3000 page data base may be considered more useful. In India, Constitutional Law can be summed up in about 400 pages. Related case laws may cover 3000 pages. A law practitioner may consider the extraction of a Constitutional Law Provision as too basic, but would value the extraction of a related Case Law. An expert system may be planned only for areas of commercial value and should be of optimal size.

Unutilised potential. AI researchers have tended to focus on the need for codification of knowledge from experts. But in all commercially viable fields in today’s world, expertise is already recorded in research papers, reference books and manuals. It is more practical to design an expert system from published data and use the expert only to verify the accuracy of the data and the acceptability of the questions. The lack of availability of a wide range of expert systems for public use is a clear indication of the rule size limitations, complexity and impracticality of current rule based expert systems. There is an urgent need for the use of practical AI solutions in thousands of areas for problems which people encounter in their daily lives.


1.The nerve impulse is a sudden change in the permeability of the membrane to sodium ions. The sodium ions carry a positive charge and displace the potassium ions, raising the voltage. This increased voltage is carried through the axon in successive steps. The speed is barely .5 to 120 meters per second. A volley of such nerve impulses are carried by the axon in a single direction only. The Oxford Companion to The Mind, 1987, Richard L.Gregory, Nervous System, P.W.Nathan, Pages 517.

2.Experiments by Karl Lashley in the 1940s showed that the skills learned by rats in maze running could not be obliterated by removal of particular cortical areas. The results of such ablations were generalised deficits proportional to the amount and not the region of the cortex removed. The Oxford Companion to The Mind, 1987, Richard L.Gregory, Memory: Biological Basis, Steven Rose, Page 458.

3.If the touch of a single hair is critical information, all surrounding sensory inputs are shut off to highlight the message. Similar automatic emphasising of contrasts takes place for both visual, auditory and sensory inputs. The brain actively participates in closing off irrelevant sensory inputs. Gray’s Anatomy, 1989, 37th Edition, Neural Organisation, Inhibitory Circuits, Page 865.

4.The visual system categorises the perceived images in terms of edges, orientation of lines, and even in terms of isolation of moving lines. Human Neuroanatomy, 1975, 6th Edition, Raymond C. Truex and Malcolm B. Carpenter, The Cerebral Cortex, The Primary Visual Area, Page 562-565.

5.The average nerve cell responds within about 5 milliseconds of receiving a message. Gray’s Anatomy, 1989, 37th Edition, Physiological Properties of Neurons, Page 878-879.

6.Current understanding is that there is a step by step conversion of dendritic input impulses into output impulses by a nerve cell. According to this understanding, a neuron has a resting voltage of about 80 mV, inside negative. This resting voltage can change gradually, by “graded potentials” or suddenly, through “action potentials”. Gradual changes occur across membranes of dendrites, and the cell body. Such changes can go up, or down. They can inhibit the cell, or trigger an impulse from it. Action potentials reverse polarity across the membranes of axons. It is an all-or-none response, completed in about 5 milli seconds. Once initiated, the action potential spreads rapidly down the axon. They travel as impulses, maintaining a specific frequency. Gray’s Anatomy, 1989, 37th Edition, Physiological Properties of Neurons, Page 879.

7.”Of the numerous synaptic terminals clustered on dendrites and soma of a multipolar neuron, some are excitatory while those from other sources are inhibitory. Depending on the activity or quiescence of such sources, the ratio of active excitatory and inhibitory synapses continuously varies. Their effects summate……….., an action potential is generated and spreads along the axon as a nerve impulse.” Gray’s Anatomy, 1989, 37th Edition, Neural Organisation, Neurons, Page 864.

8.There are receptors for pressure, touch, pulling and stretching. There’s even one to detect hair movement. Peritrtrichial receptors are cage like formations that surround hair follicles. A single axon receives data from many hair follicles and each follicle reports to two to twenty axons. Some receptor branches encircle the follicle and others run parallel to its long axis. Nociceptors are free nerve endings which convert energy from substances released by damaged cells into pain impulses. The Human Nervous System, 1983, 4th Edition, Murray L. Barr and John A. Kiernan, Introduction and Neurohistology, Peripheral Nervous System, Cutaneous Sensory Endings, Physiological Correlates, Page 37.

9.Careful stimulation of the proper motor areas can invoke flexion or extension at a single finger joint, twitching at the corners of the mouth, elevation of the palate, protrusion of the tongue, and even involuntary cries or exclamations. Human Neuroanatomy, 1975, 6th Edition, Raymond C. Truex and Malcolm B. Carpenter, The Cerebral Cortex, Efferent Cortical Areas, The Primary Motor Area, Page 571.

10.”Everyone knows what attention is. It is the taking possession by the mind, in clear and vivid form, of one out of what seem several simultaneous objects or trains of thought. Focalisation, concentration of consciousness are its essence”. The Principles of Psychology, 1890, William James. Quoted in: In the Theater of Consciousness, 1997, Bernard J. Baars, Page 95.

11.”In landmark work using cognitive and brain imaging techniques, Michael Posner and his coworkers recently discovered a network of brain centres involved in visual and executive attention”. In the Theater of Consciousness, 1997, Bernard J. Baars, Page 100.

12.”Little is known about the physiology of memory storage in the brain. Some researchers suggest that memories are stored at specific sites, and others that memories involve widespread brain regions working together; both processes may be involved”. “Memory,” Microsoft Encarta 97 Encyclopedia.

13.Long-term potentiation (LTP) is “the enduring facilitation of synaptic transmission that occurs following the activation of a synapse by high-frequency stimulation of the presynaptic neuron.” This phenomenon (LTP) has been found to occur in the mammalian hippocampus. Researchers believe that the hippocampus to be one of the major brain regions responsible for processing memories. Pinel, J. (1993). Biopsychology,(2nd Edition) Allyn & Bacon: Toronto.

14.In the early periods of evolution, “Nosebrains” dominated decision making systems of lower vertebrates. The smell of an object decided whether it was edible and could be consumed. If the odour was wrong, it was inedible and had to be avoided. The Human Nervous System 1983, 4th Edition, Murray L. Barr and John A. Kiernan, Introduction and Neurohistology, Telencephalon, Page 8.

15.In the late 1920s, W.B.Cannon published a paper which suggested that emotional behaviour was still present when the viscera was surgically or accidentally isolated from the central nervous system. Different emotions had similar patterns of visceral responses. Perceptions of visceral responses were non-specific. Emotional responses were far quicker than visceral responses. Emotions do not follow artificial stimulation of visceral responses as a matter of course. The Oxford Companion to The Mind, 1987, Richard L.Gregory, Emotion, George Mandler, Pages 219-220.

16.Scar tissue in the cerebral cortex is one of the causes of epilepsy. When operating to remove the scar tissue, the surgeon has to stimulate the brain electrically on the conscious patient to locate the problem area. Excitation of certain parts of the temporal lobe produces intense fear in the patient. Other parts cause feelings of isolation, of loneliness or sometimes of disgust. The Oxford Companion to The Mind, 1987, Richard L.Gregory, Nervous System, P.W.Nathan, Page 527.

17.The septal area has been shown to be a pleasure zone for rats. Experiments were conducted on the animals with electrodes planted in this area where they could self stimulate themselves by pressing on a lever. They were observed to continue until they were exhausted preferring the effect of stimulation to normally pleasurable activities such as consuming food. The Oxford Companion to The Mind, 1987, Richard L.Gregory, Centers in The Brain, O.L.Zangwill, Page 129.

18.The limbic system of the brain contains a ring of interconnected neurons containing over a million fibres connecting the thalamus, the hippocampus, the septal areas and the amygdaloid body. The ring transmits impulses in both directions. In 1937 Papez postulated that these parts of the brain constitute a harmonious mechanism which may elaborate functions of central emotion as well as participate in emotional expression. Bilateral removal of the hippocampal formation and amygdaloid bodies in monkeys is followed by docility and lack of emotional responses such as fear or anger. The Human Nervous System, 1983, 4th Edition, Murray L. Barr and John A. Kiernan, Regional Anatomy of the Central Nervous System, Circuits of the Limbic System, Page 268.

19.Current understanding of medical experts is that the limbic system is believed, to be intimately involved in seeking and capturing prey, courtship, mating, rearing of young, subjective and expressive elements in emotional responses and the balance between aggressive and communal behaviour. Gray’s Anatomy, 1989, 37th Edition, The Limbic Lobe and Olfactory Pathways, Page 1028.

20.”The total number of rods in the human retina has been estimated at 110-125 million and of the cones at 6.3-6.8 million (Osterberg 1935).” Gray’s Anatomy, 1989, 37th Edition, The Visual Apparatus, Page 1197.

21.When mapping activity in the cerebral cortex, the tones heard by the ear were noted to be processed within a region of the cortex called the Heschl gyrus. This auditory area of the brain receives fibres from the medial geniculate nucleus in the thalamus. There is a spatial representation in the auditory area with respect to the pitch of sounds. Tones of different pitch or frequency produce brain signals at measurably different locations within the Heschl gyrus. It was laid out like a piano keyboard. A report by Dr.Christopher Gallen of the Scripps Clinic in La Jolla, California.

22.A study in 1959 by Powell and Mountcastle indicated that a vertical column of cells extending across all cellular layers in the somatic sensory cortex constitutes the elementary functional cortical unit. The columns form a barrel, varying in diameter from 200 microns to 500 microns, with a height equal to the thickness of the cortex. Neurons of one barrel are related to the same receptive field, are activated as a rule by the same peripheral stimulus and all the cells of a vertical column discharge at more or less the same latency following a brief peripheral stimulus. A barrel represents a piece of the cortex activated by a single axon from one of the specific thalamic nuclei. Similar barrels also exist for associate and commisural fibres, which transfer information between different regions of the cortex. Human Neuroanatomy, 1975, 6th Edition, Raymond C. Truex and Malcolm B. Carpenter, The Cerebral Cortex, Sensory Areas of the Cerebral Cortex, Page 555-556. The Human Nervous System, 1983, 4th Edition, Murray L. Barr and John A. Kiernan, Regional Anatomy of the Central Nervous System, Histology of the Cerebral Cortex, Intracortical Circuits, Page 228.

23.In the early forties, Dempsey and Morison reported that repeated electrical stimuli into the “non-specific” nuclei of the thalamus resulted in widespread activity in the outermost cortical layers. The activity appeared to be of a “recruiting” nature. In 1960 Jasper again suggested that the synaptic termination of the fibres of the “non-specific” system in the cortex travels parallel to the surface and is widely distributed in all layers, but the principal functional processes appear to be within the outermost layers. Human Neuroanatomy, 1975, 6th Edition, Raymond C. Truex and Malcolm B. Carpenter, The Cerebral Cortex, Nonspecific Thalamocortical Relationships, Page 582-584.

24.Stephen Kosslyn and Martha Farah have shown extensively that visual imagery elicits activity in the same parts of the cortex as visual perception (Kosslyn, 1980). In the Theater of Consciousness, 1997, Bernard J. Baars, Page 74.

25.Throughout the growth of the nervous system, axons grow from one region to another and “map” on to specific target regions. The Oxford Companion to The Mind, 1987, Richard L.Gregory, Brain Development, Colwyn Trevarthen, Pages 101-110.

26.The information proceeds from primary areas of the cortex to secondary areas which co-ordinate the information from similar sensory receptors in the other half of the body. Neurons in the primary areas connect only to the secondary areas. All secondary areas in both hemispheres of the brain are interconnected. These areas assist binocular vision and stereo-phonic sound. The association areas receive information from every other secondary sensory region. The Human Nervous System, 1983, 4th Edition, Murray L. Barr and John A. Kiernan, Regional Anatomy of the Central Nervous System, Medullary Center, Internal Capsule and Lateral Ventricles, Medullary Center, Page 242.

27. All sensory inputs are first received in the primary somesthetic area. Electrical stimulation of this area gives modified tactile senses, such as tingling, or numb sensations. If this area gets damaged, the related sensory inputs cannot be felt. If the somesthetic area is intact and there is damage in the somesthetic association area, awareness of general senses persists but significance of information with reference to previous experience is elusive. It is impossible to correlate the surface texture, shape, size, and weight of the object or to compare the sensations with previous experience. A patient is unable to identify a common object such as a pair of scissors held in the hand while his eyes are closed. The Human Nervous System, 1983, 4th Edition, Murray L. Barr and John A. Kiernan, Regional Anatomy of the Central Nervous System, Functional Localisation in the Cerebral Cortex, The Somesthetic Association Cortex, Page 232-233.

28.Each of the 30,000 motor neurons, which control motor activity, receives approximately 20,000 synaptic contacts. The greatest number are from interneurons in the spinal tract. They run up and down the spinal pathway and synapse with the motor neurons. The Human Nervous System, 1983, 4th Edition, Murray L. Barr and John A. Kiernan, Regional Anatomy of the Central Nervous System, Spinal Cord, Ventral Horn, Page 71.

29.Situated in the brain stem, the reticular formation is an early predecessor to the brain. The reticular formation is the recipient of data from most of the sensory systems. While damage to most other regions of the brain cause only selective defects, serious damage to the reticular formation results in prolonged coma. Cutaneous and olfactory stimuli to the reticular formation appear to be especially important in maintaining consciousness. The latter stimuli may be the reason for the success of smelling salts in restoring a person from a fainting fit. Experimental results show that electrical stimulation of the reticular formation can also induce sleep in animals. While there are processes in the reticular formation which raise the level of consciousness and alertness, there may be a co-existing process that induces sleep. The Human Nervous System, 1983, 4th Edition, Murray L. Barr and John A. Kiernan, Regional Anatomy of the Central Nervous System, Reticular Formation, Page 145, 152.

30.Medical research confirms that the cerebellum is “necessary for smooth, co-ordinated, effective movement”. Gray’s Anatomy, 1989, 37th Edition, Cerebellar Dysfunction, Page 978.

31.Terminations of movements are affected by damage to the cerebellum. For a normal person, when the elbow is made to flex against resistance and the arm is released suddenly, contraction of opposing muscle fibres prevents overflexion. In cerebellar disease, flexion is uncontrolled and the patient may hit himself in the face or chest. This is called the “rebound phenomenon”. With cerebellar problems, the patient converts a movement which requires simultaneous actions at several joints into a series of movements, each involving a single joint. When asked to touch his nose, with a finger raised above his head, the patient will first lower his arm and then flex the elbow to reach his nose. This problem is called “decomposition of movement”. Human Neuroanatomy, 1975, 6th Edition, Raymond C. Truex and Malcolm B. Carpenter, The Cerebellum – Functional Considerations, Page 434.

32.Each half of the body is represented in the cerebellar cortex. The cerebellum has an arrangement that represents all motor control functions spread over its cortical layer, with topographic precision. Researchers have mapped out localised areas on the cerebellar cortex for the control of leg, arm and facial movements which they found were identical with tactile receiving areas. Motor and sensory functions were integrated in the cerebellum. Human Neuroanatomy, 1975, 6th Edition, Raymond C. Truex and Malcolm B. Carpenter, The Cerebellum – Functional Considerations, Page 439.

33.The only fibres leaving the cerebellar cortex are the axons of a specialised group of neurons called the Purkinje cells. The Human Nervous System, 1983, 4th Edition, Murray L. Barr and John A. Kiernan, Regional Anatomy of the Central Nervous System – Cerebellum, Gross Anatomy, Cerebellar Cortex, Cortical Layers, Page 159.

34.In 1967, V.Braitenberg suggested the possibility of control of sequential events by the cerebellum. These neural relationships appear to create, in the cerebellum, an accurate biological clock. Impulses in fibres which link successive Purkinje cells, reach the cell dendrites at intervals of about a one ten thousandths of a second. Alternate parallel rows of Purkinje cells are excited, while the in-between rows are inhibited. Gray’s Anatomy, 1989, 37th Edition, Mechanisms of the Cerebellar Cortex, Page 974.

35.The inferior olivary complex is the source of climbing fibres to all regions of the cerebellar cortex. In 1940 Brodal noted that in young cats and rabbits, all regions of the cerebellar cortex receive exquisitely marked out projections from the olivary nucleus. Destruction of this olivary neuron branch to the cerebellar cortex results in severe loss of co-ordination of all movements. Such damage appears to cause problems very similar to those caused by damage to the cerebellum, even though this bundle of nerves is only one of the many nerve tracts connecting the cerebellum. Human Neuroanatomy, 1975, 6th Edition, Raymond C. Truex and Malcolm B. Carpenter, The Cerebellum, Olivocerebellar Fibers, Page 422.

36.Sensory events occurring within a tenth of a second merge into a single conscious sensory experience, suggesting a 100-millisecond scale. But working memory, the domain in which we talk to ourselves or use our visual imagination, stretches over roughly 10 second steps. In the Theater of Consciousness, 1997, Bernard J. Baars, Page 48.

37.Mozart, Wolfgang Amadeus. (Based on his quotation in Hadamard 1945, Page 16). Taken from The Emperor’s New Mind, 1989, Roger Penrose, Page 547.

38.The Principles of Psychology, 1890, William James. Quoted in: In the Theater of Consciousness, 1997, Bernard J. Baars, Page 130.

39.Homeostasis is the naturally maintained, relatively constant state within the body, maintained in a changeable environment. It is brought about by various sensing, feedback and control systems, supervised by a hierarchy of control centres. The frontal cortex, limbic system, hypothalamus, reticular formation and spinal cord constitute some of the components of this hierarchy. The concept that these centres mediate these controls is based on a wide base of experimental evidence gathered by studying the impact of destruction of localised topographical targets in animals. As higher levels are included with the spinal cord below the cut off section, more effective controls are retained. With transection below the hypothalamus, minor reflex adjustments of cardiovascular, respiratory and alimentary systems survive, but are not integrated and normal temperature is not maintained. With transection above the hypothalamus, separating it from the limbic system, effective controls are maintained within a moderate range of conditions. Innate drives and motivated behaviour are preserved, including feeding, drinking, apparent satiation, and copulatory responses. But such controls fail if environmental stresses exceed a certain range e.g., persistently high or low temperatures. Animals may attack, try to eat, drink or copulate with inappropriate objects. But if the connections between the limbic system and the hypothalamus survive and only the frontal cortex is cut off, normal homeostasis is preserved even in a wide range of adverse conditions. Gray’s Anatomy, 1989, 37th Edition, Functions of the Hypothalamus, Page 1011.

40.The prefrontal area forms a part of the frontal lobe of the cortex including much of the frontal gyri, orbital gyri, most of the medial frontal gyrus and the anterior part of the cingulate gyrus. While all other regions of the cortex communicate mostly within finite regions, the prefrontal lobe has abundant connections with the association cortex of the three sensory lobes. Human Neuroanatomy, 1975, 6th Edition, Raymond C. Truex and Malcolm B. Carpenter, The Cerebral Cortex, Prefrontal Cortex, Page 587.

41.Medical evidence suggests that patients with extensive frontal lobe damage show disregard for the general tenets of behaviour and a marked lack of concentration. Some years ago, a procedure called prefrontal lobotomy, or leucotomy was widely used, either for patients with intractable pain or in attempts to modify the behaviour of severely psychotic patients. The basic operation disconnected the prefrontal area from the lower regions by cutting its nerve fibre connections. Many institutionalised patients were able to return home and even to resume their former activities. The results of these operations were evaluated in a number of publications. While there was abolition of morbid anxiety and obsessional states, Freeman and Watts noted a lessening of the consciousness of self. The patients were “tactless and unconcerned in social relationships, with an inability to maintain a responsible attitude”. Human Neuroanatomy, 1975, 6th Edition, Raymond C. Truex and Malcolm B. Carpenter, The Cerebral Cortex, Prefrontal Cortex, Page 588.

42.Lorenz, Konrad, 1972. As quoted in The Emperor’s New Mind, 1989, Roger Penrose, Page 551.