The Intuitive Algorithm
recognition mind disease search memory system diseases process symptom
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An Essay Concerning Artificial Intelligence, Intuition and The Mind
Intuition may be a pattern recognition algorithm
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.
" .............. The concept of an intuitive algorithm may provide us a key to the mechanisms and working of the human brain and the concept of "MIND". Dr.K.Jagannathan, MD DTM FAMS, Consultant Neurologist.
"........... The tenet of The Intuitive Algorithm raises innovative and interesting questions on the very basis of intuitive thinking." Dr.Prithika Chary MD DM(Neuro) PhD (Neuro) MNAMS (Neuro) MCh (Neurosurgey) Neurologist & Neurosurgeon. Recipient - Indian Council of Medical Research Award for Outstanding Woman Scientist of The Year 1982.
"............ A highly commendable intellectual endeavor, which can provide leads to researchers in artificial intelligence, cognitive sciences and advanced computer systems". Dr.K.Sundaram, PhD., Head of the Department of Computer Science, University of Madras, Principal Contributions in Bio-Physics and Computer Science at the University, at the All India Institute of Medical Sciences, New Delhi and at NASA, U.S.A.
Barriers to understanding the mind. How does the mind internally represent information? How does it instantly isolate a single pattern from a mass of interweaving 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, traveling 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 utitlised 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, up to 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.
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, dispatched 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 dispatching 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 global 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 relations