The brain-mind-computer trichotomy: from …geza.kzoo.edu/~erdi/leckek/cogsima1.pdfThe brain-mind-computer trichotomy: from cybernetics via philosophy of mind to cognitive science

The brain-mind-computer trichotomy: fromcybernetics via philosophy of mind to

cognitive science

Peter [email protected]

Henry R. Luce Professor

Center for Complex Systems Studies, Kalamazoo College

http://people.kzoo.edu/ perdi/

Institue for Particle and Nuclear Physics, Wigner Research Centre, Hungarian Academy of Sciences,

Budapest

http://cneuro.rmki.kfki.hu/

Budapest Semester in Cognitive Science

Systems Neuroscience: a study abroad summer program

http://www.bscs-us.org/

http://sysneuro-semester.org/

Situation awareness

Figure 1: Endsley 1995 basic model

– Typeset by FoilTEX – 2

Mental Models

Figure 2: Two people can see the same situation and interpret it very differently basedon experience and beliefs. ”Snow is fun vs. snow is horrible!”


The brain-mind-computer trichotomy

brainmind

computer

The brain – mind problem

moisnm dualism+++

theory of mindbrain-computer

analogy/disanalogy

Hermeneutic circle

Computational

Classical cogntive science


The Brain-Mind Problem

• monism vs. dualism

• reductionism

• emergentism

• functionalism

• downward causation

Monism:is the theory that there isonly one

fundamental kind, categoryof thing or principle.

Dualism:is the theory that the men-tal and the physical or mindand body or mind and brainare, in some sense, radicallydifferent kinds of thing.(Interactionist dualism fromDescartes to Popper andEccles)




Reductionism

• Philosophy of science. Success sto-

ries of the twentieth century science:

Quantum Physics and Molecular Biol-

ogy

• Philosophical position: a complex

system is nothing els but the sum of

its parts

• Methodological reductionism: a prob-

lem (the object of explaining some-

thing) is split up into separate parts

or aspects and thus reduced to simpler

components

• Epistemological reductionism: higher

level phenomena can be explained by

processes at a lower level

• Ontological reductionism: reality is

composed of a minimum number of

kinds of entities or substances.


The Brain-Mind Problem: the philosophical approach

Emergentism: The properties of complex systems are not reducible to those of their

constituent elements, though they could not exist without them. While many of the fundamental

properties of matter, such as mass, are held to be merely quantitative and additive, emergent

properties are said to be qualitative and novel or non-predictable.

Functionalism is the doctrine that what makes something a thought, desire, pain (or any

other type of mental state) depends not on its internal constitution, but solely on its function,

or the role it plays, in the cognitive system of which it is a part. More precisely, functionalist

theories take the identity of a mental state to be determined by its causal relations to sensory

stimulations, other mental states, and behavior.



• higher level systems influence lower level con-

figuration

• the whole is to some degree constrained by the

parts (upward causation), but at the same time

the parts are to some degree constrained by the

whole (downward causation).

CIRCULAR CAUSALITY


”Downward Causation?

There is a feedback from the ”effect” to the ”cause”

CYBERNETICS:

a great tradition

(i.e. a causal chain from emergent mental phenomena downward upon the physiological functions of

neural structures)

• it was cautiously suggested that

• the nervous system can be considered as being open to various kinds of information

• there would be no valid scientific reason to deny the existence of downward causation,

• or more precisely, a two-way causal relationship between brain and mind



Cybernetics, AI, Cognitive Science andComputational Neuroscience: Historical Aspects

1. Cybernetics

2. Artificial Intelligence: The physical symbol system hypothesis

3. Connectionism: From Perceptron to the Artificial Neural Network Movement

4. Cognitive Science

5. Situated and Embodied Cognition

6. Computational Neuroscience


Cybernetics

The Founding Fathers: Warren McCulloch and Norbert Wiener

• logic-based physiological theoryof knowledge

• the brain performs logical think-ing ...

• ... which is described by logic

• therefore ...the operation of thebrain could and should be de-scribed by logic


Cybernetics

The McCulloch-Pitts model

The MCP networks are composed by multi-input (xi, i = 1, . . . , n) single output (y)threshold elements. The state of one element (neuron) of a network is determined by thefollowing rule: y = 1, if the weighted sum of the inputs is larger than a threshold, andy = 0, in any other case:

y =

{1, if

∑iwixi > Θ

0, otherwise.(1)

Such a rule describes the operation of all neurons of the network. The state of the network is

characterized at a fixed time point by a series of zeros and ones, i.e. by a binary vector, where the

dimension of the vector is equal with the number of neurons of the network. The updating rule

contains an arbitrary factor: during one time step either the state of one single neuron or of the all

neurons can be modified. The former materialize asynchronous or serial, the latter synchronous

or parallel processing.


Cybernetics

The McCulloch-Pitts model

• introduced a formalism whoserefinement and generalizationled to the notion of finite au-tomata (an important concept incomputability theory)

• is a technique that inspired thenotion of logic design of comput-ers

• was used to build neural networkmodels to connect neural struc-tures and functions by dynamicmodels

• offered the first modern com-putational theory of brain andmind.


Cybernetics


• “Behavior, Purpose and Teleol-ogy”

• Feedback control

• “Control and Communication inthe Animal and the Machine”

• Voluntary nervous system maycontrol the environment

• Theory of goal-oriented behav-ior: a new framework to under-stand the behavior of animals,humans, and computers


Cybernetics


The Macy conferences (1946 - 1953)

• Applicability of a Logic Machine Model to both Brain and Computer

• Analogies between Organisms and Machines

• Information Theory

• Neuroses and Pathology of Mental Life

• Human and Social Communication


Cybernetics

John von Neumann: The Real Hero of Interdisciplinarity

• Set Theory

• Quantum Mechanics

• Game Theory

• Computer Architectures

• The Computer and the Brain


John von Neumann: Set Theory

• Russel - Whitehead: the crisis of mathematics

• Hilbert’s program: to formalize mathematics (the axiomatization of set theory)

• von Neumann-Bernays-Godel set theory: (has only finitely many axioms)

• abandoned after Godel’s theorems (1931)”no formal system powerful enough to formulate arithmetic could be both completeand consistent”


Von Neumann architecture


Von Neumann architecture

The IAS Computer was named for the Institute for

Advanced Study, Princeton. The machine was built

there under the direction of John von Neumann. It

cost several hundred thousand dollars. The goal of

developing the IAS was to make digital computer

designs more practical and efficient. The project to

build it began in 1946 and the computer was ready

for use in 1952.

Designers of the IAS were required to make their

plans available to several other government-funded

projects, and several other machines were built along

similar lines: JOHNNIAC, MANIAC, ORDVAC, and

ILLIAC. -> also IBM 701 (from the website of the

Smithonian Museum)

• has three basic hardware subsystems: a CPU, a main-memory system and an I/O system

• is a stored-program computer

• carries out instructions sequentially

• has, or at least appears to have, a single path between the main memory system and the control unit

of the CPU; this is often referred to as the von Neumann bottleneck


The Computer and the Brain


The Computer and the Brain in broader context

• McCulloch-Pitts and the Cybernetic movement

• Self-replicating automaton: the machine and its description

• Reliable calculation with unreliable elements

• Analog vs. digital machines

• Specialized memory unit

• Language of the brain: ”Thus the outward forms of our mathematics are not absolutelyrelevant from the point of view of evaluating what the mathematical or logical languagetruly used by the central nervous system is”


From Cybernetics to AI and back?

• August 1955: coincidence:

• von Neumann was diagnosedwith cancer

• A proposal for the Dartmouthsummer research project on Ar-tificial Intelligence

• mechanism vs. function

• zeros and ones vs. general sym-bol manipulation

• Newell, A., and H. A. Simon.1956. The logic theory machine:A complex information process-ing system. IRE Trans. Inf.Theory IT-2:61-79


From Cybernetics to AI and back?

While it seemed to be an analogybetween Brain and Computer

+ at the elementary hardware level

+ at the level of mathematical(quasi)-equivalence

the Organization Principles arevery different

The importance of the actualbiological substrate:

Synaptic organization !!

”...Eccles has shown how excita-tion and inhibition are expressed bychanges of membrane potential..”


Cybernetics

Second-order cybernetics

• autonomous system, role of ob-server, self-referential systems

• Heinz von Foerster (1911–2002)

• radical constructivism

• knowledge about the externalworld is obtained by preparingmodels on it

It is difficult to reconstruct the story, but it might be true that a set of cyberneticians,who felt the irreducible complexity of the system-observer interactions, abandoned tobuild and test formal models, and used a verbal language using metaphors. They werethe subjects of well-founded critics for not studying specific phenomena (Heylighen andJoslyn 2001)


Cybernetics

The dissolution/dissemination of cybernetic ideas

At least four disciplines have crystallized from cybernetics

• Biological control theory

• Neural modeling

• Artificial Intelligence

• Cognitive psychology

Preliminary crystal seeds:

Rashevsky’s continuous neural models (and mathematical biophysics)

Turing’s paper on computing machinery and intelligence (Turing 1950)


BioSystems 88 (2007) 178–184

The mathematical biophysics of Nicolas Rashevsky

Paul CullComputer Science Department, Oregon State University, Corvallis, OR 97331, USA

Abstract

N. Rashevsky (1899–1972) was one of the pioneers in the application of mathematics to biology. With the slogan:

mathematical biophysics : biology :: mathematical physics : physics,

he proposed the creation of a quantitative theoretical biology. Here, we will give a brief biography, and consider Rashevsky’scontributions to mathematical biology including neural nets and relational biology. We conclude that Rashevsky was an importantfigure in the introduction of quantitative models and methods into biology.© 2006 Elsevier Ireland Ltd. All rights reserved.

Keywords: Rashevsky; Mathematical biophysics; Mathematical biology; History; Neural nets; Relational biology

1. Introduction

Who was Nicolas Rashevsky? When I recently askeda few people if they knew Rashevsky, I got some funnyreplies:

“Oh, you mean Nikolai Lobachevsky (1792–1856),the Russian who invented non-Euclidean geometryand the one Tom Lehrer wrote the song about?”

“Do you mean Samuel Reshevsky (1911–1992), thechess prodigy and grandmaster?”

The answer is, of course, “No, I mean Nicolas Rashevskywho created mathematical biophysics” (see Fig. 1). Fiftyyears ago, Rashevsky was quite well known, but he is lessknown today. I did a quick Google search to see currentinterest in Rashevsky. I found a number of referencesto his work on neural nets and on relational biology. Inthis short note, I will briefly outline Rashevsky’s life andwork, and hopefully explain why he is still an importantbut controversial figure.

E-mail address: [email protected].

2. Brief biography

Nicolas Rashevsky was born in Chernigov, Russia onSeptember 1899. He studied at the University of Kiev,completing his graduate work in physics in 1919. Hetaught physics at the University of Kiev and at RobertCollege in Constantinople. In 1921, he became Profes-sor of Physics at the Russian University at Prague. In1924, he came to the United States and joined the West-inghouse Research Laboratories. In 1934, he came to theUniversity of Chicago as a Rockfeller Fellow in Mathe-matical Biophysics. From 1935 to 1940, he worked in thedepartments of Psychology and Physiology. In 1939, hestarted and was the first editor of the Bulletin of Math-ematical Biophysics. In 1940, he founded the Sectionof Mathematical Biophysics within the Department ofPhysiology. In 1947, he founded and became Profes-sor and Chairman of the Committee on MathematicalBiology. He continued in this position through 1964. In1965, he became Professor of Mathematical Biology inthe Mental Health Research Institute at the University ofMichigan in Ann Arbor. He died in Holland, Michiganin 1972.

0303-2647/$ – see front matter © 2006 Elsevier Ireland Ltd. All rights reserved.doi:10.1016/j.biosystems.2006.11.003


A. M. Turing (1950) Computing Machinery and Intelligence. Mind 49: 433-460.

COMPUTING MACHINERY AND INTELLIGENCE

By A. M. Turing

1. The Imitation Game

I propose to consider the question, "Can machines think?" This should begin with definitions of the meaning of the terms "machine" and "think." The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous, If the meaning of the words "machine" and "think" are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to the question, "Can machines think?" is to be sought in a statistical survey such as a Gallup poll. But this is absurd. Instead of attempting such a definition I shall replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.

The new form of the problem can be described in terms of a game which we call the 'imitation game." It is played with three people, a man (A), a woman (B), and an interrogator (C) who may be of either sex. The interrogator stays in a room apart front the other two. The object of the game for the interrogator is to determine which of the other two is the man and which is the woman. He knows them by labels X and Y, and at the end of the game he says either "X is A and Y is B" or "X is B and Y is A." The interrogator is allowed to put questions to A and B thus:

C: Will X please tell me the length of his or her hair?

Now suppose X is actually A, then A must answer. It is A's object in the game to try and cause C to make the wrong identification. His answer might therefore be:

"My hair is shingled, and the longest strands are about nine inches long."

In order that tones of voice may not help the interrogator the answers should be written, or better still, typewritten. The ideal arrangement is to have a teleprinter communicating between the two rooms. Alternatively the question and answers can be repeated by an intermediary. The object of the game for the third player (B) is to help the interrogator. The best strategy for her is probably to give truthful answers. She can add such things as "I am the woman, don't listen to him!" to her answers, but it will avail nothing as the man can make similar remarks.

We now ask the question, "What will happen when a machine takes the part of A in this game?" Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman? These questions replace our original, "Can machines think?" – Typeset by FoilTEX – 29

The cybernetics approach to command and control:Boyd’s OODA loop


Artificial Intelligence: The physical symbol systemhypothesis (Alan Newell and Herbert Simon)

A physical symbol system has the necessary and sufficient means for intelli-gent action

• Necessity: Anything capable of intelligent action is a physical symbol system

• Sufficiency: Any (sufficiently sophisticated) PSS is capable of intelligent action

Four basic ideas

• Symbols are physical patterns

• Symbols can be combined to form complex symbol structures

• The system contains processes for manipulating complex symbol structures

• The processes for representing complex symbol structures can themselves by symbol-ically represented within the system


Artificial Intelligence: The physical symbol systemhypothesis (Alan Newell and Herbert Simon)

• Thinking and the PSSS

• Problem solving: heuristic search

Problems are solved by generating and modifying symbol structures until a solutionstructure is reached

• GPS starts with symbolic descriptions of the start state and the goal state

• aims to find a sequence of admissible transformations that will transform the startstate into the goal state


Artificial Intelligence

It has produced significant results.

However, it did did not fulfill the BIG promise to creat intelligent devices thatparallel human intelligence


Connectionism: From Perceptron to the ArtificialNeural Network Movement

Perceptron

• neural network: activity dynamics +LEARNING rule (Hebb and its varia-tions)

• delta rule: difference between the actualand the expected output

• adjusting threshold and weights

• linearly separable functions

• Minsky - Papert

• multilayer perceptron: backpropagation


Connectionism: From Perceptron to the ArtificialNeural Network Movement

Deep learning: (probaly not) the new siver bullet

Deep Learning in Neural Networks: An Overview - J .Schmidhuber

Further studies, say:Y. Bengio: Fundamentals of Deep Learning of Representations http://people.idsia.ch/~juergen/deep-learning-overview.html

speech recognitionobject recognition

http://www.cs.tau.ac.il/~wolf/deeplearningmeeting/pdfs/Tel-Aviv-7nov2014.

pdf

There is no single machine learning model that is the ”silver bullet” to solve all yourproblems.


Cognitive science is the interdisciplinary study ofmind and intelligence

• philosophy

• psychology

• neuroscience

• artificial intelligence

• linguistics

• anthropology

Cognitive science is the interdisciplinary scien-tific study of the mind and its processes. Itexamines what cognition is, what it does andhow it works. It includes research on intel-ligence and behavior, especially focusing onhow information is represented, processed,and transformed (in faculties such as per-ception, language, memory, reasoning, andemotion) within nervous systems (humans orother animals) and machines (e.g. comput-ers). Cognitive science consists of multiple re-search disciplines, including psychology, artifi-cial intelligence, philosophy, neuroscience, lin-guistics, and anthropology. It spans many lev-els of analysis, from low-level learning and de-cision mechanisms to high-level logic and plan-ning; from neural circuitry to modular brain or-ganization. The fundamental concept of cog-nitive science is that ”thinking can best beunderstood in terms of representational struc-tures in the mind and computational proce-dures that operate on those structures.”


History of cognitive science: preliminary remarks

Philosophy: Fundamental questions since Aristotle andPlato:1. What is the nature of mind? Metaphysics and nature ofreality2. What is the nature of knowledge? Epistemology andnature of knowledgePsychology:, 1890s. Behaviorism: can’t study what is inthe mind(from ”philosophical psychology” towards ”experimentalpsychology”)1950’s. Miller, etc.: mind has structure3. How do we think?Neuroscience:4. How does the brain make a mind?Artificial intelligence: 1956. Minsky, Newell, Simon, Mc-Carthy5. How to construct mind?Linguistics :1956. Chomsky versus behaviorist view of lan-guage.6. Language acquisition and evolution. Innateness?Anthropology: social, cultural aspects of knowledge7. Is there any cultural difference in the thinking of peo-ple?


Cognitive science is the interdisciplinary study ofmind and intelligence

The need of INTEGRA-TION:How can all these fields,with different histories andmethodologies can be inte-grated to produce an under-standing of mind?


Key concepts

Mental representation

Computational procedures

Thinking = Mental representations + com-putational proceduresmore precisely:

Thinking = representational structures + proce-

dures that operate on those structures.

Analogy between computation and thinking:

data structures mental representations

+ algorithms + procedures

= running programs = thinking

Methodological consequence: study the mind by

developing computer simulations of thinking


Situated and Embodied Cognition

• brain - body- environment

• ”knowing by doing”

• action - perception cycle

• intentional dynamics

Figure 3: Walter Freeman(1927-2016)


Situated and Embodied Cognition

Cognitive robots: movements arenot based on internal representa-tions, rather, they are based on therobot’s dynamic interaction withits environment

The role of dynamical systemsapproach and neural mech-anisms in building ”consciousrobots”.

Figure 4: Jun Tani: ExploringRobotic Minds. Actions, Symbols,and Consciousness as Self-organiz-ing Dynamic Phenomena. OUP2016


Cognitive Systems Research


Back to Neurobiology: Computational Neuroscience

Top down

• starts from behavioral data

• information processing circuit

• implementation of neural mechanisms

Bottom up

• starts from anatomical and physiologicalreality

• rhythms

• behavior

Figure 5: Brain as a multi-level system. Neural mechanisms→ computational algorithms.


Documents

The brain-mind-computer trichotomy: from …geza.kzoo.edu/~erdi/leckek/cogsima1.pdfThe brain-mind-computer trichotomy: from cybernetics via philosophy of mind to cognitive science