Platonic model of mind as an approximation to neurodynamics

Duch, Wlodzislaw (1998) Platonic model of mind as an approximation to neurodynamics. [Book Chapter]

Full text available as:



Hierarchy of approximations involved in simplification of microscopic theories, from sub-cellural to the whole brain level, is presented. A new approximation to neural dynamics is described, leading to a Platonic-like model of mind based on psychological spaces. Objects and events in these spaces correspond to quasi-stable states of brain dynamics and may be interpreted from psychological point of view. Platonic model bridges the gap between neurosciences and psychological sciences. Static and dynamic versions of this model are outlined and Feature Space Mapping, a neurofuzzy realization of the static version of Platonic model, described. Categorization experiments with human subjects are analyzed from the neurodynamical and Platonic model points of view.

Item Type:Book Chapter
Keywords:Neurodynamics, mind models, neural networks, neurofuzzy systems, symbolic dynamics, categorization, cognitive neuroscience, cognitive psychology.
Subjects:Neuroscience > Computational Neuroscience
Computer Science > Artificial Intelligence
Computer Science > Neural Nets
Neuroscience > Neuropsychology
ID Code:913
Deposited By:Duch, Prof Wlodzislaw
Deposited On:10 Aug 2000
Last Modified:11 Mar 2011 08:54

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

\bibitem{newell} Newell A, Simon H. A. (1976) {\em Computer Science as

empirical inquiry: symbols and search}. Communic. of the ACM 19: 113-126;

\bibitem{unified} Newell A, {\em Unified theories of cognition.} (Harvard

Univ. Press, Cambridge, MA 1990)

\bibitem{mindproblems} Harnad, S. (1990) {\em The symbol grounding

problem.} Physica D 42: 335-346; Harnad, S. (1993) {\em Problems,

problems: the frame problem as a symptom of the symbol grounding problem.}

PSYCOLOQUY 4 (34) frame-problem.11; Rakover, S.S. (1993). {\em Precise of

Metapsychology: Missing Links in Behavior, Mind, and Science}. PSYCOLOQUY

4(55) metapsychology.1.rakover.

\bibitem{compbrain} P.S. Churchland, T.J. Sejnowski, {\em The

computational brain} (MIT, Bradford Book 1992)

\bibitem{rashevsky} N. Rashevsky, {\em Mathematical Biophysics }(Dover,

NY 1960)

\bibitem{cogneuro} M. S. Gazzaniga, ed. {\em The Cognitive Neurosciences} (MIT, Bradford Book 1995)

\bibitem{developm} E. Thelen, L.B. Smith, {\em A Dynamic Systems Approach

to the Development of Cognition and Action} (MIT Press 1994)

\bibitem{primas} Primas H (1981) {\em Chemistry, quantum mechanics and

reductionism} (Springer Verlag, Berlin)

\bibitem{andersonjr93} Anderson JR\ (1993) {\em Rules of the Mind }%

(Lawrence Erlbaum Associates)

\bibitem{andersonjr95} Anderson JR (1995){\em \ Learning and Memory }(J.

Wiley and Sons, NY)

\bibitem{oscar} Pollock J.L, {\em Cognitive Carpentery. A\ Blueprint for

how to build a person}. (Bradford Book, 1995)

\bibitem{callatay} de Callata\"{y} AM (1992) {\em Natural and artificial

intelligence. Misconceptions about brains and neural networks. }North


\bibitem{burnod} Burnod Y, {\it An Adaptive Neural Network. The Cerebral

Cortex}, London: Prentice-Hall, 1990

\bibitem{Cog} Brooks, Rodney A., Lynn Andrea Stein. Building Brains for

Bodies (MIT AI Lab Memo 1439), August 1993.

\bibitem{levine} Levine DS (1991) {\em Introduction to neural and

cognitive modeling} (L. Erlbaum, London)

\bibitem{caianiello} Caianiello E.R., {\em Outline of a theory of thought

processes and thinking machines. }Journal of Theor. Biology 2 (1961)

204-235; E.R. Caianiello, {\em A theory of neural networks.} In: Neural

Computing Architectures, ed. I. Aleksander (MIT Press, MA 1989)

\bibitem{CALM} Murre J., {\em CALM, Categorization and Learning Modules}

(Erlbaum 1992)

\bibitem{amit} Amit D.J, Fusi S, Yakovlev V, Paradigmatic working memory (attractor) cell in IT cortex, Neuural Computations 9 (1997) 1101; Amit D. J, Brunel N, Tsodyks M.V, {\em Correlations of cortical Hebbian reverberations: experiment versus theory}, J. Neuroscience, 14 (1994) 6445; D.J. Amit, {\em Modeling brain function. The world of attractor neural networks} (Cambridge Univ. Press 1989)

\bibitem{miyashita} Miyashita Y (1990) {\em Associative representation of

the visual long-term memory in neurons of the primate temporal cortex},

in: Iwai E and Mishkin M, eds, {\em Vision, memory and the temporal lobe}

(Elsevier, New York), pp. 75-87

\bibitem{griniasty} Griniasty M., M. Tsodyks, D. Amit (1993) {\em %

Conversion of temporal correlations between stimuli to spatial

correlations between attractors}. Neural Comput. {\bf 5} 1-17

\bibitem{visualcortex} Erwin E., K. Obermayer, K. Schulten, {\em Models

of Orientation and Ocular Dominance Columns in the Visual Cortex: A

Critical Comparison}. Neural Computation {\bf 7} (1995) 425-468

\bibitem{ingber95} Ingber L, {\em Statistical mechanics of multiple

scales of neocortical interactions. }in:\ Neocortical dynamics and Human

EEG\ Rhythms, ed. Nunez PL (Oxford University Press 1995), p. 628-681; Ingber L, {\em Generic mesoscopic neural networks based on statistical

mechanics of neocortical interactions. }Phys. Rev. A {\bf 45} (1992) R2183-2186

\bibitem{mindspace} Duch W, A solution to the fundamental problems of cognitive sciences, UMK - KMK - TR 1/94 report (1994), available from\\ and from the International Philosophical Preprints Exchange.

\bibitem{kelso95} Kelso J.A.S, {\em Dynamic Patterns}, Bradford Book,

MIT Press 1995

\bibitem{cogsci} Stillings N.A., Feinstein M.H, Garfield J.L, Rissland

E.L, Rosenbaum D.A, Wiesler S.E, Baker-Ward L. Cognitive Science: An

Introduction. (MIT Press 1987)

\bibitem{black} I. Black, {\em Information in the Brain A Molecular

Perspective}, A Bradford Book 1994.

\bibitem{dennett} D.C. Dennett, Consciousness explained (Little Brown,

Boston 1991)

\bibitem{penrose} Penrose R, {\em The Emperor's new mind }(Oxford Univ.

Press 1989); {\em In the Shadow of the Mind }(Oxford Univ. Press 1994)

\bibitem{stapp} Stapp H.P (1993) {\em Mind, matter and quantum mechanics}

(Springer Verlag, Heidelberg)

\bibitem{eccles} Eccles J.C. (1994) {\em How the self controls its brain}

(Springer Verlag, Berlin)

\bibitem{genesis} J. M. Bower, D. Beeman, {\em The Book of GENESIS:

Exploring Realistic Neural Models with the GEneral NEural SImulation

System} (Springer 1994); see also


Montague P.R, Dayan P, Sejnowski T.J, Volume learning: signaling covariance through neural tissue, in: Eeckman F.H, Bower J.M (Eds.), Computation and neural systems. Kluver 1993, pp. 377-381;


Krekelberg B, Taylor J.G, {\em Nitric Oxide in Cortical Map

Formation} Journal of Chemical Neuroanatomy, 10 (1996) 191-196

\bibitem{stevens} C. Stevens, {\em Neurophysiology: a Primer}. New York,

Wiley 1996

\bibitem{anderson} J.A. Anderson, {\em An Introduction to Neural

Networks}, A Bradford Book 1995

\bibitem{gamma} Whittington M.A, Traub R.D, Jefferys J.G.R, Synchronized

oscillations in interneuron networks driven by metabotropic glutamate

receptor activation. Nature 373 (1995) 612-615

\bibitem{ratgamma} Traub R.D, Whittington M.A, Colling S.B, Buzsaki G,

Jefferys J.G.R, Analysis of gamma rhythms in the rat hippocampus in vitro

and in vivo. Journal of Physiology 493 (1996) 471-484

\bibitem{rolls94} Rolls E.T, Brain mechanisms for invariant visual

recognition and learning, Behavioral Processes 33 (1994) 113-138

\bibitem{maass} Maass W, Fast sigmoidal networks via spiking neurons,

Neural Computation 9 (1997) 279-304.

\bibitem{nonmonotonic} Yanai Hiro-Fumi, Amari Shun-ichi, Auto-associative

memory with two-stage dynamics of non-monotonic neurons, IEEE Transactions

on Neural Networks, vol. 7, pp. 803-815

\bibitem{hebb} Hebb D, {\em The Organization of Behavior} (J. Wiley, NY


\bibitem{module} Szentagothai, J. (1975). {\em The 'module-concept' in the cerebral cortex architecture.} Brain Research, 95, 475-496.

\bibitem{calvin} Calvin W.H, {\em Cortical columns, modules and Hebbian

cell assemblies}, in: M. A. Arbib, Editor, {\em The Handbook of Brain

Theory and Neural Networks }(MIT Press 1995), pp. 269-272

\bibitem{singer} Singer W, {\em Synchronization of neuronal responses as

a putative binding mechanism}, in: M. A. Arbib, Editor, {\em The Handbook

of Brain Theory and Neural Networks} (MIT Press 1995), pp. 960-964

\bibitem{abeles} Abeles M, Corticotronics (New York, Cambridge University Press 1991)

\bibitem{lateral} Sirosh, J., Miikkulainen, R., and Choe, Y., editors,

{\em Lateral Interactions in the Cortex: Structure and Function.} The UTCS

Neural Networks Research Group, Austin, TX, 1996. Electronic book,

\bibitem{engel} Engel A.K., P. K\"{o}nig, A.K. Kreiter, T.B. Schillen, W.

Singer (1992) {\em Temporal coding in the neocortex: new vistas on

integration in the nervous system. }Trends in Neurosc. {\bf 15:} 218-226

\bibitem{binding} Traub R.D, Whittington M.A, Stanford I.M, Jefferys

J.G.R, A mechanism for generation of long-range synchronous fast

oscillations in the cortex. Nature 382 (1996) 621-624; Jefferys J.G.R,

Traub R.D, Whittington M.A, Neuronal networks for induced ``40 Hz''

rhythms. Trends in Neurosciences 19 (1996) 202-208

\bibitem{amari} S-i. Amari, Field theory of self-organizing neural nets. IEEE Transations on Systems, Man and cybernetics 13 (1983) 741-748

\bibitem{freeman} W.J. Freeman, {\em Tutorial in Neurobiology: From Single

Neurons to Brain Chaos.} International Journal of Bifurcation and Chaos 2

(1992) 451-482.

\bibitem{mallot} Mallot H.A, Giannakopoulos F, Population networks: a

large scale framework for modeling cortical neural networks.

Max-Planck-Institute of biological cybernetics, Technical Report 24 (1996)


Amit D.J, The Hebbian paradigm reintegrated: local reverberations as internal representations. Brain and Behavioral Science 18 (1995) 617-657

\bibitem{georgopoulos} Georgopoulos AP, Taira M, Lukashin A, {\em

Cognitive neurophysiology of the motor cortex}, Science 260 (1993) 47-52

\bibitem{dualpop} Koechlin E, Burnod Y, {\em Dual population coding in the

neocortex: a model of interaction between representation and attention in

the visual cortex}. Tech. Report, Inst. des Neurosciences, Paris 1995.

\bibitem{Mussa-Ivaldi} Mussa-Ivaldi F.A, From basis functions to basis

fields: using vector primitives to capture vector patterns. Biolg.

Cybernetics 67 (1992) 479-489


Pellionisz A, Llin\'as R, Tensorial approach to the geometry of brain function: cerebellar coordination via metric tensor. Neuroscience 5 (1980) 1125-1136


Pellionisz A, Tomko D.L, Bloedel J.R,

Neural geometry revealed by neurocomputer analysis of multi-unit recordings. In: Eeckman F.H, Bower J.M (Eds.), Computation and neural systems. Kluver 1993, pp. 67-71

\bibitem{damasio82} Damasio, A.R, Damasio H, Van Hoesen G.W,

{\em Prosopagnosia: anatomic basis and behavioral mechanisms.}

Neurology 32 (1982) 331-341.

\bibitem{stein} Stein B. E, Meredith M. A, {\em The merging of the

senses}. (MIT Press, Cambridge, MA 1993)


Anderson C, van Essen D, Neurobiological computational systems, in:

computational intelligence imitating life, ed. J.M. ¯urada, R.J. Marks,

C.J. Robinson, IEEE Press, NY 1994


Siegelmann H.T, Computation beyond the Turing limit, Science 268 (1995)

383-396; Siegelmann H.T. The simple dynamics of super Turing theories.

Theoretical Computer Science, 168 (1996) 461-472


B. MacLennan, {\em Field computation in the brain},

CS-92-174 (Univ. of Tennessee, Knoxville, TN 37996) %


Jeden z ostatnich numerów Neural Computations (1997)


F\"old\'iak P, The `Ideal homunculus': statistical inferences from neural population responses. In: Eeckman F.H, Bower J.M (Eds.), Computation and neural systems. Kluver 1993, pp. 55-60

\bibitem{palm} Palm G. (1990) {\em Cell assemblies as a guidline for

brain research}, Concepts in Neuroscience, {\bf 1}: 133-147

\bibitem{mountcastle} Mountcastle V.B. (1978) {\em An organizing

principle for cerebral function. The unit module and the distributed system.} In: The mindful brain, eds. Edelman GE and Mountcastle VB,

MIT-Press, Cambridge, MA

\bibitem{happel94} Happel BLM and Murre JMJ (1994)

{\em The Design and Evolution of Modular Neural Network Architectures.}

Neural Networks 7: 985-1004.

\bibitem{zipser} Zipser D (1991) {\em Reccurent network model of the

neural mechanism of short-term active memory.} Neural Computation {\bf 3}:



D.J. Amit, N. Brunel, Global spontaneous activity and local structured

(learned) delay activity in cortex (preprint, Inst. of Physics, Univ. of

Rome, 1995)

\bibitem{lisman} Lisman J.E. and Idiart M.A.P. {\em Storage of 7 $\pm$ 2 short-term memories in oscillatory subcycles}, Science 267 (1995) 1512-1515


Goldfarb L, Abela J, Bhavsar V.C, Kamat V.N, Can a vector space based learning algorithm discover inductive class generalization in symbolic environment? Pattern Recognition Letters 16 (1995) 719-726


Elman J.L, Language as a dynamical system, in: R.F. Port, T. van Gelder,

Eds, Mind as motion: explorations in the dynamics of cognition (Cambridge,

MA, MIT Press 1995), pp. 195-223


J. Newman and B.J. Baars, Neural Global Workspace Model, Concepts in Neuroscience 4 (1993) 255-290


Ruppin E, Neural modelling of psychiatric disorders, Network 6 (1995) 635-656


Ruppin E, Reggia J, Berndt R (Eds.), Neural modeling of brain and cognitive disorders. Singapore, World Scientific 1996

\bibitem{neuroact} Freeman W.J., {\em Mass Action in the Nervous system}

(Academic Press, NY 1975); Freeman W.J, Simulation of chaotic EEG patterns with a dynamic model of the olfactory system. Biolog. Cybernetics 56 (1987) 139-150

\bibitem{skarda} Skarda C, W.J. Freeman, {\em How brains make chaos

to make sense of the world. }The Behavioral and Brain Sci. {\bf 10} (1987)


\bibitem{cowan} Cowan J.D., {\em A statistical mechanics of nervous

activity. }Lectures on Math. in Life Sciences 2 (1970) 1-57, ed. by M.

Gerstenhaber (Am. Math. Soc, Providence RI)


Koerner E, Tsujino H, Masutani T, A cortical-type modular neural network

for hypothetical reasoning, Neural Networsk (in print)


Somers D. C, Todorov E.V., Siapas A.G, Sur M, Vector-space integration of local and long-range information in visual cortex. AI memo 1556, November 1995.


Murre J, TraceLink: A model of amnesia and consolidation of memory. Hippocampus 6 (1996) 675-684

\bibitem{libet} Libet B. (1985) {\em Unconscious cerebral initiative and

the role of conscious will in voluntary action.} The Behavioral and Brain

Sciences 8: 529-566

\bibitem{libetsum} Libet B. (1993) {\em Neurophysiology of Consciousness.

Collected papers and new essays} (Birkhuser, Boston, Basel Berlin)


Taylor J.G, Alavi F.N, Mathematical analysis of a competitive

network for attention. In: J.G. Taylor, ed. Mathematical Approaches to Neural Networks (Elsevier 1993), pp.341-382


Haken H, Synergetic Computers and Cognition. Springer 1991

\bibitem{thinking} A. Garnham and J. Oakhill, {\em Thinking and reasoning}. (Oxford, Blackwell 1994)

\bibitem{fodor} Fodor J. {\em \ Psychosemantics}. MIT Press, Cambridge, MA 1987)

\bibitem{fodorpyl} Fodor J, Pylyshin Z,

{\em \ Critical analysis of connectionism.} Cognition 28 (1988) 3-72

\bibitem{casey} Casey M.P, {\em Computation in Discrete-Time Dynamical

Systems} (PhD thesis, UCSD 1995, available in neuroprose).


Edelman S, Intrator N, Learning as extraction of low-dimensional representations. In: Medin D, Goldstone R, Schyns P (Eds.), Mechanism of Percpetual Learning (Academic Press, in print)

\bibitem{perception} I. Roth, V. Bruce {\em Perception and Representation}, (Open University Press, 2n ed, 1995)

\bibitem{FSM} Duch W, Diercksen G.H.F, {\it Feature Space Mapping as a

universal adaptive system}. Computer Physics Communications {\bf 87}

(1995) 341-371; Duch W, {\em Floating Gaussian Mapping: a new model of

adaptive systems}, Neural Network World 4 (1994) 645-654; Duch W, Adamczak R, Jankowski N, {\em New developments in the Feature Space Mapping model}, Third Conference on Neural Networks and Their Applications, Kule, October 1997 (in print)

\bibitem{FSMaplic} Duch W, Adamczak R, Jankowski N, Naud A, {\em

Feature Space Mapping: a neurofuzzy network for system identification},

Engineering Applications of Neural Networks, Helsinki 1995, pp. 221--224


Crick F, {\em The Astonishing hypothesis. The scientific search for the soul. }(Charles Scribner's Sons: New York 1994)

\bibitem{kohonen} T. Kohonen, {\em An Introduction to Neural Computing.}

Neural Networks 1 (1988) 3-16; T. Kohonen, {\em Self-organization and

Associative Memory} (Springer-Verlag 1984, 3rd edition: 1989); T. Kohonen,

{\em Self-organizing Maps} (Springer-Verlag 1995).

\bibitem{locallearn} L. Bottou, V. Vapnik, {\em Local learning

algorithms,} Neural Comput. 4 (1992) 888-901; V. Vapnik, L. Bottou, {\em

Local Algorithms for Pattern Recognition and Dependencies Estimation},

Neural Comput, 1993, v.5, pp. 893-909


Edelman G, Bright Air, Brillant Fire. On the matter of mind. (Penguin 1992)

\bibitem{MBR} D.L. Waltz, {\it Memory-based reasoning}, in: M. A. Arbib,

Editor, {\em The Handbook of Brain Theory and Neural Networks }(MIT Press

1995), pp. 568-570

\bibitem{baars} Baars B.J. (1988) {\em A Cognitive Theory of Consciousness} (Cambridge University Press, Cambridge, MA)

\bibitem{symbdyn} T. Bedford, M. Keane and C. Series, {\em Ergodic

theory, symbolic dynamics and hyperbolic spaces} (Oxford University Press



Sommerhoff, G. (1990) Life, brain and consciousness (North Holland:



Parnas B.R, Stochastic resonance and noise in the neural coding and senosry signals. In: Bower J.M (Ed.), Computation neuroscience. Trends in research 1995. Academic Press 199, pp. 113-118

\bibitem{finsler} P.L. Antonelli, R.S. Ingarden, M. Matsumoto, The Theory of Sprays and Finsler Spaces with Applications in Physics and Biology (Kluver Academic, Dodrecht 1993)


Tanaka K, Inferotemporal cortex and object vision, Ann. Review of Neuroscience 19 (1996) 109-139


Ullman S, High-level vision. Object recognition and visual cognition. MIT Press 1996


Duch W, Jankowski N, New neural transfer functions. Applied Mathematics and Computer Science (in print, 1997)


Shepard R.N, Toward a universal law of generalization for psychological science. Science 237 (1987) 1317-1323


Hsu C.S, Global analysis by cell mapping, J. of Bifurcation and Chaos 2 (1994) 727-771


Shepard R.N, Multidimensional scaling, tree fitting and clustering. Science 210 (1980) 390-397


Edelman S, Intrator N, Poggio T, Complex Cells and Object Recognition (submitted to NIPS'97)


Lund K, Hyperspace Analog to Language: a General Model of Semantic Representation. TENNET VI, Sixth Annual Conference in Theoretical and Experimental Neuropsychology, Montreal, Quebec 1995

\bibitem{Fauconnier} G. Fauconniere, {\em Mental Spaces }(Cambridge Univ.

Press 1994)


G\"ardenfors P, Holmqvist K, Concept formation in dimensional spaces, Lund University Cognitive Studies Report 26 (1994)


Ripps L.J, Shoben E.J, Smith E.E, Semantic distance and the verification of semantic relations. Journal of Verbal Learning and Verbal Behavior 12 (1973) 1-20


Ritter H, Kohonen T, Self-organizing semantic maps. Biolog. Cybernetics 61 (1989) 241-254


Yanaru T, Hirotja T, Kimura N, An emotion-processing system based on fuzzy inference and its subjective observations. Int. J. Approximate Reasoning 10 (1994) 99-122


Musha T, EEG - emotions, Proc. of 3rd confernce on Soft Computing, Iizuka 1996, pp.


Van Loocke P, The Dynamics of Concepts. A connectionist model. Lecture Notes in Artificial Intelligence, Vol. 766 (Springer Verlag 1994)

\bibitem{buzan} Buzan T, (1989) {\em Use your head}. (BBC Books: London)

\bibitem{RBF} Poggio T, Girosi F, {\em Networks for approximation

and learning}. Proc. of the IEEE 78 (1990) 1481-1497

\bibitem{RAN} Platt J, {\em A resource-allocating network for function

interpolation.} Neural Computation 3 (1991) 213-225; Kadirkamanathan V,

Niranjan M, {\em A function estimation approach to sequential learning with

neural networks. }Neural Computation 5 (1993) 954-975


Duch W, Adamczak R, Jankowski N,

{\em Initialization of adaptive parameters in density networks}, Third Conference on Neural Networks and Their Applications, Kule, Poland (in print)

\bibitem{FSMrules} Duch W, Adamczak R, Gr¹bczewski K, {\it Extraction of crisp logical rules using constrained backpropagation networks.}

International Conference on Artificial Neural Networks (ICNN'97), Houston, TX, 9-12.6.1997, pp. 2384-2389

\bibitem{bishop} C. Bishop, Neural networks for pattern recognition (Clarendon Press, Oxford 1995)


Cohen M.M, Massaro D.W, On the similarity of categorization models, In: F.G. Ashby, ed. Multidimensional models of perception and cognition (LEA, Hillsdale, NJ 1992), chapter 15.


Nosofsky R.M, Gluck M.A, Palmeri T.J, McKinley S.C, Glauthier P, Comparing models of rule-based classification learning: a replication and extension of Shepard, Hovland and Jenkins (1961). Memory and Cognition 22 (1994) 352-369

\bibitem{artmind} Duch W. (1994) {\em Towards Artificial Minds}, Proc. of I National Conference on neural networks and applications, Kule, April 1994, pp. 17-28


R.N. Shepard, C.I. Hovland and H.M. Jenkins (1961) Learning and memorization of classifications. Psychological Monographs, issue 517


Medin D.L, Edelson S.M, Problem structure and the use of base-rate information from experience. Journ. of Exp. Psych: General 117 (1988) 68-85


Kruschke J. K, Erickson M.A, Five principles for models of category learning. In: Z. Dienes (ed.), Connectionism and Human Learning (Oxford, England: Oxford University Press 1996)


Wallis G, Presentation order affects human object recognition learning,

Technical Report, Max-Planck Inst. of Biological Cybernetics, Aug. 1996


Repository Staff Only: item control page