---
abstract: '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. '
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given: Wlodzislaw
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date: 1998
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- family: Kasabov
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keywords: "Neurodynamics, mind models, neural networks, neurofuzzy systems, symbolic dynamics, categorization, cognitive neuroscience, cognitive psychology.\n"
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pagerange: 491-512
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publication: Brain-like computing and intelligent information systems
publisher: 'Springer, Singapore'
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referencetext: |+
\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
Holland.
\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\\
ftp.phys.uni.torun.pl/pub/papers/kmk 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 http://www.bbb.caltech.edu/GENESIS
\bibitem{NO}
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
1949)
\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,
http://www.cs.utexas.edu/users/nn/web-pubs/htmlbook96.
\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)
\bibitem{amit-bbs}
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
\bibitem{tensor}
Pellionisz A, Llin\'as R, Tensorial approach to the geometry of brain function: cerebellar coordination via metric tensor. Neuroscience 5 (1980) 1125-1136
\bibitem{pellionisz}
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)
\bibitem{essen}
Anderson C, van Essen D, Neurobiological computational systems, in:
computational intelligence imitating life, ed. J.M. Żurada, R.J. Marks,
C.J. Robinson, IEEE Press, NY 1994
\bibitem{siegelmann}
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
\bibitem{simulacrum}
B. MacLennan, {\em Field computation in the brain},
CS-92-174 (Univ. of Tennessee, Knoxville, TN 37996) % maclennan@cs.utk.edu
\bibitem{metric-spikes}
Jeden z ostatnich numerów Neural Computations (1997)
\bibitem{foldiak}
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}:
178-192
\bibitem{brunel}
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
\bibitem{goldfarb}
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
\bibitem{elman}
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
\bibitem{newman}
J. Newman and B.J. Baars, Neural Global Workspace Model, Concepts in Neuroscience 4 (1993) 255-290
\bibitem{ruppin}
Ruppin E, Neural modelling of psychiatric disorders, Network 6 (1995) 635-656
\bibitem{psychiatry}
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)
161-195;
\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)
\bibitem{Koerner}
Koerner E, Tsujino H, Masutani T, A cortical-type modular neural network
for hypothetical reasoning, Neural Networsk (in print)
\bibitem{somers}
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.
\bibitem{tracelink}
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)
\bibitem{attention}
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
\bibitem{synergetics}
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).
\bibitem{learnlow}
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
\bibitem{crick}
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
\bibitem{edelmang}
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
1991)
\bibitem{sommerhoff}
Sommerhoff, G. (1990) Life, brain and consciousness (North Holland:
Amsterdam)
\bibitem{stochastic}
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)
\bibitem{tanaka}
Tanaka K, Inferotemporal cortex and object vision, Ann. Review of Neuroscience 19 (1996) 109-139
\bibitem{ullman}
Ullman S, High-level vision. Object recognition and visual cognition. MIT Press 1996
\bibitem{basisf}
Duch W, Jankowski N, New neural transfer functions. Applied Mathematics and Computer Science (in print, 1997)
\bibitem{shepard87}
Shepard R.N, Toward a universal law of generalization for psychological science. Science 237 (1987) 1317-1323
\bibitem{cellmap}
Hsu C.S, Global analysis by cell mapping, J. of Bifurcation and Chaos 2 (1994) 727-771
\bibitem{shepard80}
Shepard R.N, Multidimensional scaling, tree fitting and clustering. Science 210 (1980) 390-397
\bibitem{objrecog}
Edelman S, Intrator N, Poggio T, Complex Cells and Object Recognition (submitted to NIPS'97)
\bibitem{HAL}
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)
\bibitem{conceptf}
G\"ardenfors P, Holmqvist K, Concept formation in dimensional spaces, Lund University Cognitive Studies Report 26 (1994)
\bibitem{ripps}
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
\bibitem{SOMword}
Ritter H, Kohonen T, Self-organizing semantic maps. Biolog. Cybernetics 61 (1989) 241-254
\bibitem{yanaru}
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
\bibitem{musha}
Musha T, EEG - emotions, Proc. of 3rd confernce on Soft Computing, Iizuka 1996, pp.
\bibitem{Loocke}
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
\bibitem{FSMinit}
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, Grąbczewski 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)
\bibitem{catmodels}
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.
\bibitem{nosofsky}
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
\bibitem{shepard}
R.N. Shepard, C.I. Hovland and H.M. Jenkins (1961) Learning and memorization of classifications. Psychological Monographs, issue 517
\bibitem{invbase}
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
\bibitem{kruschke}
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)
\bibitem{wallis}
Wallis G, Presentation order affects human object recognition learning,
Technical Report, Max-Planck Inst. of Biological Cybernetics, Aug. 1996
relation_type: []
relation_uri: []
reportno: ~
rev_number: 14
series: ~
source: ~
status_changed: 2007-09-12 16:35:25
subjects:
- comp-neuro-sci
- comp-sci-art-intel
- comp-sci-neural-nets
- neuro-psy
succeeds: ~
suggestions: ~
sword_depositor: ~
sword_slug: ~
thesistype: ~
title: Platonic model of mind as an approximation to neurodynamics
type: bookchapter
userid: 568
volume: ~