Platonic model of mind as an approximation to neurodynamics

\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. ¯urada, 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, 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)

\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