Computer Simulation in Brain Science

doi:10.1017/CBO9780511983467

There has been an enormous increase in research activity aimed at elucidating the basis for cortical activity in the brain. Among modern techniques used in this area of scientific endeavour, few have proved as popular as computer simulation. Model neural networks are the subject of intense study, and some remarkable properties have already come to light: these networks are able to discriminate, remember and associate. Professor Cotterill has assembled leading experts in this burgeoning field to produce an exciting review of advances. The volume covers the creation of computer models of neural networks, and their use in the study of neural function, of cognition, memory and vision. The results and future directions explored here will have an important bearing on research into brain function, physiology, psychology, biophysics and artificial intelligence.

"...an important contribution to research into brain function, physiology, psychology, biophysics, and artificial intelligence." Sci Tech Book News

"...of interest to those following the neural net field...contains thirty-five different papers by such distinguished authors..." Intelligence

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Contents

Frontmatter
pp i-iv
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Contents
pp v-viii
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List of contributors
pp ix-xiv
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Preface
pp xv-xvi
- By Rodney Cotterill, Technical University of Denmark
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Neurons and neural networks: general principles
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1 - Some recent developments in the theory of neural networks
pp 1-11
- By Leon N. Cooper, Brown University
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2 - Representation of sensory information in self-organizing feature maps, and the relation of these maps to distributed memory networks
pp 12-25
- By Teuvo Kohonen, Helsinki University of Technology
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3 - Excitable dendritic spine clusters: nonlinear synaptic processing
pp 26-43
- By W. Rail, National Institutes of Health, I. Segev, National Institutes of Health
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4 - Vistas from tensor network theory: a horizon from reductionalistic neurophilosophy to the geometry of multi-unit recordings
pp 44-73
- By András J. Pellionisz, New York University Medical School
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Synaptic plasticity, topological and temporal features, and higher cortical processing
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5 - Neurons with hysteresis?
pp 74-87
- By Geoffrey W. Hoffmann, University of British Columbia
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6 - On models of short- and long-term memories
pp 88-103
- By P. Peretto
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7 - Topology, structure, and distance in quasirandom neural networks
pp 104-118
- By J. W. Clark, University of Cape Town, G. C. Littlewort, University of Cape Town, J. Rafelski, University of Cape Town
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8 - A layered network model of sensory cortex
pp 119-147
- By Bryan J. Travis, Los Alamos National Laboratory
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9 - Computer simulation of networks of electrotonic neurons
pp 148-163
- By E. Niebur, University of Lausanne, P. Erdös, University of Lausanne
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10 - A possible role for coherence in neural networks
pp 164-188
- By Rodney M. J. Cotterill, Technical University of Denmark
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11 - Simulations of the trion model and the search for the code of higher cortical processing
pp 189-209
- By Gordon L. Shaw, University of California, Dennis J. Silverman, University of California
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12 - AND–OR logic analogue of neuron networks
pp 210-220
- By Y. Okabe, University of Tokyo, M. Fukaya, University of Tokyo, M. Kitagawa, University of Tokyo
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Spin glass models and cellular automata
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13 - Neural networks: learning and forgetting
pp 221-231
- By J.P. Nadal, University of Tokyo, G. Toulouse, University of Tokyo, M. Mézard, University of Tokyo, J. P. Changeux, University of Tokyo, S. Dehaene
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14 - Learning by error corrections in spin glass models of neural networks
pp 232-239
- By S. Diederich, Universität Giessen, M. Opper, Universität Giessen, R. D. Henkel, Universität Giessen, W. Kinzel, Universität Giessen
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15 - Random complex automata: analogy with spin glasses
pp 240-248
- By H. Flyvbjerg, University of Copenhagen
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16 - The evolution of data processing abilities in competing automata
pp 249-259
- By Michel Kerszberg, Federal Republic of Germany, Aviv Bergman, SRI International
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17 - The inverse problem for neural nets and cellular automata
pp 260-267
- By Eduardo R. Caianiello, Università di Salerno, Maria Marinaro, Università di Salerno
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Cyclic phenomena and chaos in neural networks
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18 - A new synaptic modification algorithm and rhythmic oscillation
pp 268-292
- By Kazuyoshi Tsutsumi, Kobe University, Haruya Matsumoto, Kobe University
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19 - ‘Normal’ and ‘abnormal’ dynamic behaviour during synaptic transmission
pp 293-302
- By G. Barna, Central Research Institute for Physics of the Hungarian Academy of Sciences, P. Érdi, Central Research Institute for Physics of the Hungarian Academy of Sciences
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20 - Computer simulation studies to deduce the structure and function of the human brain
pp 303-315
- By P. A. Anninos, University of Thraki, G. Anogianakis, University of Thessalonika
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21 - Access stability of cyclic modes in quasirandom networks of threshold neurons obeying a deterministic synchronous dynamics
pp 316-344
- By J. W. Clark, University of Cape Town, K. E. Kürten, Universität zu Köln, J. Rafelski, University of Cape Town
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22 - Transition to cycling in neural networks
pp 345-356
- By G. C. Littlewort, University of Cape Town, J. W. Clark, University of Cape Town, J. Rafelski, University of Cape Town
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