Contents

pp v-viii

List of contributors

pp ix-xiv

Preface

pp xv-xvi

Neurons and neural networks: general principles

1 - Some recent developments in the theory of neural networks

pp 1-11

2 - Representation of sensory information in self-organizing feature maps, and the relation of these maps to distributed memory networks

pp 12-25

3 - Excitable dendritic spine clusters: nonlinear synaptic processing

pp 26-43

4 - Vistas from tensor network theory: a horizon from reductionalistic neurophilosophy to the geometry of multi-unit recordings

pp 44-73

Synaptic plasticity, topological and temporal features, and higher cortical processing

5 - Neurons with hysteresis?

pp 74-87

6 - On models of short- and long-term memories

pp 88-103

7 - Topology, structure, and distance in quasirandom neural networks

pp 104-118

8 - A layered network model of sensory cortex

pp 119-147

9 - Computer simulation of networks of electrotonic neurons

pp 148-163

10 - A possible role for coherence in neural networks

pp 164-188

11 - Simulations of the trion model and the search for the code of higher cortical processing

pp 189-209

12 - AND–OR logic analogue of neuron networks

pp 210-220

Spin glass models and cellular automata

13 - Neural networks: learning and forgetting

pp 221-231

14 - Learning by error corrections in spin glass models of neural networks

pp 232-239

15 - Random complex automata: analogy with spin glasses

pp 240-248

16 - The evolution of data processing abilities in competing automata

pp 249-259

17 - The inverse problem for neural nets and cellular automata

pp 260-267

Cyclic phenomena and chaos in neural networks

18 - A new synaptic modification algorithm and rhythmic oscillation

pp 268-292

19 - ‘Normal’ and ‘abnormal’ dynamic behaviour during synaptic transmission

pp 293-302

20 - Computer simulation studies to deduce the structure and function of the human brain

pp 303-315

21 - Access stability of cyclic modes in quasirandom networks of threshold neurons obeying a deterministic synchronous dynamics

pp 316-344

22 - Transition to cycling in neural networks

pp 345-356