Modeling Brain Function: The World of Attractor Neural NetworksCambridge University Press, 1989 - Всего страниц: 504 One of the most exciting and potentially rewarding areas of scientific research is the study of the principles and mechanisms underlying brain function. It is also of great promise to future generations of computers. A growing group of researchers, adapting knowledge and techniques from a wide range of scientific disciplines, have made substantial progress understanding memory, the learning process, and self organization by studying the properties of models of neural networks - idealized systems containing very large numbers of connected neurons, whose interactions give rise to the special qualities of the brain. This book introduces and explains the techniques brought from physics to the study of neural networks and the insights they have stimulated. It is written at a level accessible to the wide range of researchers working on these problems - statistical physicists, biologists, computer scientists, computer technologists and cognitive psychologists. The author presents a coherent and clear nonmechanical presentation of all the basic ideas and results. More technical aspects are restricted, wherever possible, to special sections and appendices in each chapter. The book is suitable as a text for graduate courses in physics, electrical engineering, computer science and biology. |
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Стр. vi
... noise . 2.3 On Attractors . 2.3.1 2.3.2 2.3.3 2.3.4 2.3.5 Psychiatric speculations and images The role of noise and simulated annealing 2.3.6 Frustration and diversity of attractors Bibliography 3 General Ideas Concerning Dynamics ...
... noise . 2.3 On Attractors . 2.3.1 2.3.2 2.3.3 2.3.4 2.3.5 Psychiatric speculations and images The role of noise and simulated annealing 2.3.6 Frustration and diversity of attractors Bibliography 3 General Ideas Concerning Dynamics ...
Стр. vii
... noise . 126 - Free - energy landscapes - a noisy Lyapunov function . 3.3.4 Free - energy minima , non - ergodicity , order- parameters 3.4 Free - Energy of Fully Connected Ising Model . . 3.4.1 From minimization equation to the free ...
... noise . 126 - Free - energy landscapes - a noisy Lyapunov function . 3.3.4 Free - energy minima , non - ergodicity , order- parameters 3.4 Free - Energy of Fully Connected Ising Model . . 3.4.1 From minimization equation to the free ...
Стр. viii
... noise analysis - memories , spurious states . . 4.3.2 Basins of attraction and retrieval times 4.3.3 ... Noise at Low Loading • 200 4.6.1 4.6.2 Ergodicity at high noise levels - asynchronous . . 200 Just below the critical noise level ...
... noise analysis - memories , spurious states . . 4.3.2 Basins of attraction and retrieval times 4.3.3 ... Noise at Low Loading • 200 4.6.1 4.6.2 Ergodicity at high noise levels - asynchronous . . 200 Just below the critical noise level ...
Стр. ix
... noise 255 • 5.5.3 The role of noise 256 • 259 5.5.4 Synaptic structure and underlying dynamics . 5.5.5 Network storing sequence with several patterns . 262 5.6 Appendix : Elaborate Temporal Sequences 5.6.1 Temporal sequences by time ...
... noise 255 • 5.5.3 The role of noise 256 • 259 5.5.4 Synaptic structure and underlying dynamics . 5.5.5 Network storing sequence with several patterns . 262 5.6 Appendix : Elaborate Temporal Sequences 5.6.1 Temporal sequences by time ...
Стр. x
... Noise and Fields Effect of external fields - thresholds and PSP's . 308 . 311 6.4.3 Fields coupled to several ... Noise and Synaptic Dilution 7.1.1 Two meanings of robustness . 7.1.2 Noise in synaptic efficacies • .. 315 315 • 318 324 ...
... Noise and Fields Effect of external fields - thresholds and PSP's . 308 . 311 6.4.3 Fields coupled to several ... Noise and Synaptic Dilution 7.1.1 Two meanings of robustness . 7.1.2 Noise in synaptic efficacies • .. 315 315 • 318 324 ...
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Modeling Brain Function: The World of Attractor Neural Networks Daniel J. Amit Недоступно для просмотра - 1989 |
Часто встречающиеся слова и выражения
action potential activity ANN's asynchronous dynamics attractor average axon basins of attraction becomes behavior biological bits Chapter cognitive computation correlations corresponding cycle-time decreases described detailed balance dilution discussion distribution dynamical process eigen-value energy ergodicity excitatory fact ferromagnetic Figure finite fixed point free-energy fully connected function Hamming distance hence implies inhibitory initial input Ising model learning local field Lyapunov function magnetization mean-field equations memorized patterns memory minima modification neural networks neurons noise level noiseless non-ergodicity non-zero number of patterns output overlaps parameter perceptron phase potential pre-synaptic present probability PSP's random patterns random variables result retrieval quality right hand side Section simulations single solutions spike spin-glass spins spurious stable stimulus stochastic storage capacity stored patterns structure synapses synaptic efficacies synaptic matrix synchronous temperature temporal sequences term threshold tion trajectories transition uncorrelated updating values zero