Hysteresis And Neural Memory

by Isaak D Mayergoyz, Can E Korman

Overview

This book presents a concise and rigorous exposition of Preisach hysteresis models and their applications to the modeling of neural memory. It demonstrates that memory of Preisach hysteresis models mimics such properties as: selective nature of neural memories extracted from sensory inputs, distributed nature of neural memories and their engrams, neural memory formation as an emerging property of sparse connectivity, neural memory stability with respect to protein turnover, neural memory storage plasticity and neural memory recalls and their effect on storage.The text is designed to be accessible and appealing to a broad audience of neuroscientists, biologists, bioengineers, electrical engineers, applied mathematicians and physicists interested in neural memory and its molecular basis.

Product Details

ISBN-13:	9789811209505
Publisher:	World Scientific Publishing Company, Incorporated
Publication date:	08/30/2019
Pages:	292
Product dimensions:	6.00(w) x 9.00(h) x 0.69(d)

Table of Contents

Preface vii

1 Classical Preisach Model 1

1.1 What is Hysteresis? 1

1.2 Definition of the Classical Preisach Model of Hysteresis 11

1.3 Diagram Technique and the Basic Properties of the Preisach Model 16

1.4 Identification Problem, FORCs and Representation Theorem 32

1.5 Hysteresis Energy Losses 56

References 70

2 Generalized Preisach Models 73

2.1 "Moving" Preisach Model of Hysteresis 73

2.2 Preisach Model of Hysteresis with Input-Dependent Measure 83

2.3 "Restricted" Preisach Models of Hysteresis 100

2.4 "Dynamic" Preisach Models of Hysteresis 114

2.5 Nonlinear Diffusion and Preisach Model 124

References 144

3 Neural Memory and Hysteresis 147

3.1 Neuron 147

3.2 Channels and Synapses 153

3.3 Action Potentials 164

3.4 Hysteresis Models of Neural Memory 181

3.5 Preisach-Based Data Storage and Global Optimizers 201

References 209

4 Hysteresis Driven by Random Processes 213

4.1 Basic Facts About Stochastic Processes and Hysteresis Driven by I.I.D. Processes 213

4.2 Hysteresis Driven by a Continuous-Time Noise Process 223

4.3 Noise in Hysteretic Systems and Stochastic Processes on Graphs 234

4.4 Spectral Density of Outputs of Hysteretic Systems Driven by Noise 246

4.5 Functional (Path) Integration Models of Hysteresis 255

References 271

Index 275