Hysteresis And Neural Memory

By: Can Korman, Isaak D Mayergoyz

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eText | 20 August 2019

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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.

Contents:

Classical Preisach Model:
- What is Hysteresis?
- Definition of the Classical Preisach Model of Hysteresis
- Diagram Technique and the Basic Properties of the Preisach Model
- Identification Problem, FORCs and Representation Theorem
- Hysteresis Energy Losses
Generalized Preisach Models:
- 'Moving' Preisach Model of Hysteresis
- Preisach Model of Hysteresis with Input-Dependent Measure
- 'Restricted' Preisach Models of Hysteresis
- 'Dynamic' Preisach Models of Hysteresis
- Nonlinear Diffusion and Preisach Model
Neural Memory and Hysteresis:
- Neuron
- Channels and Synapses
- Action Potentials
- Hysteresis Models of Neural Memory
- Preisach Based Data Storage and Global Optimizers
Hysteresis Driven by Random Processes:
- Basic Facts About Stochastic Processes and Hysteresis Driven by I.I.D. Processes
- Hysteresis Driven by a Continuous-Time Noise Process
- Noise in Hysteretic Systems and Stochastic Processes on Graphs
- Spectral Density of Outputs of Hysteretic Systems Driven by Noise
- Functional (Path) Integration Models of Hysteresis

Readership: Professionals, researchers, academics and graduate students in bioengineering, neurobiology, biophysics and electrical engineering.Hysteresis;Neuron;Ion Channel;Neuroscience;Action Potential00