Hysteresis and Neural Memory

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 HysteresisDiagram Technique and the Basic Properties of the Preisach ModelIdentification Problem, FORCs and Representation TheoremHysteresis Energy LossesGeneralized Preisach Models:'Moving' Preisach Model of HysteresisPreisach Model of Hysteresis with Input-Dependent Measure'Restricted' Preisach Models of Hysteresis'Dynamic' Preisach Models of HysteresisNonlinear Diffusion and Preisach ModelNeural Memory and Hysteresis:NeuronChannels and SynapsesAction PotentialsHysteresis Models of Neural MemoryPreisach Based Data Storage and Global OptimizersHysteresis Driven by Random Processes:Basic Facts About Stochastic Processes and Hysteresis Driven by I.I.D. ProcessesHysteresis Driven by a Continuous-Time Noise ProcessNoise in Hysteretic Systems and Stochastic Processes on GraphsSpectral Density of Outputs of Hysteretic Systems Driven by NoiseFunctional (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