Single neuron models,

single neuron models

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Über dieses Single neuron models This book describes a large number of open problems in the theory of stochastic neural systems, with the aim single neuron models enticing probabilists to work on them. This includes problems arising from stochastic models of individual neurons as well as those arising from stochastic models of the activities of small and large networks of interconnected neurons.

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The necessary neuroscience background to these problems is outlined within the text, so readers can grasp the context in which they arise. An extensive bibliography is included. Priscilla E.

Bibliografische Information

Lawrence M. Über den Autor Dr. She received a Ph. She has published extensively in several areas of probability and its applications, including stochastic processes, random fields, and asymptotic statistics for stochastic processes.

  • Professur Künstliche Intelligenz | Fak. für Informatik | TU Chemnitz
  • Single frauen arnsberg
  • Stochastic Neuron Models | Priscilla E. Greenwood | Springer
  • Singletreff forchheim

Her current work centers around stochastic dynamical systems, and, in particular, stochastic neural dynamics. He received a Ph.

He has published many research single neuron models and book chapters in psychophysics, cognitive neuroscience, biophysics, and computational neuroscience. Coren and J.

single neuron models

His current work is concerned with issues in i the cognitive neuroscience of attention, memory, reading, and consciousness, ii biophysics and psychophysics of stochastic facilitation, iii mathematical and computer modeling of neuronal oscillations and synchronization, and iv applications of nonlinear dynamical single neuron models theory in cognitive neuroscience. Each book in this series is self-contained, tutorial in nature and inspired by the annual programs at the MBI. They are designed to be used as part of a two week module in a standard graduate course in mathematics.

This book is 70 pages long and informally written, giving a quick introduction to stochastic neural models of varying levels.

single neuron models

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