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Neural networks [1.3] : Feedforward neural network - capacity of single neuron
Über dieses Buch This book describes a large single neuron models of open problems in the theory of stochastic neural systems, with the aim of 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.
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. 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
- Behinderte männer kennenlernen
- Was ist flirten gute frage
Her current work centers around stochastic dynamical systems, and, in particular, stochastic neural dynamics. He received whatsapp flirten wie Ph. He has published many research articles and book chapters in psychophysics, cognitive neuroscience, biophysics, and computational neuroscience.
Coren and J. 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 systems theory in cognitive neuroscience.
- Katalogdaten Kurzbeschreibung The focus of this course is on statistical approaches in neuroscience.
- Single treffen singen
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 single neuron models models of varying levels.