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Weakly Connected Neural Networks

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Published by Springer New York, Imprint, Springer in New York, NY .
Written in English

Subjects:

  • Mathematics,
  • Global analysis (Mathematics),
  • Neurosciences

Book details:

About the Edition

This book is devoted to local and global analysis of weakly connected systems with applications to neurosciences. Using bifurcation theory and canonical models as the major tools of analysis, it presents systematic and well motivated development of both weakly connected system theory and mathematical neuroscience. Bifurcations in neuron and brain dynamics, synaptic organizations of the brain, and the nature of neural codes are among the many important issues addressed. The authors offer the reader classical results as well as some of the most recent developments in the field. The book will be useful to researchers and graduate students in various branches of mathematical neuroscience.

Edition Notes

Statementby Frank C. Hoppensteadt, Eugene M. Izhikevich
SeriesApplied Mathematical Sciences, 0066-5452 -- 126, Applied mathematical sciences (Springer-Verlag New York Inc.) -- 126.
ContributionsIzhikevich, Eugene M.
Classifications
LC ClassificationsQA299.6-433
The Physical Object
Format[electronic resource] /
Pagination1 online resource (400 pages 149 illustrations).
Number of Pages400
ID Numbers
Open LibraryOL27095294M
ISBN 101461218284
ISBN 109781461218289
OCLC/WorldCa840278238

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  Weakly Connected Neural Networks (Applied Mathematical Sciences Book ) Softcover reprint of the original 1st ed. Edition, Kindle Edition by Frank C. Hoppensteadt (Author), Eugene M. Izhikevich (Author, Contributor) Format: Kindle Edition. Book 25 Manufacturer: Springer. Weakly Connected Neural Networks (Applied Mathematical Sciences ()) by Hoppensteadt, Frank C.; Izhikevich, Eugene M. and a great selection of related books, art . Get this from a library! Weakly Connected Neural Networks. [Frank C Hoppensteadt; Eugene M Izhikevich] -- This book is devoted to local and global analysis of weakly connected systems with applications to neurosciences. Using bifurcation theory and canonical models as . Weakly Connected Neural Networks is devoted to local and global analysis of weakly connected systems with applications to neurosciences. Using bifurcation theory and canonical models as the major tools of analysis, it presents systematic and well-motivated development of both weakly connected system theory and mathematical neuroscience.

Weakly Connected Neural Networks With Illustrations Springer. Contents Preface vii I Introduction 1 1 Introduction 3 Models in Mathematical Biology 3 Ordinary Language Models 5 Qualifications 7 Dale's Principle 9 Weakness of Synaptic Connections 图书Weakly Connected Neural Networks 介绍、书评、论坛及推荐. Devoted to local and global analysis of weakly connected systems with applications to neurosciences, this book uses bifurcation theory and canonical models as the major tools of analysis. It presents a systematic and well motivated development of both weakly. The last Chapter 13 describes the relationship between synaptic organizations and dynamical properties of networks of neural oscillators. In other words, the problem of learning and memorization of phase information in the weakly connected network of oscillators corresponding to multiple Andronov-Hopf bifurcation is treated : $ This book is devoted to an analysis of general weakly connected neural networks (WCNNs) that can be written in the form () m Here, each Xi E IR is a vector that summarizes all physiological attributes of the ith neuron, n is the number of neurons, Ii describes the dynam- ics of the ith neuron, and gi describes the interactions between neurons. The small parameter indicates the strength of.

Cite this chapter as: Hoppensteadt F.C., Izhikevich E.M. () Neural Networks. In: Weakly Connected Neural Networks. Applied Mathematical Sciences, vol memristor and preliminaries on weakly connected neural networks and Malkin’s Theorem in Sects. 2 and 3 respectively, and then formulate the mathematical models of a cell and the star-like.   We formulate the training of a neural network to predict the voxel correspondence, which is represented by a dense displacement field (DDF) u n, as a weakly-supervised learning problem that maximises a utility function indicating the expected label similarity over N training image pairs: (1) J = 1 N ∑ n = 1 N 1 M n ∑ m = 1 M n J m n (l m n. Class 1 Neural Excitability, Conventional Synapses, Weakly Connected Networks, and Mathematical Foundations of Pulse-Coupled Models. IEEE Transactions On Neural Networks, Izhikevich E.M. () Weakly Pulse-Coupled Oscillators, FM Interactions, Synchronization, and Oscillatory Associative Memory.