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The Relevance of the Time Domain to Neural Network Models

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Cover of 'The Relevance of the Time Domain to Neural Network Models'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction
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    Chapter 2 Adaptation and Contraction Theory for the Synchronization of Complex Neural Networks
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    Chapter 3 Temporal Coding Is Not Only About Cooperation—It Is Also About Competition
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    Chapter 4 Using Non-oscillatory Dynamics to Disambiguate Pattern Mixtures
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    Chapter 5 Functional Constraints on Network Topology via Generalized Sparse Representations
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    Chapter 6 Evolution of Time in Neural Networks: From the Present to the Past, and Forward to the Future
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    Chapter 7 Synchronization of Coupled Pulse-Type Hardware Neuron Models for CPG Model
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    Chapter 8 The Relevance of the Time Domain to Neural Network Models
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    Chapter 9 Solving Complex Control Tasks via Simple Rule(s): Using Chaotic Dynamics in a Recurrent Neural Network Model
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    Chapter 10 Time Scale Analysis of Neuronal Ensemble Data Used to Feed Neural Network Models
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    Chapter 11 Simultaneous EEG-fMRI: Integrating Spatial and Temporal Resolution
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    Chapter 12 Erratum to: Time Scale Analysis of Neuronal Ensemble Data Used to Feed Neural Network Models
Attention for Chapter 4: Using Non-oscillatory Dynamics to Disambiguate Pattern Mixtures
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Chapter title
Using Non-oscillatory Dynamics to Disambiguate Pattern Mixtures
Chapter number 4
Book title
The Relevance of the Time Domain to Neural Network Models
Published by
Springer, Boston, MA, January 2012
DOI 10.1007/978-1-4614-0724-9_4
Book ISBNs
978-1-4614-0723-2, 978-1-4614-0724-9
Authors

Tsvi Achler

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Australia 1 33%
Unknown 2 67%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 33%
Other 1 33%
Student > Doctoral Student 1 33%
Readers by discipline Count As %
Computer Science 2 67%
Physics and Astronomy 1 33%

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