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Theory of cortical plasticity

Author: Leon N Cooper
Publisher: New Jersey : World Scientific, 2004.
Edition/Format:   eBook : Document : EnglishView all editions and formats
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Genre/Form: Electronic books
Computer network resources
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Leon N Cooper
ISBN: 9789812387462 9812387463 9789812387912 9812387919
OCLC Number: 567962488
Description: 1 online resource (xxiv, 308 pages)
Contents: Cover --
Contents --
Preface --
Acknowledgements --
The Software Package, Plasticity --
Notation --
Common Acronyms and Abbreviations --
1. Introduction --
1.1 Visual Cortex Plasticity --
1.2 Theoretical background --
1.3 Comparison of Theory and Experiment --
1.4 Cellular basis for the postulates of the BCM theory --
1.5 A model of inputs to visual cortex cells --
2. Single Cell Theory --
2.1 Introduction --
2.2 Definitions and Notation --
2.3 BCM synaptic modification --
2.4 One Dimensional Analysis --
2.4.1 Fixed threshold --
2.4.2 Instantaneously sliding threshold --
2.4.3 Instantaneously sliding threshold with a Probabilistic Input --
2.4.4 Summary of the One Dimensional Case --
2.5 The Nonlinear Sliding Threshold --
2.6 Analysis of a two dimensional neuron --
2.6.1 Two input environment --
2.6.2 Stability analysis --
2.6.3 Single input environment --
2.6.4 Many input environment --
2.7 Some Experimental Consequences --
2.7.1 Normal Rearing --
2.7.2 Monocular deprivation --
2A. Stability of BCM with a Weight Decay Term, in a Linearly Independent Environment --
2A.1 Initial Fixed Points --
2A.1.1 Consistency --
2A.2 Stability --
2A.2.1 Consistency --
2A.2.2 Consequences --
3. Objective Function Formulation --
3.1 Introduction --
3.2 Formulation of the BCM Theory Using an Objective Function --
3.2.1 Single Neuron --
3.2.2 Extension to a Nonlinear Neuron --
3.2.3 Extension to a Network with Feed-Forward Inhibition --
3.3 The BCM feature extraction and coding --
3.3.1 BCM and suspicious coincidence detection --
3.4 Information Theoretic Considerations --
3.4.1 Information theory and synaptic modification rules --
3.4.2 Information theory and early visual processing --
3.4.3 Information properties of Principal Components --
3.5 Extraction of Optimal Unsupervised Features --
3.5.1 Projection Pursuit and Deviation from Gaussian Distributions --
3.5.2 Skewness --
3.5.3 Kurtosis --
3.5.4 Quadratic BCM --
3.5.5 Constrained BCM measure --
3.5.6 Independent Components and Receptive Fields --
3.5.7 Kurtosis and ICA --
3.5.8 Friedmans distance from uniform measure --
3.5.9 Entropy --
3.5.10 The concept of minimum mutual information between neurons --
3.5.11 Some Related Statistical and Computational Issues in BCM --
3.6 Analysis of the Fixed Points of BCM in High Dimensional Space --
3.6.1 n linearly independent inputs --
3.6.2 Noise with no Patterned Input --
3.6.3 Patterned Input with Noise --
3.7 Application to Various Rearing Conditions --
3.7.1 Normal Rearing (NR) --
3.7.2 Monocular Deprivation (MD) --
3.7.3 Binocular Deprivation (BD) --
3.7.4 Reverse Suture (RS) --
3.7.5 Strabismus --
3.8 Discussion --
3A. Convergence of the Solution of the Random Differential Equations --
3A.1 Convergence of the Deterministic Equation --
3A.2 Convergence of the Random Equation --
3B. Analysis and Comparison of BCM and Kurtosis in Extended Distributions --
3B.1 Two Dimensions --
3B.2 Monocular Deprivation --
3B.2.1 Kurtosis --
3B.2.2 BCM --
3B.3 Binocular Deprivation --
3B.3.1 Gaussian Noise --
3B.3.2 Kurtosis --
3B.3.3 BCM --
3B.4 Reverse Suture --
3B.5 Strabismus --
3B.5.1 Kurtosis --
3B.5.2 BCM --
3C. Statistical Theorems --
4. Cortical Network Theory --
4.1 Introduction --
4.2 Mean.
Responsibility: Leon Cooper [and others].

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