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E-book: Theory Of Cortical Plasticity

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(Brown Univ, Usa & Bryant College, Usa), (Brown Univ, Usa), (Brown Univ, Usa & Tel-aviv Univ, Israel), (Brown Univ, Usa)
  • Format: 332 pages
  • Pub. Date: 15-Apr-2004
  • Publisher: World Scientific Publishing Co Pte Ltd
  • Language: eng
  • ISBN-13: 9789814483315
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Theory Of Cortical PlasticityLarger Image
  • Format: 332 pages
  • Pub. Date: 15-Apr-2004
  • Publisher: World Scientific Publishing Co Pte Ltd
  • Language: eng
  • ISBN-13: 9789814483315
Other books in subject:

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Four researchers from the Brown University Institute for Brain and Neural Systems present the Bieninstock, Cooper, and Munro (BCM) theory of synaptic plasticity, explaining how brains work. After an introduction, they consider single cell theory, objective function formulations, cortical network theory, second-order learning rules, receptive field selectivity in a natural image environment, ocular dominance in normal and deprived cortex, networks of interacting BCM neurons, and experimental evidence. A software package on the accompanying disk allows the simulations connecting the theory and experiments to be performed and varied. Annotation ©2004 Book News, Inc., Portland, OR (booknews.com)
Preface vii
Acknowledgements xi
The Software Package, Plasticity xix
Notation xxi
Common Acronyms and Abbreviations xxiii
Introduction
1(16)
Visual Cortex Plasticity
3(2)
Theoretical background
5(2)
Comparison of Theory and Experiment
7(2)
Cellular basis for the postulates of the BCM theory
9(2)
A model of inputs to visual cortex cells
11(6)
Single Cell Theory
17(28)
Introduction
17(1)
Definitions and Notation
18(3)
BCM synaptic modification
21(2)
One Dimensional Analysis
23(2)
Fixed threshold
23(1)
Instantaneously sliding threshold
23(1)
Instantaneously sliding threshold with a Probabilistic Input
24(1)
Summary of the One Dimensional Case
25(1)
The Nonlinear Sliding Threshold
25(2)
Analysis of a two dimensional neuron
27(6)
Two input environment
28(1)
Stability analysis
29(1)
Selective Critical Points
29(1)
Non-selective critical points
30(2)
Single input environment
32(1)
Many input environment
32(1)
Some Experimental Consequences
33(12)
Normal Rearing
33(1)
Monocular deprivation
34(11)
Stability of BCM with a Weight Decay Term, in a Linearly Independent Environment
45(6)
Initial Fixed Points
45(1)
Consistency
46(1)
Stability
46(5)
Consistency
49(1)
Consequences
49(2)
Objective Function Formulation
51(38)
Introduction
51(1)
Formulation of the BCM Theory Using an Objective Function
51(7)
Single Neuron
52(3)
Extension to a Nonlinear Neuron
55(1)
Extension to a Network with Feed-Forward Inhibition
56(2)
The BCM feature extraction and coding
58(2)
BCM and suspicious coincidence detection
58(2)
Information Theoretic Considerations
60(5)
Information theory and synaptic modification rules
60(2)
Information theory and early visual processing
62(2)
Information properties of Principal Components
64(1)
Extraction of Optimal Unsupervised Features
65(13)
Projection Pursuit and Deviation from Gaussian Distributions
66(2)
Skewness
68(1)
Skewness 1
68(1)
Skewness 2
69(1)
Kurtosis
69(1)
Kurtosis 1
69(1)
Kurtosis 2
69(1)
Quadratic BCM
70(1)
Constrained BCM measure
70(1)
Independent Components and Receptive Fields
71(2)
Kurtosis and ICA
73(1)
Friedman's distance from uniform measure
73(1)
Entropy
74(2)
The concept of minimum mutual information between neurons
76(1)
Some Related Statistical and Computational Issues in BCM
77(1)
Analysis of the Fixed Points of BCM in High Dimensional Space
78(7)
n linearly independent inputs
79(1)
Stability of the solution
80(2)
Noise with no Patterned Input
82(1)
Noise with Zero Mean
83(1)
Noise with Positive Mean
83(1)
Patterned Input with Noise
84(1)
Application to Various Rearing Conditions
85(2)
Normal Rearing (NR)
85(1)
Monocular Deprivation (MD)
85(1)
Binocular Deprivation (BD)
86(1)
Reverse Suture (RS)
86(1)
Strabismus
87(1)
Discussion
87(2)
Convergence of the Solution of the Random Differential Equations
89(4)
Convergence of the Deterministic Equation
89(1)
Convergence of the Random Equation
90(3)
Analysis and Comparison of BCM and Kurtosis in Extended Distributions
93(16)
Two Dimensions
95(3)
Monocular Deprivation
98(4)
Kurtosis
99(2)
BCM
101(1)
Binocular Deprivation
102(3)
Gaussian Noise
102(1)
Kurtosis
103(1)
BCM
103(2)
Reverse Suture
105(1)
Strabismus
106(3)
Kurtosis
107(1)
BCM
108(1)
Statistical Theorems
109(2)
Cortical Network Theory
111(14)
Introduction
111(1)
Mean Field Theory
112(9)
Position and Stability of Fixed Points of LGN-Cortical Synapses in the Mean Field Network
117(2)
Comparison of Linear Feed-Forward with Lateral Inhibition Network: Mean Field Approximation
119(2)
Matrix-based analysis of networks of interacting and nonlinear BCM neurons
121(1)
Discussion
122(3)
Asymptotic Behavior of Mean Field Equations with Time Dependent Mean Field
125(2)
Review and Analysis of Second Order Learning Rules
127(58)
Introduction
127(1)
Hebb's rule and its derivatives
128(9)
Stabilized Hebbian rule
131(2)
Finding multiple principal components
133(1)
Fixed points of saturating Hebb rules
134(2)
Why are Principal Components not Local
136(1)
Summary
137(1)
Orientation Selectivity
137(18)
An exactly soluble 1D Model
138(4)
Radially symmetric models in 2D
142(1)
2D correlational Models
142(2)
Analysis of Linsker's Model
144(4)
Formation of Receptive-Fields in a Natural Image Environment
148(1)
The visual environment
148(1)
PCA simulations with natural images
149(1)
Analysis of receptive fields formed in a Radially Symmetric Environment
150(3)
Non symmetric environment
153(1)
Summary
154(1)
Ocular Dominance
155(5)
Ocular Dominance in Correlational Low-Dimensional Models
155(4)
Misaligned inputs to cell
159(1)
Combined Orientation Selectivity and Ocular Dominance
160(12)
The two-eye 1D soluble Model
160(1)
A Correlational Model of Ocular Dominance and Orientation Selectivity
161(4)
Four Channel Models
165(1)
Mixing Principal Components
166(4)
Local External Symmetry Breaking
170(1)
A two eye model with Natural Images
171(1)
Deprivation Experiments
172(4)
A Simple Correlational Model using Exact PCA Dynamics
172(1)
Normal Rearing (NR)
173(1)
Monocular Deprivation (MD)
174(1)
Reverse Suture (RS)
175(1)
Binocular Deprivation (BD)
175(1)
Simulation results with natural images
176(1)
Discussion
176(9)
Representing the correlation matrix in a Bessel function Base
185(4)
The correlation function for the pre-processed images
187(2)
Properties of Correlation Functions And How to Make Good Ones
189(2)
The Parity Transform: Symmetry properties of the eigenstates of the two eye problem
191(4)
Receptive field selectivity in a natural image environment
195(26)
Modeling Orientation Selectivity
195(3)
The input environment
196(1)
Sufficient conditions for obtaining orientation selectivity
196(1)
Dependence on RF size and localization
197(1)
Spatial frequency of receptive fields
197(1)
Orientation selectivity with statistically defined learning rules
198(2)
What drives orientation selectivity
200(1)
ON/OFF inputs
201(3)
Direction Selectivity
204(3)
Strobe Rearing
206(1)
Conclusions
207(14)
Technical Remarks Concerning Simulations of Selectivity
221(8)
Testing Orientation and Direction
221(2)
Orientation Selectivity
221(1)
Direction Selectivity
222(1)
Spatial frequency of receptive fields
223(1)
Displaying the Weights
224(1)
Different Forms of BCM Modification
224(5)
Evaluation of the Objective Function using Newton's Method
225(4)
Ocular dominance in normal and deprived cortex
229(24)
Development of normal ocular dominance
229(4)
Deprivation of normal binocular inputs
233(6)
Binocular Deprivation
234(1)
Monocular Deprivation
234(1)
Recovery from Monocular Deprivation
235(1)
Strabismus
236(1)
Robustness to Parameters
237(2)
Time Course of Deprivation: Simulation Time Versus Real Time
239(3)
Dependence of deprivation on spontaneous activity or noise
242(1)
Conclusions
243(10)
Networks of interacting BCM Neurons
253(18)
Simplified Environments
253(3)
Natural Image Environment
256(5)
Orientation Selectivity
256(2)
Orientation Selectivity and Ocular Dominance
258(1)
Orientation and Direction Selectivity
259(2)
Structured lateral connectivity
261(6)
Conclusions
267(4)
Experimental evidence for the assumptions and consequences of the BCM theory
271(20)
Evidence confirming the postulates of the BCM theory
271(7)
The shape of the plasticity curve
272(1)
Rate based induction
272(2)
Pairing based induction
274(1)
Possible physiological bases of synaptic plasticity
275(1)
The sliding modification threshold
276(2)
Evidence confirming the consequences of the BCM theory
278(4)
Conclusion
282(9)
Bibliography 291


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