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Chapter 7 Supervised Hebbian Learning

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Outline Linear Associator The Hebb Rule Pseudoinverse Rule Application

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Linear Associator

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Hebb s Postulate When an axon of cell A is near enough to excite a cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that As efficiency, as one of the cells firing B, is increased. D. O. Hebb, 1949 A B

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Hebb Rule(1/2)

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Hebb Rule(2/2)

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Batch Operation

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Performance Analysis(1/2) Input patterns are orthonormal,

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Performance Analysis(2/2)

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Example(orthonormal)

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Example(not orthogonal)

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Pseudoinverse Rule(1/3)

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Pseudoinverse Rule(2/3)

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Pseudoinverse Rule(3/3)

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Relationship to the Hebb Rule

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Example

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Autoassociative Memory

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Tests 50% Occluded 67% Occluded Noisy Patterns (7 pixels)

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Variations of Hebbian Learning Basic Rule: Learning Rate: Smoothing: Delta Rule: Unsupervised:

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Solved Problems

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Solution:

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Solved Problems

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