3_5_09_UnsupervisedLearning

3_5_09_UnsupervisedLearning - Outline of the Lecture...

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Unformatted text preview: Outline of the Lecture Outline of the Lecture Hebbian Unsupervised Learning Hebbian Unsupervised Learning Development Orientation Selectivity Ocular Development Orientation Selectivity Ocular Dominance Dominance Hebbian Unsupervised Hebbian Unsupervised Learning Leads to Self Learning Leads to Self Organization of Neural Organization of Neural Circuits Circuits Long-term potentiation and depression at the hippocampus are examples of Hebb-Stent rule. A full feedforward network has vector inputs and outputs connected by a weight matrix. In learning models, the dynamics of firing are much faster than those of synaptic plasticity. Hence, a good approximation is The simplest rule following Hebbs conjecture is v = w d r w d t = v r u In the simplest case, one uses this rule with w d r w d t = v r u Combining the response with the last equation, gives the correlation-based plasticity rule: v = w d r w d t = v r u w d r w d t = r w r u ( ) r u w d r w d t = Q r w w h e r e Q b b ' = u b u b ' = u 1 w b ' u b ' b ' = 1...
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This note was uploaded on 06/08/2009 for the course BME 575L taught by Professor Grzywacz during the Spring '09 term at USC.

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3_5_09_UnsupervisedLearning - Outline of the Lecture...

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