3_5_09_UnsupervisedLearning

3_5_09_UnsupervisedLearning - Long-term potentiation and...

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Long-term potentiation and depression at the hippocampus are examples of Hebb-Stent rule.
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A full feedforward network has vector inputs and outputs connected by a weight matrix.
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In learning models, the dynamics of firing are much faster than those of synaptic plasticity. Hence, a good approximation is The simplest rule following Hebb’s conjecture is v = w u τ w d w dt = v u In the simplest case, one uses this rule with w d w dt = v u
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Combining the response with the last equation, gives the correlation-based plasticity rule: v = w u τ w d w dt = v u w d w dt = w u ( ) u w d w dt = Q w where Q bb ' = u b u b ' = u 1 w b ' u b ' b ' = 1 N u u 2 w b ' u b ' b ' = 1 N u u N u w b ' u b ' b ' = 1 N u = u 1 u b ' w b ' b ' = 1 N u u 2 u b ' w b ' b ' = 1 N u u N u u b ' w b ' b ' = 1 N u
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As for linear recurrent networks, the solution is in terms of eigenvalues ( λ μ 0 ) and eigenvectors ( ) of the input correlation matrix Q : τ w d w dt = Q w
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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 - Long-term potentiation and...

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