2_19_09_LinearRecurrentNetworks_1

2_19_09_LinearRecurrentNetworks_1 - Outline of the Lecture...

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Unformatted text preview: Outline of the Lecture Outline of the Lecture Linear Recurrent Network Models Linear Recurrent Network Models Recurrent Matrices Eigenvalues, Eigenvectors Recurrent Matrices Eigenvalues, Eigenvectors Properties Properties Synaptic-Matrix Eigenvector Synaptic-Matrix Eigenvector Properties Determine Properties Determine Responses of Linear Responses of Linear Recurrent Networks Recurrent Networks A full feedforward network has vector inputs and outputs connected by a weight matrix. A recurrent network is a feedforward network with a recurrent synaptic weight matrix. For a feedforward network: For a recurrent network: τ r d v a d t = - v a + F W a b b = 1 N a å u b ae è ç ö ø ÷ ae è ç ö ø ÷ τ r d r v d t = - r v + F W × r u ( ) τ r d r v d t = - r v + F W × r u + M × r v ( ) Assuming that the activation function F is linear, that is, F(x)=x, and denoting the input as h =...
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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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2_19_09_LinearRecurrentNetworks_1 - Outline of the Lecture...

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