2_17_09_FeedforwardNetworks_1

2_17_09_FeedforwardNetworks_1 - Outline of the Lecture...

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Unformatted text preview: Outline of the Lecture Outline of the Lecture Feedforward Network Models Feedforward Network Models Feedforward Networks Dynamics Example: Feedforward Networks Dynamics Example: Reaching Reaching Feedforward Networks Feedforward Networks Are the Simplest Kind of Are the Simplest Kind of Brain Circuits Brain Circuits The simplest neural-network model for brain computations is feedforward with one output. The simplest model for the postsynaptic current in a linear feedforward network is d I s d t = - + ( 29 = 1 s d I s d t = - I s + r w r u We assume a steady-state current-to- action-potential-frequency function (the activation function), F(I s ). An extreme model uses very fast firing: For very slow firing: s d I s d t = - I s + r w r u w ith v = F I s ( ) r d v d t = - v + F I s t ( ) ( ) r d v d t = - v + F r w r u ( ) Neurons can display both slow- and fast-firing properties as the mean input current varies. The simplest neural-network model for brain computations is feedforward with one output. A full feedforward network has vector inputs and outputs connected by a weight matrix. For a feedforward network:...
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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_17_09_FeedforwardNetworks_1 - Outline of the Lecture...

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