2_24_09_NonlinearRecurrentNetworks

2_24_09_NonlinearRecurrentNetworks - A recurrent network is...

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Unformatted text preview: A recurrent network is a feedforward network with a recurrent synaptic weight matrix. Assuming that the activation function F is linear, that is, F(x)=x, and denoting the input as h = W . u r d v dt = v + h + M v r d v dt = I v + h + M v r d v dt = M I ( ) v + h r d v dt = v + F W u + M v ( ) We now consider the consequences of the assumption that the activation function is a rectification with threshold : F h + M v ( ) = h + M v + x [ ] + = x i x i x i < We also consider the continuous approximation for recurrent networks (for the particular example of orientation selectivity): The main property in the primary- visual- cortex is orientation selectivity, which arises from feedforward and recurrent synapses. We also consider the continuous approximation for recurrent networks (for the particular example of orientation selectivity): r d v dt =...
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2_24_09_NonlinearRecurrentNetworks - A recurrent network is...

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