The computational limitations of balanced networks
published: Oct. 17, 2008, recorded: September 2008, views: 349
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Computation in neural networks relies crucially on non-linearity. In neural networks in the balanced state the non-linearity of the neuronal transfer function becomes functionally unimportant. The disappearance of this non- linearity strongly limits the computational power of balanced networks. I will show in examples of balanced networks for associative memory how one can try to circumvent this limitation of balanced networks and discuss the problems with these solutions.
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