In a multiscale stochastic network, combining signals before the nonlinearity (integration) encodes more input-output information than combining them after the nonlinearity (summation), with fast processing and certain layer sizes giving the largest gains.
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Multiscale nonlinear integration drives accurate encoding of input information
In a multiscale stochastic network, combining signals before the nonlinearity (integration) encodes more input-output information than combining them after the nonlinearity (summation), with fast processing and certain layer sizes giving the largest gains.