Prediction of neural responses using a dynamic network (DNet) model.

A. The DNet model. The cochleagram is passed through a set of linear-nonlinear filters, whose response is then integrated over time using an exponentially-decaying impulse response to produce the hidden units’ outputs. A weighted sum of these outputs is passed through a similar output unit to make a prediction of the neural response. B. CCnorm for the DNet model with different latency spans. Each black dot is a neuron, the curve indicates the mean over 73 neurons and error bars are standard error of the mean. C. The input weights (STRFs) for the ‘effective’ hidden units (see Methods) of 8 example neurons (each column is a different neuron) for the DNet model. D. ‘Effectiveness’ (see Methods) and E. IE score (see Methods) of the hidden units shown in panel C.