Verified algorithm and degree of speed up.

<p>(A) The second order component of a cell’s response is modeled as a linear weighting of pairwise products in the stimulus. The linear weighting matrix is the Volterra kernel, <i>G</i>⁽²⁾. (B) The GPU code correctly extracts the Wiener kernel from the stimulus-response pair. We simulated a model cell that used a Volterra kernel <i>G</i>⁽²⁾ to respond to a Gaussian input with unit variance. We then used the GPU code to estimate the Weiner kernel <i>K</i>⁽²⁾. Differences between the actual and estimated kernel go to 0 as the number of stimulus-response samples increases. In this case, the Wiener kernel equals the Volterra kernel because the response does not depend on higher order functions of the stimulus. (C-F) Performance of the GPU-enabled module versus the reference CPU implementation. Unless otherwise stated for each simulation parameter sweep, the dataset included 64 responses of 2¹⁹ samples, with inputs of 8 spatial dimensions each. The code extracted filters with 64 temporal offsets. All calculation times are given in seconds; code was run on hardware as described in Methods. Numbers above the GPU datapoints indicate the factor speedup of the GPU module relative to the CPU module. (C) Calculation time comparison for a sweep of the number of samples of each response. (D) Calculation time comparison for a sweep of the number of temporal offsets. (E) Calculation time comparison for a sweep of the number of spatial dimensions. (F) Calculation time comparison for a sweep of the number of responses (e.g. recorded cells in an imaging dataset).