def backward(ctx, gradOutput):
(membranePotential, threshold, pdfTimeConstant, pdfScale, srmKernel,
Ts) = ctx.saved_tensors
spikePdf = pdfScale / pdfTimeConstant * torch.exp(
-torch.abs(membranePotential - threshold) / pdfTimeConstant)
return slayerCuda.corr(gradOutput * spikePdf, srmKernel,
Ts), None, None, None
def backward(ctx, gradOutput):
(filter, Ts) = ctx.saved_tensors
gradInput = slayerCuda.corr(gradOutput.contiguous(), filter, Ts)
if filter.requires_grad is False:
gradFilter = None
else:
gradFilter = None
pass
return gradInput, gradFilter, None
def backward(ctx, gradOutput): ''' ''' (filter, Ts) = ctx.saved_tensors return slayerCuda.corr(gradOutput, filter, Ts), None, None
