Papers › Smooth Exact Gradient Descent Learning in Spiking Neural Networks
Smooth Exact Gradient Descent Learning in Spiking Neural Networks
Christian Klos, Raoul-Martin Memmesheimer
Gradient descent prevails in artificial neural network training, but seems inept for spiking neural networks as small parameter changes can cause sudden, disruptive (dis-)appearances of spikes. Here, we demonstrate exact gradient descent based on continuously changing spiking dynamics. These are generated by neuron models whose spikes vanish and appear at the end of a trial, where it cannot influence subsequent dynamics. This also enables gradient-based spike addition and removal. We illustrate our scheme with various tasks and setups, including recurrent and deep, initially silent networks.
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