ePoster · Computational Neuroscience
Smooth exact gradient descent learning in spiking neural networks
Christian Klos, Raoul-Martin Memmesheimer · Bernstein Conference 2024
The ability to train spiking neural network models is essential for the modeling of biological neural networks as well as for neuromorphic computing. The standard approach when training non-spiking neural networks is gradient descent. In spiking neural networks, however, gradient descent learning is complicated by the all-or-none character of spikes (Fig. A). It can lead to unexpected (dis-)appearances of spikes during training, resulting in disruptive, discontinuous changes in the network dynamics [1]. Further, it seemingly prohibits exact gradients to systematically generate or remove spikes