Gradient-based methods for spiking physical systems
Julian Goltz
Sebastian Billaudelle
Laura Kriener
Luca Blessing
Christian Pehle
Eric Müller
Johannes Schemmel
Mihai A. Petrovici

Abstract
Recent efforts have fostered significant progress towards deep learning in spiking networks, both theoretical and in silico. Here, we discuss several different approaches, including a tentative comparison of the results on BrainScaleS-2, and hint towards future such comparative studies.
View on arXivComments on this paper