RT Journal Article
SR Electronic
T1 A solution to the learning dilemma for recurrent networks of spiking neurons
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 738385
DO 10.1101/738385
A1 Guillaume Bellec
A1 Franz Scherr
A1 Anand Subramoney
A1 Elias Hajek
A1 Darjan Salaj
A1 Robert Legenstein
A1 Wolfgang Maass
YR 2019
UL http://biorxiv.org/content/early/2019/12/09/738385.abstract
AB Recurrently connected networks of spiking neurons underlie the astounding information processing capabilities of the brain. But in spite of extensive research, it has remained open how they can learn through synaptic plasticity to carry out complex network computations. We argue that two pieces of this puzzle were provided by experimental data from neuroscience. A new mathematical insight tells us how these pieces need to be combined to enable biologically plausible online network learning through gradient descent, in particular deep reinforcement learning. This new learning method – called e-prop – approaches the performance of BPTT (backpropagation through time), the best known method for training recurrent neural networks in machine learning. In addition, it suggests a method for powerful on-chip learning in novel energy-efficient spike-based hardware for AI.