A solution to the learning dilemma for recurrent networks of spiking neurons

G. Bellec, F. Scherr, A. Subramoney, E. Hajek, D. Salaj, R. Legenstein, and W. Maass

Abstract:

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 learning through synaptic plasticity could be organized in such networks. We argue that two pieces of this puzzle were provided by experimental data from neuroscience. A new mathematical insight tells us how they need to be combined to enable network learning through gradient descent. The resulting 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. But in contrast to BPTT, e-prop is biologically plausible. In addition, it elucidates how brain-inspired new computer chips that are drastically more energy ef ficient can be enabled to learn.



Reference: G. Bellec, F. Scherr, A. Subramoney, E. Hajek, D. Salaj, R. Legenstein, and W. Maass. A solution to the learning dilemma for recurrent networks of spiking neurons. bioRxiv/org/10.1101/738385, August 2019.