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

Guillaume Bellec, Franz Scherr, Anand Subramoney, Elias Hajek, Darjan Salaj, Robert Legenstein, Wolfgang Maass

Research output: Working paperPreprint


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.
Original languageEnglish
Number of pages31
Publication statusPublished - 9 Dec 2019

Publication series

NamebioRxiv - the Preprint Server for Biology
PublisherCold Spring Harbor Laboratory Press


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