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

Guillaume Bellec, Franz Scherr, Anand Subramoney, Elias Hajek, Darjan Salaj, Robert Legenstein and Wolfgang Maass ()
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Guillaume Bellec: Graz University of Technology
Franz Scherr: Graz University of Technology
Anand Subramoney: Graz University of Technology
Elias Hajek: Graz University of Technology
Darjan Salaj: Graz University of Technology
Robert Legenstein: Graz University of Technology
Wolfgang Maass: Graz University of Technology

Nature Communications, 2020, vol. 11, issue 1, 1-15

Abstract: Abstract Recurrently connected networks of spiking neurons underlie the astounding information processing capabilities of the brain. Yet in spite of extensive research, how they can learn through synaptic plasticity to carry out complex network computations remains unclear. We argue that two pieces of this puzzle were provided by experimental data from neuroscience. A mathematical result 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 learning method–called e-prop–approaches the performance of backpropagation through time (BPTT), the best-known method for training recurrent neural networks in machine learning. In addition, it suggests a method for powerful on-chip learning in energy-efficient spike-based hardware for artificial intelligence.

Date: 2020
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DOI: 10.1038/s41467-020-17236-y

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