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

Nature Communications
G. BellecWolfgang Maass

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.

References

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Citations

Feb 2, 2021·Frontiers in Systems Neuroscience·Hyojin BaeChang-Eop Kim
Jan 29, 2021·Current Opinion in Neurobiology·Dhruva V Raman, Timothy O'Leary
Feb 19, 2021·Neuron·Friedemann ZenkeDan F M Goodman
Mar 13, 2021·Frontiers in Neuroscience·Thomas DalgatyJérôme Casas
Mar 30, 2021·Frontiers in Neuroscience·Shuncheng JiaBo Xu
Apr 27, 2021·ELife·Owen MackwoodHenning Sprekeler
Apr 30, 2021·Frontiers in Computational Neuroscience·Brian Gardner, André Grüning
Jun 29, 2021·Frontiers in Neuroinformatics·Jonas StapmannsDavid Dahmen
Aug 10, 2021·PLoS Computational Biology·Alex D BirdHermann Cuntz
Aug 11, 2021·PLoS Computational Biology·Linnie Jiang, Ashok Litwin-Kumar
Sep 15, 2021·ELife·Dhruva V Raman, Timothy O'Leary
Oct 12, 2021·Frontiers in Neurorobotics·Samuel SchmidgallJoe Hays

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Methods Mentioned

BETA
chip
chips

Software Mentioned

mathop
prop
BPTT
Arcade Learning Environment
TIMIT
Atari
SpiNNaker
RTRL

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