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

被引：303

作者：

Bellec, Guillaume ^{[1
]}

Scherr, Franz ^{[1
]}

Subramoney, Anand ^{[1
]}

Hajek, Elias ^{[1
]}

Salaj, Darjan ^{[1
]}

Legenstein, Robert ^{[1
]}

Maass, Wolfgang ^{[1
]}

机构：

[1] Graz Univ Technol, Inst Theoret Comp Sci, Inffeldgasse 16b, Graz, Austria

来源：

NATURE COMMUNICATIONS | 2020年 / 11卷 / 01期

关键词：

PLASTICITY; BACKPROPAGATION; POWER; MODEL; TIME;

D O I：

10.1038/s41467-020-17236-y

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页数：15

共 58 条

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