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SuperSpike: supervised learning in multilayer spiking neural networks

Abstract:

A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spiking neural circuits learn and compute in vivo, as well as how we can instantiate such capabilities in artificial spiking circuits in silico. Here we revisit the problem of supervised learning in temporally coding multilayer spiking neural networks. First, by using a surrogate gradient approach, we derive SuperSpike,...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1162/neco_a_01086

Authors


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Institution:
University of Oxford
Division:
MSD
Department:
Physiology Anatomy & Genetics
Role:
Author
More from this funder
Name:
Wellcome Trust
Funding agency for:
Zenke, F
Grant:
110124/Z/15/Z
More from this funder
Name:
Swiss National Science Foundation
Funding agency for:
Zenke, F
Grant:
110124/Z/15/Z
More from this funder
Name:
Office of Naval Research
Funding agency for:
Ganguli, S
More from this funder
Name:
James S. McDonnell foundations
Funding agency for:
Ganguli, S
More from this funder
Name:
Simons
Funding agency for:
Ganguli, S
Publisher:
Massachusetts Institute of Technology Press
Journal:
Neural Computation More from this journal
Volume:
30
Issue:
6
Pages:
1514-1541
Publication date:
2018-05-23
Acceptance date:
2018-01-23
DOI:
EISSN:
1530-888X
ISSN:
0899-7667
Pubs id:
pubs:865242
UUID:
uuid:22779ec3-6911-4135-8d09-67a56161fb7e
Local pid:
pubs:865242
Source identifiers:
865242
Deposit date:
2018-07-09

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