Supervised Learning in Spiking Neural Networks with FORCE Training

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Title: Supervised Learning in Spiking Neural Networks with FORCE Training
Authors: Nicola, W
Clopath, C
Item Type: Journal Article
Abstract: Populations of neurons display an extraordinary diversity in the behaviors they affect and display. Machine learning techniques have recently emerged that allow us to create networks of model neurons that display behaviors of similar complexity. Here we demonstrate the direct applicability of one such technique, the FORCE method, to spiking neural networks. We train these networks to mimic dynamical systems, classify inputs, and store discrete sequences that correspond to the notes of a song. Finally, we use FORCE training to create two biologically motivated model circuits. One is inspired by the zebra finch and successfully reproduces songbird singing. The second network is motivated by the hippocampus and is trained to store and replay a movie scene. FORCE trained networks reproduce behaviors comparable in complexity to their inspired circuits and yield information not easily obtainable with other techniques, such as behavioral responses to pharmacological manipulations and spike timing statistics.
Issue Date: 20-Dec-2017
Date of Acceptance: 19-Oct-2017
ISSN: 2041-1723
Publisher: Nature Publishing Group
Journal / Book Title: Nature Communications
Volume: 8
Copyright Statement: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit licenses/by/4.0/. © The Author(s) 2017
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
The Leverhulme Trust
The Royal Society
Wellcome Trust
Biotechnology and Biological Sciences Research Council (BBSRC)
Biotechnology and Biological Sciences Research Cou
Funder's Grant Number: EP/M019780/1
ORCA 64155 (BB/N013956/1)
Keywords: q-bio.NC
MD Multidisciplinary
Publication Status: Published
Article Number: 2208
Appears in Collections:Faculty of Engineering

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