Training Spiking Neural Networks Using Lessons From Deep Learning

The brain is the perfect place to look for inspiration to develop more efficient neural networks. The inner workings of our synapses and neurons provide a glimpse at what the future of deep learning might look like. This article serves as a tutorial and perspective showing how to apply the lessons l...

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Published in:	Proceedings of the IEEE Vol. 111; no. 9; pp. 1016 - 1054
Main Authors:	Eshraghian, Jason K., Ward, Max, Neftci, Emre O., Wang, Xinxin, Lenz, Gregor, Dwivedi, Girish, Bennamoun, Mohammed, Jeong, Doo Seok, Lu, Wei D.
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.09.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Australia Back propagation Back propagation networks Biological neural networks Brain modeling Deep learning Distance learning Electronic learning neural code Neural networks neuromorphic Neuromorphics Neurons online learning Online tutorials spiking neural networks (SNNs) Synapses Training Tutorials
ISSN:	0018-9219, 1558-2256
Online Access:	Get full text
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Abstract	The brain is the perfect place to look for inspiration to develop more efficient neural networks. The inner workings of our synapses and neurons provide a glimpse at what the future of deep learning might look like. This article serves as a tutorial and perspective showing how to apply the lessons learned from several decades of research in deep learning, gradient descent, backpropagation, and neuroscience to biologically plausible spiking neural networks (SNNs). We also explore the delicate interplay between encoding data as spikes and the learning process; the challenges and solutions of applying gradient-based learning to SNNs; the subtle link between temporal backpropagation and spike timing-dependent plasticity; and how deep learning might move toward biologically plausible online learning. Some ideas are well accepted and commonly used among the neuromorphic engineering community, while others are presented or justified for the first time here. A series of companion interactive tutorials complementary to this article using our Python package, snnTorch, are also made available: https://snntorch.readthedocs.io/en/latest/tutorials/index.html .
AbstractList	The brain is the perfect place to look for inspiration to develop more efficient neural networks. The inner workings of our synapses and neurons provide a glimpse at what the future of deep learning might look like. This article serves as a tutorial and perspective showing how to apply the lessons learned from several decades of research in deep learning, gradient descent, backpropagation, and neuroscience to biologically plausible spiking neural networks (SNNs). We also explore the delicate interplay between encoding data as spikes and the learning process; the challenges and solutions of applying gradient-based learning to SNNs; the subtle link between temporal backpropagation and spike timing-dependent plasticity; and how deep learning might move toward biologically plausible online learning. Some ideas are well accepted and commonly used among the neuromorphic engineering community, while others are presented or justified for the first time here. A series of companion interactive tutorials complementary to this article using our Python package, snnTorch, are also made available: https://snntorch.readthedocs.io/en/latest/tutorials/index.html .
Author	Jeong, Doo Seok Dwivedi, Girish Lenz, Gregor Wang, Xinxin Lu, Wei D. Bennamoun, Mohammed Ward, Max Eshraghian, Jason K. Neftci, Emre O.
Author_xml	– sequence: 1 givenname: Jason K. orcidid: 0000-0002-5832-4054 surname: Eshraghian fullname: Eshraghian, Jason K. email: Jeshragh@ucsc.edu organization: Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA – sequence: 2 givenname: Max surname: Ward fullname: Ward, Max organization: Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA – sequence: 3 givenname: Emre O. orcidid: 0000-0002-0332-3273 surname: Neftci fullname: Neftci, Emre O. organization: Department of Computer Science and the Department of Cognitive Sciences, University of California at Irvine (UC Irvine), Irvine, CA, USA – sequence: 4 givenname: Xinxin orcidid: 0000-0003-0136-5936 surname: Wang fullname: Wang, Xinxin organization: Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA – sequence: 5 givenname: Gregor orcidid: 0000-0003-0619-3555 surname: Lenz fullname: Lenz, Gregor organization: SynSense AG, Zürich, Switzerland – sequence: 6 givenname: Girish orcidid: 0000-0003-0717-740X surname: Dwivedi fullname: Dwivedi, Girish organization: School of Medicine, The University of Western Australia, Crawley, WA, Australia – sequence: 7 givenname: Mohammed orcidid: 0000-0002-6603-3257 surname: Bennamoun fullname: Bennamoun, Mohammed organization: Department of Computer Science and Software Engineering, The University of Western Australia, Crawley, WA, Australia – sequence: 8 givenname: Doo Seok orcidid: 0000-0001-7954-2213 surname: Jeong fullname: Jeong, Doo Seok organization: Division of Materials Science and Engineering, Hanyang University, Seoul, South Korea – sequence: 9 givenname: Wei D. orcidid: 0000-0003-4731-1976 surname: Lu fullname: Lu, Wei D. organization: Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
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Snippet	The brain is the perfect place to look for inspiration to develop more efficient neural networks. The inner workings of our synapses and neurons provide a...
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SubjectTerms	Australia Back propagation Back propagation networks Biological neural networks Brain modeling Deep learning Distance learning Electronic learning neural code Neural networks neuromorphic Neuromorphics Neurons online learning Online tutorials spiking neural networks (SNNs) Synapses Training Tutorials
Title	Training Spiking Neural Networks Using Lessons From Deep Learning
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