The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Supervised Learning Based on Temporal Coding in Spiking Neural Networks
Abstract
Gradient descent training techniques are remarkably successful in training analog-valued artificial neural networks (ANNs). Such training techniques, however, do not transfer easily to spiking networks due to the spike generation hard nonlinearity and the discrete nature of spike communication. We show that in a feedforward spiking network that uses a temporal coding scheme where information is encoded in spike times instead of spike rates, the network input-output relation is differentiable almost everywhere. Moreover, this relation is piecewise linear after a transformation of variables. Methods for training ANNs thus carry directly to the training of such spiking networks as we show when training on the permutation invariant MNIST task. In contrast to rate-based spiking networks that are often used to approximate the behavior of ANNs, the networks we present spike much more sparsely and their behavior cannot be directly approximated by conventional ANNs. Our results highlight a new approach for controlling the behavior of spiking networks with realistic temporal dynamics, opening up the potential for using these networks to process spike patterns with complex temporal information.
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Cites
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Cited by
Neftci, E. O., Mostafa, H., & Zenke, F. (2019) · IEEE Signal Processing Magazine · doi:10.1109/msp.2019.2931595 · Cited by 1,538
Self-citationScaling for edge inference of deep neural networks
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Exploring Neuromorphic Computing Based on Spiking Neural Networks: Algorithms to Hardware
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2022 roadmap on neuromorphic computing and engineering
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Metadata
- Authors
- Mostafa, H.
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2017
- Volume
- 29
- Issue
- 7
- Pages
- 1-9
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W2963335874
- Cited by
- 331
- References
- 29
- FWCI
- 10.96
- Type
- journal article
- Language
- en
- Open access
- closed
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