Nguyen, Huy Le, Chu, Dominique (2023) Incremental Neural Synthesis for Spiking Neural Networks. In: 2022 IEEE Symposium Series on Computational Intelligence (SSCI). 2022 IEEE Symposium Series On Computational Intelligence. . IEEE (doi:10.1109/ssci51031.2022.10022275) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:97546)
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Official URL: https://doi.org/10.1109/ssci51031.2022.10022275 |
Abstract
We present an iterative neural synthesis approach to train Convolutional Spiking Neural Networks for classification problems. Unlike previous neural synthesis methods which primarily compute the neuron firing rates, our method is designed to compute multiple spikes at arbitrary timings. As such, our approach is directly applicable to spatio-temporal problems using spiking network models. In our approach, each weight update is formulated as a linear Constraint Satisfaction Problem, which can then be solved using existing numerical techniques. On the MNIST, EMNIST, and ETH-80 image classification benchmarks, our approach demonstrates competitive with other models in the literature, while requiring relatively few training samples to converge to a good solution.
Item Type: | Conference or workshop item (Paper) |
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DOI/Identification number: | 10.1109/ssci51031.2022.10022275 |
Additional information: | “© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.” |
Uncontrolled keywords: | Spiking Neural Network, Supervised Learning, Unsupervised Learning, Classification |
Subjects: | Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.87 Neural computers, neural networks |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing |
Funders: | University of Kent (https://ror.org/00xkeyj56) |
Depositing User: | Huy Nguyen |
Date Deposited: | 22 Oct 2022 10:20 UTC |
Last Modified: | 10 Feb 2023 14:21 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/97546 (The current URI for this page, for reference purposes) |
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