Kozdon, K;
Bentley, P;
(2018)
Wide learning: Using an ensemble of biologically-plausible spiking neural networks for unsupervised parallel classification of spatio-temporal patterns.
In: Bonissone, PP and Fogel, D, (eds.)
2017 IEEE Symposium Series on Computational Intelligence (SSCI).
(pp. pp. 3183-3190).
IEEE
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Abstract
Spiking neural networks have been previously used to perform tasks such as object recognition without supervision. One of the concerns relating to the spiking neural networks is their speed of operation and the number of iterations necessary to train and use the network. Here, we propose a biologically plausible model of a spiking neural network which is used in multiple, separately trained copies to process subsets of data in parallel. This ensemble of networks is tested by applying it to the task of unsupervised classification of spatio-temporal patterns. Results show that despite different starting weights and independent training, the networks produce highly similar spiking patterns in response to the same class of inputs, enabling classification with fast training time.
| Type: | Proceedings paper |
|---|---|
| Title: | Wide learning: Using an ensemble of biologically-plausible spiking neural networks for unsupervised parallel classification of spatio-temporal patterns |
| Event: | IEEE Symposium Series on Computational Intelligence (IEEE SSCI), 27 November - 1 December 2017, Honolulu, Hawaii, US |
| Location: | Honolulu, HI |
| Dates: | 27 November 2017 - 01 December 2017 |
| ISBN-13: | 9781538627273 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/SSCI.2017.8285167 |
| Publisher version: | https://doi.org/10.1109/SSCI.2017.8285167 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | spiking neural network; spike timing dependent plasticity; pattern detection; parallel computing; ensemble network |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10047812 |
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