Kozdon, KW;
Bentley, P;
(2018)
The Evolution of Training Parameters for Spiking Neural Networks with Hebbian Learning.
In: Ikegami, T and Virgo, N and Witkowski, O and Oka, M and Suzuki, R and Iizuka, H, (eds.)
ALIFE 2018: The 2018 Conference on Artificial Life.
(pp. pp. 276-283).
MIT Press
Preview |
Text
Kozdon, Bentley 2018 The Evolution of Training Parameters for Spiking Neural Networks with Hebbian Learning.pdf - Accepted Version Download (1MB) | Preview |
Abstract
Spiking neural networks, thanks to their sensitivity to the timing of the inputs, are a promising tool for unsupervised processing of spatio-temporal data. However, they do not perform as well as the traditional machine learning approaches and their real-world applications are still limited. Various supervised and reinforcement learning methods for optimising spiking neural networks have been proposed, but more recently the evolutionary approach regained attention as a tool for training neural networks. Here, we describe a simple evolutionary approach for optimising spiking neural networks. This is the first published use of evolutionary algorithm to develop hyperparameters for fully unsupervised spike-timing- dependent learning for pattern clustering using spiking neural networks. Our results show that combining evolution and unsupervised learning leads to faster convergence on the optimal solutions, better stability of fit solutions and higher fitness of the whole population than using each approach separately.
| Type: | Proceedings paper |
|---|---|
| Title: | The Evolution of Training Parameters for Spiking Neural Networks with Hebbian Learning |
| Event: | ALIFE 2018: The 2018 Conference on Artificial Life, 23-27 July 2018, Tokyo, Japan |
| Location: | Tokyo |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1162/isal_a_00055 |
| Publisher version: | https://doi.org/10.1162/isal_a_00055 |
| Language: | English |
| Additional information: | © 2018 Massachusetts Institute of Technology. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license (https://creativecommons.org/licenses/by/4.0/). |
| UCL classification: | UCL 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/10054192 |
Loading...
Loading...Loading...Loading...Archive Staff Only
 |
View Item |
