Supervised Learning in Multilayer Spiking Neural Networks.
Sporea, Ioana and Gruning, Andre (2013) Supervised Learning in Multilayer Spiking Neural Networks. Neural Computation, 25 (2), 2. pp. 473-509.
|
Text
sporea_neco_a_00396.pdf Available under License : See the attached licence file. Download (763kB) | Preview |
|
|
Text (licence)
SRI_deposit_agreement.pdf Available under License : See the attached licence file. Download (33kB) | Preview |
Abstract
We introduce a supervised learning algorithm for multilayer spiking neural networks. The algorithm overcomes a limitation of existing learning algorithms: it can be applied to neurons firing multiple spikes in artificial neural networks with hidden layers. It can also, in principle, be used with any linearizable neuron model and allows different coding schemes of spike train patterns. The algorithm is applied successfully to classic linearly nonseparable benchmarks such as the XOR problem and the Iris data set, as well as to more complex classification and mapping problems. The algorithm has been successfully tested in the presence of noise, requires smaller networks than reservoir computing, and results in faster convergence than existing algorithms for similar tasks such as SpikeProp.
Item Type: | Article | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Divisions : | Faculty of Engineering and Physical Sciences > Computer Science | |||||||||
Authors : |
|
|||||||||
Date : | 2013 | |||||||||
DOI : | 10.1162/NECO_a_00396 | |||||||||
Copyright Disclaimer : | Copyright 2013 Massachusetts Institute of Technology | |||||||||
Related URLs : | ||||||||||
Additional Information : | Available Neural Computation, 25, 473-509, http://www.mitpressjournals.org/doi/abs/10.1162/NECO_a_00396#.VGs1GvmsV8E © 2013 The MIT Press | |||||||||
Depositing User : | Symplectic Elements | |||||||||
Date Deposited : | 18 Nov 2014 12:02 | |||||||||
Last Modified : | 06 Jul 2019 05:14 | |||||||||
URI: | http://epubs.surrey.ac.uk/id/eprint/806443 |
Actions (login required)
![]() |
View Item |
Downloads
Downloads per month over past year