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  Reservoir computing with self-organizing neural oscillators

Khajehabdollahi, S., Giannakakis, E., Prosi, J., & Levina, A. (2021). Reservoir computing with self-organizing neural oscillators. In Artificial Life Conference Proceedings (pp. 1-3). MIT Press. doi:10.1162/isal_a_00409.

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 Creators:
Khajehabdollahi, S, Author           
Giannakakis, E1, 2, Author           
Prosi, J1, 2, Author           
Levina, A1, 2, Author           
Affiliations:
1Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_3017468              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 Abstract: Reservoir computing is a powerful computational framework that is particularly successful in time-series prediction tasks. It utilises a brain-inspired recurrent neural network and allows biologically plausible learning without backpropagation. Reservoir computing has relied extensively on the self-organizing properties of biological spiking neural networks. We examine the ability of a rate-based network of neural oscillators to take advantage of the self-organizing properties of synaptic plasticity. We show that such models can solve complex tasks and benefit from synaptic plasticity, which increases their performance and robustness. Our results further motivate the study of self-organizing biologically inspired computational models that do not exclusively rely on end-to-end training.

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 Dates: 2021-07
 Publication Status: Published online
 Pages: 3
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1162/isal_a_00409
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Title: ALIFE 2021: The 2021 Conference on Artificial Life
Place of Event: Praha, Czech Republic
Start-/End Date: 2021-07-19 - 2021-07-23

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Title: Artificial Life Conference Proceedings
Source Genre: Proceedings
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Publ. Info: MIT Press
Pages: - Volume / Issue: 2021 Sequence Number: 78 Start / End Page: 1 - 3 Identifier: -