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Evolving Probabilistic Spiking Neural Networks for Spatio-temporal Pattern Recognition


Kasabov, N; Dhoble, K; Nuntalid, N; Mohemmed, A (2011). Evolving Probabilistic Spiking Neural Networks for Spatio-temporal Pattern Recognition. In: 18th International Conference on Neural Information Processing (ICONIP 2011), Shanghai, China, 13 November 2011 - 17 November 2011, 230-239.

Abstract

This paper proposes a novel architecture for continuous spatio-temporal data modeling and pattern recognition utilizing evolving probabilistic spiking neural network ‘reservoirs’ (epSNNr). The paper demonstrates on a simple experimental data for moving object recognition that: (1) The epSNNr approach is more accurate and flexible than using standard SNN; (2) The use of probabilistic neuronal models is superior in several aspects when compared with the traditional deterministic SNN models, including a better performance on noisy data.

Abstract

This paper proposes a novel architecture for continuous spatio-temporal data modeling and pattern recognition utilizing evolving probabilistic spiking neural network ‘reservoirs’ (epSNNr). The paper demonstrates on a simple experimental data for moving object recognition that: (1) The epSNNr approach is more accurate and flexible than using standard SNN; (2) The use of probabilistic neuronal models is superior in several aspects when compared with the traditional deterministic SNN models, including a better performance on noisy data.

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3 citations in Web of Science®
6 citations in Scopus®
10 citations in Microsoft Academic
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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Uncontrolled Keywords:Spatio-Temporal Patterns, Spiking Neural Network, Reservoir Computing, Liquid State Machine
Language:English
Event End Date:17 November 2011
Deposited On:09 Mar 2012 14:58
Last Modified:23 Sep 2018 07:04
Publisher:Springer
Series Name:Lecture notes in computer science
Number:7064/2011
Number of Pages:9
ISSN:0302-9743
ISBN:978-3-642-24964-8;978-3-642-24965-5
OA Status:Closed
Publisher DOI:https://doi.org/10.1007/978-3-642-24965-5_25

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