Spiking neural network controllers evolved for animat foraging based on temporal pattern recognition in the presence of noise on input

Chama Bensmail; Volker Steuber; Neil Davey; Borys Wróbel

doi:10.1007/978-3-030-01418-6_30

Spiking neural network controllers evolved for animat foraging based on temporal pattern recognition in the presence of noise on input

Chama Bensmail, Volker Steuber, Neil Davey, Borys Wróbel

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

We evolved spiking neural network controllers for simple animats, allowing for these networks to change topologies and weights during evolution. The animats’ task was to discern one correct pattern (emitted from target objects) amongst other different wrong patterns (emitted from distractor objects), by navigating towards targets and avoiding distractors in a 2D world. Patterns were emitted with variable silences between signals of the same pattern in the attempt of creating a state memory. We analyse the network that is able to accomplish the task perfectly for patterns consisting of two signals, with 4 interneurons, maintaining its state (although not infinitely) thanks to the recurrent connections.

Original language	English
Title of host publication	Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings
Publisher	Springer Nature
Pages	304-313
Number of pages	10
ISBN (Print)	9783030014179
DOIs	https://doi.org/10.1007/978-3-030-01418-6_30
Publication status	E-pub ahead of print - 27 Sept 2018
Event	27th International Conference on Artificial Neural Networks, ICANN 2018 - Rhodes, Greece Duration: 4 Oct 2018 → 7 Oct 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11139 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	27th International Conference on Artificial Neural Networks, ICANN 2018
Country/Territory	Greece
City	Rhodes
Period	4/10/18 → 7/10/18

Keywords

Adaptive exponential integrate and fire
Animat
Spiking neural networks
Temporal pattern recognition

Access to Document

10.1007/978-3-030-01418-6_30Licence: Other

Cite this

Bensmail, C., Steuber, V., Davey, N., & Wróbel, B. (2018). Spiking neural network controllers evolved for animat foraging based on temporal pattern recognition in the presence of noise on input. In Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings (pp. 304-313). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11139 LNCS). Springer Nature . Advance online publication. https://doi.org/10.1007/978-3-030-01418-6_30

Bensmail, Chama ; Steuber, Volker ; Davey, Neil et al. / Spiking neural network controllers evolved for animat foraging based on temporal pattern recognition in the presence of noise on input. Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings. Springer Nature , 2018. pp. 304-313 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "We evolved spiking neural network controllers for simple animats, allowing for these networks to change topologies and weights during evolution. The animats{\textquoteright} task was to discern one correct pattern (emitted from target objects) amongst other different wrong patterns (emitted from distractor objects), by navigating towards targets and avoiding distractors in a 2D world. Patterns were emitted with variable silences between signals of the same pattern in the attempt of creating a state memory. We analyse the network that is able to accomplish the task perfectly for patterns consisting of two signals, with 4 interneurons, maintaining its state (although not infinitely) thanks to the recurrent connections.",

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author = "Chama Bensmail and Volker Steuber and Neil Davey and Borys Wr{\'o}bel",

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Bensmail, C, Steuber, V, Davey, N & Wróbel, B 2018, Spiking neural network controllers evolved for animat foraging based on temporal pattern recognition in the presence of noise on input. in Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11139 LNCS, Springer Nature , pp. 304-313, 27th International Conference on Artificial Neural Networks, ICANN 2018, Rhodes, Greece, 4/10/18. https://doi.org/10.1007/978-3-030-01418-6_30

Spiking neural network controllers evolved for animat foraging based on temporal pattern recognition in the presence of noise on input. / Bensmail, Chama; Steuber, Volker; Davey, Neil et al.
Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings. Springer Nature , 2018. p. 304-313 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11139 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Bensmail C, Steuber V, Davey N, Wróbel B. Spiking neural network controllers evolved for animat foraging based on temporal pattern recognition in the presence of noise on input. In Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings. Springer Nature . 2018. p. 304-313. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2018 Sept 27. doi: 10.1007/978-3-030-01418-6_30

Spiking neural network controllers evolved for animat foraging based on temporal pattern recognition in the presence of noise on input

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