University of Hertfordshire

By the same authors

Standard

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; Wróbel, Borys.

Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings. Springer Verlag, 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 proceedingConference contribution

Harvard

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 Verlag, 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

APA

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 Verlag. https://doi.org/10.1007/978-3-030-01418-6_30

Vancouver

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 Verlag. 2018. p. 304-313. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-01418-6_30

Author

Bensmail, Chama ; Steuber, Volker ; Davey, Neil ; Wróbel, Borys. / 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 Verlag, 2018. pp. 304-313 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

Bibtex

@inproceedings{d8d3a9cd2a414b55b822b80ea77e7948,
title = "Spiking neural network controllers evolved for animat foraging based on temporal pattern recognition in the presence of noise on input",
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.",
keywords = "Adaptive exponential integrate and fire, Animat, Spiking neural networks, Temporal pattern recognition",
author = "Chama Bensmail and Volker Steuber and Neil Davey and Borys Wr{\'o}bel",
note = "{\circledC} Springer Nature Switzerland AG 2018",
year = "2018",
month = "9",
day = "27",
doi = "10.1007/978-3-030-01418-6_30",
language = "English",
isbn = "9783030014179",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "304--313",
booktitle = "Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings",
address = "Germany",

}

RIS

TY - GEN

T1 - Spiking neural network controllers evolved for animat foraging based on temporal pattern recognition in the presence of noise on input

AU - Bensmail, Chama

AU - Steuber, Volker

AU - Davey, Neil

AU - Wróbel, Borys

N1 - © Springer Nature Switzerland AG 2018

PY - 2018/9/27

Y1 - 2018/9/27

N2 - 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.

AB - 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.

KW - Adaptive exponential integrate and fire

KW - Animat

KW - Spiking neural networks

KW - Temporal pattern recognition

UR - http://www.scopus.com/inward/record.url?scp=85054813240&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-01418-6_30

DO - 10.1007/978-3-030-01418-6_30

M3 - Conference contribution

SN - 9783030014179

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 304

EP - 313

BT - Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings

PB - Springer Verlag

ER -