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 proceedingConference 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 languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings
PublisherSpringer Nature
Pages304-313
Number of pages10
ISBN (Print)9783030014179
DOIs
Publication statusE-pub ahead of print - 27 Sept 2018
Event27th International Conference on Artificial Neural Networks, ICANN 2018 - Rhodes, Greece
Duration: 4 Oct 20187 Oct 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11139 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference27th International Conference on Artificial Neural Networks, ICANN 2018
Country/TerritoryGreece
CityRhodes
Period4/10/187/10/18

Keywords

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

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