Evolving Spiking Neural Networks to Control Animats for Temporal Pattern Recognition and Foraging

Chama Bensmail, Volker Steuber, Neil Davey, Borys Wrobel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)
37 Downloads (Pure)


We evolved spiking neural networks (SNNs) to control animats in a task requiring temporal pattern recognition and foraging in a 2D environment with two types of objects emitting patterns: a target and a distractor. The target emits a specific temporal pattern composed of two components, while the distractor emits random patterns that are all the other combinations of these two components. The fitness function rewarded finding targets and avoiding distractors. We show that the evolved animats are robust to changes of the number of objects in the environment, strength of the actuators, duration of signals, intervals between signals in the pattern and between patterns. Our long term goal is to understand the mechanisms governing the neural networks that accomplish simple but not trivial computational tasks inspired by minimally cognitive abilities of animals, such as phonotaxis.
Original languageEnglish
Title of host publication2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
ISBN (Electronic)978-1-5386-2726-6
ISBN (Print)978-1-5386-2727-3
Publication statusPublished - 8 Feb 2018


  • adaptive exponential (AdEX) integrate-and-fire neuron
  • animat control
  • artificial evolution
  • complex networks
  • evolutionary algorithm
  • spiking neural networks
  • temporal pattern recognition


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