Adaptive Olfactory Encoding in Agents Controlled by Spiking Neural Networks

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We created a neural architecture that can use two different types of information encoding strategies depending on the environment. The goal of this research was to create a simulated agent that could react to two different overlapping chemicals having varying concentrations. The neural network controls the agent by encoding its sensory information as temporal coincidences in a low concentration environment, and as firing rates at high concentration. With such an architecture, we could study synchronization of firing in a simple manner and see its effect on the agent’s behaviour.
Original languageEnglish
Pages (from-to)148-158
JournalLecture Notes in Computer Science (LNCS)
Issue numberFrom Animals to Animats 10
Publication statusPublished - 2008


  • neural encoding
  • firing rate
  • temporal coincidence


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