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. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Oros, N., Steuber, V., Davey, N., Cañamero, L., & Adams, R. (2008). Adaptive olfactory encoding in agents controlled by spiking neural networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5040 LNAI, pp. 148–158). https://doi.org/10.1007/978-3-540-69134-1_15
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