Abstract
Spike-based reinforcement learning of navigation
Highlights
We have studied a spiking, reinforcement learning model derived from reward maximization [1,2] where causal relations between pre-and postsynaptic activity set a synaptic eligibility trace [2,3]
Neurons are modeled according to the "Integrate-and-Fire" model with escape noise
The simulated rat explores the environment in random search
Summary
We have studied a spiking, reinforcement learning model derived from reward maximization [1,2] where causal relations between pre-and postsynaptic activity set a synaptic eligibility trace [2,3]. Address: 1Laboratory of Computational Neuroscience, School of Computer and Communications Sciences and Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, CH-1015, Switzerland and 2Institute of Physiology, University of Bern, Buehlplatz 5, 3012
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