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Neurocomputing 69 (2006) 12111214 Adaptive sensory processing for efficient place coding

Summary: Neurocomputing 69 (2006) 1211­1214
Adaptive sensory processing for efficient place coding
Denis SheynikhovichĂ, Ricardo Chavarriaga, Thomas Stro¨ sslin, Wulfram Gerstner
Laboratory of Computational Neuroscience, EPFL, CH-1015 Lausanne, Switzerland
Available online 2 February 2006
This work presents a neural model of self-localisation implemented on a simulated mobile robot with a realistic visual input. A
population of modelled place cells with overlapping receptive fields is constructed online during exploration. In contrast to similar
models of place cells, parameters of neurons in the sensory pathway adapt online to the environments statistics in order to maximise
information transmission. The robot's position can be decoded from the population activity with high accuracy. The information
transmission rate of the cells is comparable to the information rate of biological place cells.
r 2006 Elsevier B.V. All rights reserved.
Keywords: Self-localisation; Information maximisation; Place cells
1. Introduction
A large body of experimental data suggests that rats are
able to build a spatial representation of the environment
they are located in. Such a representation may reside in
spatially tuned neurons (i.e. place cells) found in the rat
hippocampus [7]. External sensory input (primarily visual)
plays a major role in controlling the formation of such a


Source: Arleo, Angelo - Laboratory of Neurobiology of Adaptive Processes, Université Pierre-et-Marie-Curie, Paris 6


Collections: Biology and Medicine