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A cortical column model for multiscale spatial planning Louis-Emmanuel Martinet1,2

Summary: A cortical column model for multiscale spatial planning
Louis-Emmanuel Martinet1,2
and Angelo Arleo1
CNRS - UPMC Univ Paris 6, UMR 7102, F-75005, Paris, France
CNRS - UPMC Univ Paris 6, UMR 7222, F-75005, Paris, France
Abstract. An important issue in spatial memory is the learning of abstract rep-
resentations suitable for navigation planning. To address this problem, we have
already developed a planning system inspired by the columnar organization of
the mammalian cortex [1]. This model provides a neuromimetic architecture ca-
pable of learning topological spatial representations and planning goal-directed
actions. The work presented here deals with the ability to encode multiscale rep-
resentations of the environment, in order to solve large maze tasks. This is shown
by validating the model on a multiscale version of the Tolman & Honzik's detour
task [2]. Simulation results demonstrate that the performances of the planning
system are invariant with respect to the scale of the maze. A series of statistical
analyses is provided to characterise the neural activities subserving spatial plan-
ning. It is shown that the structural properties of the environment are encoded by


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


Collections: Biology and Medicine