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Modeling cerebellar learning for spatial cognition Jean-Baptiste Passot
 

Summary: Modeling cerebellar learning for spatial cognition
Jean-Baptiste Passot
1,2
, Laure Rondi-Reig
1,2
, Angelo Arleo
1,2
1 UPMC Univ Paris 6, UMR 7102, F-75005 Paris, France
2 CNRS, UMR 7102, F-75005 Paris, France
E-mail: jean-baptiste.passot@upmc.fr
Recent experimental findings have begun to unravel the implication of the cerebellum in high-level functions such
as spatial cognition [1,2]. We focus on behavioural genetic data showing that L7-PKCI mice (lacking LTD at
parallel fibres­Purkinje cell synapses) have a spatial learning impairment in the Morris Watermaze (MWM),
whereas they exhibit normal performances in the Starmaze, a paradigm that reduces the procedural demand of the
task [3]. These results suggest that cerebellar learning may prominently contribute to the procedural component of
spatial learning [3].
We model the main information processing components of the cerebellar microcomplex via a large-scale
network of spiking neurons. We test the performances of artificial L7-PKCI mice in simulated MWM and Starmaze
environments. Importantly, we isolate the purely procedural component of the learning process by endowing
simulated controls and mutants with identical declarative learning capabilities. The model reproduces most of the

  

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

 

Collections: Biology and Medicine