Speaker: Claudia Clopath (Columbia University)
Title: Modeling cerebellar learning
When: Tuesday, March 27, 2012
Time: 11:30AM
Where: Simons Center, Lecture Hall 102
Abstract:
Purkinje cells (PCs) of the cerebellar cortex have long been considered to perform similarly as perceptrons: Given an input pattern in the granular layer, they should learn to provide an adequate motor output, thanks to plasticity of the parallel fiber (PF) to PC synapses, under the supervision of the climbing fiber input which is assumed to carry an error signal (Marr 1969, Albus 1971). Supervised learning in the perceptron model has been studied extensively in the case of random uncorrelated input/output associations. In particular, it is known that when synapses are constrained to be positive (to account for the fact that PF-PC synapses are excitatory), the synaptic weight distribution at maximal storage capacity is composed of a large fraction of zero-weight synapses (‘silent’ synapses, Brunel et al. 2004). However, in the case of the cerebellum, the assumption of uncorrelated inputs and outputs is clearly unrealistic, as any naturalistic inputs/motor sequences will carry some substantial degree of temporal correlations. We therefore investigated both the capacity and the optimal connectivity in feed-forward networks learning associations between temporally correlated input/output sequences. We then consider a bistable output to mimic the postulated bistability of the PC (Yartsev et al. 2009, Loewenstein et al. 2005, Williams et al. 2002, Oldfield et al. 2010). We show that bistability can increase capacity, when the output correlation is bigger than the input correlation. Moreover, the weight distribution of the PF-PC synapses consists in any case of a large number of silent synapses and does not depend on the level of correlations.