Realistic modeling applied to cerebellar function

We used realistic simulation approaches, based on fully active compartmental models, to study the cerebellum. The author summarizes some of the recent results that address cerebellar learning. Assuming that the long term depression (LTD) is the basis of learning in cerebellar Purkinje cells, two questions arise: what is a parallel fiber pattern and how can the occurrence of learned patterns be decoded from the Purkinje cell spike train? We applied network simulations of the granular layer using conductance based models of granule and Golgi cells to study the patterns of the parallel fiber activity in response to natural mossy fiber stimulation. Based on these simulations we predicted a sparse temporal coding by parallel fibers of complex spatial maps of mossy fiber input. We studied the pattern recognition by Purkinje cells in a realistic context, i.e. how to recognize the effect of depressed synapses on a spiking neuron which is spontaneously active most of the time. Using our standard Purkinje cell model we predict that parallel fiber patterns that have undergone LTD cause an increase in the Purkinje cell output instead of the decrease assumed by most cerebellar learning theories