Computational characterization of cerebellum granule neuron responses to auditory and visual inputs

Abstract

The multimodal nature of sensory and tactile inputs to cerebellum is of significance for understanding brain function. Granule neuron properties in modifying auditory and visual stimuli was mathematically modeled in this study. Cerebellum granule neuron is a small electrotonically compact neuron and is among the largest number of neurons in the cerebellum. Granule neurons receives four excitatory inputs from four different mossy fibers. We mathematically reconstructed the firing patterns of both auditory and visual responses and decode the mossy fiber input patterns from both modalities. A detailed multicompartment biophysical model of granule neuron was used and in vivo behavior was modeled with short and long bursts. The cable compartmental model could reproduce input-output behavior as seen in real neurons to specific inputs. The response patterns reveal how auditory and visual patterns are encoded by the mossy fiber-granule cell relay and how multiple information modalities are processed by cerebellum granule neuron as responses of auditory and visual stimuli.