Learning performance of cerebellar circuit depends on diversity and chaoticity of spiking patterns in granule cells: A simulation study

Abstract

The cerebellum, composed of numerous neurons, plays various roles in motor control. Although it is functionally subdivided, the cerebellar cortex has a canonical structural pattern in neuronal circuits including a recurrent circuit pattern formed by granule cells (GrCs) and Golgi cells (GoCs). The canonical circuital pattern suggests the existence of a fundamental computational algorithm, although it remains unclear. Modeling and simulation studies are useful for verifying hypotheses about complex systems. Previous models have shown that they could reproduced the neurophysiological data of the cerebellum; however, the dynamic characteristics of the system have not been fully elucidated. Understanding the dynamic characteristics of the circuital pattern is necessary to reveal the computational algorithm embedded in the circuit. This study conducted numerical simulations using the cerebellar circuit model to investigate dynamic characteristics in a simplified model of cerebellar microcircuits. First, the diversity and chaoticity of the patterns of spike trains generated from GrCs depending on the synaptic strength between the GrCs and GoCs were investigated based on cluster analysis and the Lyapunov exponent, respectively. Then the effect of synaptic strength on learning tasks was investigated based on the convergence properties of the output signals from Purkinje cells. The synaptic strength for high learning performance was almost consistent with that for the high diversity of the generated patterns and the edge of chaos. These results suggest that the learning performance of the cerebellar circuit depends on the diversity and the chaoticity of the spiking patterns from the GrC–GoC recurrent circuit.