A spiking network model of the cerebellum for predicting movements with diverse complex spikes.

Abstract

Smooth and coordinated motor control is believed to be achieved through prediction by forward models in the cerebellum, which generate predicted movements from motor commands. These models are acquired via supervised learning, where instruction signals, originating from the inferior olive and represented as complex spikes (CSs) in Purkinje cells, guide learning. Previous studies show that CSs represent a wide variety of motor- and nonmotor-related activities, but how this diversity contributes to forward model acquisition remains unclear. We hypothesized that predicted movements are learned through the combination of various types of CSs. To test this, we developed a spiking network model of the cerebellum as a supervised learning machine, using instruction signals based on Ca²⁺ imaging data from a self-initiated lever-pull task in mice. While individual signals did not fully represent lever movements, the combination of Purkinje cell activities, trained by different instruction signals, allowed neurons in the cerebellar nucleus to represent lever trajectory. Additionally, the same set of instruction signals trained the model to generate different movement trajectories. We further confirmed that a mouse musculoskeletal model successfully reproduced lever-pulling movements. These findings suggest that forward models in the cerebellum are achieved through a combination of diverse CSs with different spatiotemporal profiles, providing an over-complete basis for movement prediction.