Homeostatic bidirectional plasticity in Upbound and Downbound micromodules of the olivocerebellar system

Abstract

Olivocerebellar learning is highly adaptable, unfolding over minutes to weeks depending on the task. However, the stabilizing mechanisms of the synaptic dynamics necessary for ongoing learning remain unclear. We constructed a model to examine plasticity dynamics under stochastic input and investigate the impact of inferior olive (IO) reverberations on Purkinje cell (PCs) activity and synaptic plasticity. We explored Upbound and Downbound cerebellar micromodules, which are organized loops of IO neurons, cerebellar nuclei neurons and microzones of PCs characterized by their unique molecular profiles and different levels of baseline firing. Our findings show synaptic weight convergence followed by stability of synaptic weights. In line with their relatively low and high intrinsic firing, we observed that Upbound and Downbound PCs have a propensity for potentiation and depression, respectively, with both PC types reaching stability at differential levels of overall strength of their parallel-fiber (PF) inputs. The oscillations and coupling of IO neurons participating in the Upbound and Downbound modules determine at which frequency band PFs can be stabilized optimally. Our results indicate that specific frequency components drive IO resonance and synchronicity, which, in turn, regulate temporal patterning across Upbound and Downbound zones, orchestrating their plasticity dynamics. Author SummaryThe olivocerebellar system is a part of the brain that facilitates learning and controlling movements. To coordinate movements it integrates sensorimotor information with motor command signals. The resulting behaviour needs to be continuously adjusted during motor learning. The leading hypothesis is that changes in strength of the synaptic connections between neurons underlie the learning process. The challenge is to elucidate the factors that determine the exact timing and precision of the learned movements. To answer this question we developed a computational model of the two main types of modules of the olivocerebellar system that can control different types of movements in a bidirectional fashion. We found that the rhythm and coupling of the olivary neurons play an important role in controlling and stabilizing plasticity in the cerebellar cortex of both types of modules, together shaping learning-dependent timing of motor behaviour.