Cerebellar Micro Complex Model Using Histologic Boolean Mapping Simulates Adaptive Motor Control.

Abstract

Despite extensive cerebellar research, the functional role of individual cerebellar micro complexes (CmCs) in motor coordination remains debated. This study aimed to utilise a reductionist approach to model the CmC function in motor control using the Histologic Boolean Mapping (HBM-VNR) framework and validate it through replication of features observed in the literature. HBM-VNR modelled each neuron within the CmC as a Boolean expression derived from its architectural connectivity. The model incorporates the Variable Neuronal Response (VNR) synaptic model, introducing probabilistic post-synaptic firing to reflect physiological variability. Motor control dynamics follow the cerebellar brain inhibition phenomenon, where Deep Cerebellar Nucleus (DCN) firing activates the antagonist muscles. The model performed the task of feedback-control in an idealised joint following a desired sinusoidal position. HBM-VNR produced a minimalistic model that reproduced adaptive compensation to external forces and predicted intention tremor when CmC population was reduced, and the expected ethanol induced motor impairments. Simulated firing patterns of the DCN and Purkinje cell showed patterns resembling real recordings both in physiological and pathological situations. The Shifting Central Frequency Hypothesis (SCFH) was suggested to explain the CmC comparator functionality. This study presents HBM-VNR as a histologically grounded modelling approach for neural circuits. HBM-VNR simulated adaptive motor control and predicted neocerebellar syndrome symptomatology and alcohol intoxication effects. SCFH offers a computational mechanism consistent with the cerebellar internal model theories and places CmC as the basis for motor learning in line with the literature, positioning HBM-VNR as a scalable framework for neuroanatomical modelling.