Computational Roles of Inhibitory Interneurons in a Cerebellum Spiking Neural Model of Adaptive Vestibulo-ocular-optokinetic Reflex

Project: ResearchGRF

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Description

Cerebellum is a powerful adaptive controller that mediates numerous functions that are critical for everyday life and even survival. Vestibulo-ocular reflex (VOR) and optokinetic reflex (OKR) are two important reflexive eye movement mechanisms that ensure a stable visual field during movement of the animal and/or the target, which is also a common task encountered in engineering control. They are adaptive since they need to compensate for the changes in the body and environment as well as the neural circuitry. Their adaptations are closely related to cerebellar function realized by its well-organized circuitry and numerous plasticity. Yet, the computational roles of molecular layer interneurons (MLI) in cerebellum-mediated learning and control have not been well-understood. Recent studies show that mutant mice with deleted MLI inhibition to Purkinje (PKJ) cells demonstrated deficits in VOR/OKR adaptation. In vitro studies showed that MLI to PKJ connections follow a specific spatial organization and are modulated by synaptic plasticity. However, a computational cerebellum model of VOR/OKR which can connect these findings to help address the roles of MLI over neural spiking to behavioural levels at a network scale is still lacking. In this project, we will investigate the computational roles of MLI by an integrated modeling and hardware testing approach. We will first develop a cerebellum spiking neural network model of VOR/OKR to investigate the impacts of (1) spatial organization of MLIR PKJ connection; and (2) plasticity at MLI synapses on PKJ firing pattern and VOR/OKR learning. How they affect neural firing patterns and consequently the behavior will be systematically characterized in the simulations. Second, how MLI may affect behavior as the cerebellum model operates in closed-loop with the environment will be investigated with a robot system. We will develop a real-time hardware system to implement the cerebellum model for controlling a mobile robot performing target stabilization task mimicking VOR/OKR. The hardware simulation system will be optimized for the cerebellum model and be modularized to facilitate future extension. The robot testing allows evaluation of robustness of the cerebellum model under influence of MLI in the presence of disturbance in the physical systems and communication delay. These results will also provide evidence of the potential functional advantages of the cerebellum controller. Together, this project will advance our understanding of the computational mechanism in cerebellum, which will provide new insight to reverse engineer our brain as well as to develop more adaptive and robust robot control. ?

Detail(s)

StatusActive
Effective start/end date1/01/18 → …