A Neurorobotics Model of the Cerebellar-Basal Ganglia Circuitry: decision making and motor control in healthy and diseased states

The neural circuitry underlying decision making and motor control is remarkably complex, with several specialised brain structures interacting in non intuitive feedback loops. Nevertheless, animals excel when faced with conflicting decisions, providing rapid and precise motor responses. To further understand how this is possible, computational models that resemble neurobiological aspects found in animals have been proposed. In this talk, I'll first describe our data-driven spiking neural network model of the basal ganglia-thalamus-cortex (BG-T-C) network based on simultaneous electrophysiological recordings from seven brain regions of Marmoset monkeys, including healthy animals and animal models of Parkinson's Disease. Next, this model is expanded to include the recently uncovered pathway between the cerebellum and the BG-T-C. This integrated model is then embedded into a neurorobotics model and used to predict and adjust the motion of the hands of a robot in real time. Overall, we believe that our proposed neurorobotics model is a further step towards more efficient robotics controllers.

Short Bio

Dr. Renan C. Moioli is an assistant professor at the Digital Metropolis Institute (IMD) from the Federal University of Rio Grande do Norte, Brazil, where he is also part of the Graduate Program in Bioinformatics. He has a D. Phil. in Cognitive Science from the University of Sussex (2013), UK, working at the Centre for Computational Neuroscience and Robotics (CCNR). From 2013 to 2018 he was a research fellow at the Edmond and Lily Safra International Institute of Neuroscience, Brazil, working on brain-machine interfaces. From 2019 to 2022, he was a UK Royal Society Newton Advanced Fellow, working in close collaboration with Heriot-Watt's Robotics Laboratory. His research interests are in the fields of computational intelligence and bioinformatics.