Computational Motor Neuroscience Lab

To understand underlying neural mechanisms of learning and memory, we take a combined approach of computational modeling, behavioral experiments, neuroimaging, and neuromodulation. We are interested in two distinct memory systems, which are procedural and declarative memory, and how these two systems interact. Building on these scientific findings, we investigate the development of efficient learning protocols applicable to the rehabilitation of patients with stroke and Parkinsonism. The DeepDraw project investigates the Humans sensory-motor control system by combining the explainability of MRI neuroimaging and the computational power of task driven deep neural networks. The overall goal of this project is to compare brain activity and neural network model activity in a sequential single letter writing task to explore the eventual hierarchical composition of proprioception and motor control in humans. The current study aims to apply AI to understand the neural basis of human motor control. For this, we first create an fMRI experiment in which human participants track a moving target using a joystick in the scanner. Next, we developed a deep reinforcement learning (DRL) model simulating the same motor task performed by the human participants. The model takes raw pixel inputs from images and generates continuous motor action to account for the entire cognitive process of humans, from perception to action. Specifically, it combines a convolutional neural network (CNN) and a twin-delayed deep deterministic policy gradient algorithm (TD3). We successfully trained the model simulating the human motor control and then tested whether the trained model predicts the neural responses. We used the Voxel-wise Encoding Model (VM) and demonstrated that the internal representations of