Title:Neuroengineering Approaches for Mapping Cortico-Basal Ganglia Neural Dynamics onto Reward-Guided Decision Processes.

The investigation of reward learning and decision-making often focuses on the basal ganglia network, a group of subcortical nuclei including the dorsomedial striatum (DMS), globus pallidus externus (GPe), and substantia nigra reticulata (SNr). The basal ganglia receive inputs from the cortex and thalamus, which are gated by the DMS and further processed through interactions with the GPe and SNr. The SNr, in turn, provides basal ganglia feedback to thalamus and cortex for refining motor function and decisions. However, our understanding of real-time interactions within the cortico-basal ganglia network in mediating complex behavior is far from complete. Traditional linear neural recording methods restrict our ability to fully explore this question, due to limited coverage of the entire network and significant tissue damage. To overcome these challenges, I propose the use of a novel flexible neural probe called the C-probe, innovated by the Lin lab. This C-probe is designed to record neural activity along the non-linear cortex-DMS-GPe-SNr axis (Aim1), enabling the examination of top-down neural encoding structures in decision-making across the entire network. Decision-making is thought to be influenced by both long and short-timescale plastic connections between cortical and striatal synapses. While short-term corticostriatal plasticity allows for rapid, trial-by-trial updating, long-term plasticity shapes basal neural network connectivity and tunes learning capacity. The mechanisms by which long-term corticostriatal plasticity shapes short-term striatal dynamics for trial-and-error learning remain unclear. To address this, I will collaborate with Dr. Lin to integrate advanced optogenetic techniques with a state-of-the-art optoelectronic tissue-like injectable mesh probe, known as the OPTIM probe, to rewire corticostriatal connectivity and evaluate the consequent impact on neurocomputations (Aim 2). This research will not only pioneer neuroengineering approaches for mapping complex network dynamics onto learning and decision processes but also shed light on the neurocomputational role of the cortico-basal ganglia network in decision-making.