Principal investigator Devika Narain
E-mail address firstname.lastname@example.org
Neural control of movements using brain machine interfaces
Several years ago, we discovered a novel neural code that hints at a neural mechanism for controlling the tempo of movements i.e. whether you deliberately play a music composition slowly or quickly or strike a tennis return hard or not. A major undertaking in our lab is building neural interfaces to utilize and manipulate this code with the long-term goal of restoring movement control in patients. In this project, we will use a combination of closed-loop engineering and systems neuroscience to collect and analyze neurophysiological data from rodent brain-machine interfaces.
- Machine learning
- Closed-loop engineering
Wang, J., et al. (2018). Nature Neuroscience, 21, 102-110. DOI: 10.1038/s41593-017-0028-6.
Implicit and explicit learning as a window into interareal brain communications
As explained in the recent book ‘Thinking, Fast and Slow’, in cognitive neuroscience, it has long been maintained that the acquisition of implicit processes is automatic and rapid, whereas, learning of explicit processes is believed to require more effort and is slower. Interestingly, exactly the opposite is true in motor neuroscience where learning of implicit processes is known to be slower than explicit learning. Here we report that in time perception, which often straddles cognitive and motor learning, implicit timing is learned more rapidly than explicit temporal metrics. This project entails the analysis of existing neurophysiological data underlying these behaviors. If interested, students can also be trained on electrophysiological techniques and experimental procedures (optional).
- Machine learning
- Dimensionality reduction
- Optional: electrophysiology, surgery and behavioral training
Ma, Q., et al. (2023). Social cognitive and affective neuroscience, 18(1), nsac044. DOI: 10.1093/scan/nsac044.