To develop and apply novel signal processing and machine learning algorithms to explain the high-dimensional structure and timecourse of neural population activity.
To apply this knowledge to the design of next-generation biomedical devices that interface with large populations of neurons.
intersection of signal processing / machine learning, biomedical engineering, and basic neuroscience.
“Our group seeks to elucidate how large populations of neurons process information, from encoding sensory stimuli to guiding motor actions. Most neurophysiological studies to date involve studying one neuron at a time. Although one neuron can be informative about the sensory stimulus or motor action, it often doesn't tell the full story. While this provides the motivation for looking across a neural population, the heterogeneity of the activity of different neurons can be baffling”
“Learning alters neural activity to simultaneously support memory and action” by D. M. Losey, J. A. Hennig†, E. R. Oby†, M. D. Golub, P. T. Sadtler, K. M. Quick, S. I. Ryu, E. C. Tyler-Kabara, A. P. Batista*, B. M. Yu*, S. M. Chase*. bioRxiv.
“Dimensionality reduction of calcium-imaged neuronal population activity” by T. Koh, W. E. Bishop, T. Kawashima, B. B. Jeon, R. Srinivasan, S. J. Kuhlman, M. B. Ahrens, S. M. Chase*, B. M. Yu*. bioRxiv.