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
(e.g., see Churchland and Shenoy,
2007 and Machens
et al., 2010).|
We have two major aims:
(1) To develop and apply novel signal processing and machine learning algorithms to explain the high-dimensional structure and timecourse of neural population activity.
(2) To apply this knowledge to the design of next-generation biomedical devices that interface with large populations of neurons in increasingly sophisticated ways.
The work is at the intersection of signal processing / machine learning, biomedical engineering, and basic neuroscience.