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My lab applies the techniques of probabilistic modeling and machine learning to neural and behavioural science in two ways. First, we study the theoretical principles that underlie the problem of perception, or to a lesser extent motor control, and relate our results to published behavioural and neurophysiological findings. We also test the predictions of these theories in our own behavioural experiments, or collaborate with others to gather suitable physiological data. Second, we design new ways to discover important structure in neural data, focusing particularly on the growing field of multichannel spike-level electrophysiological or optical recordings.
1. How are sensory expectations about the environment formed; how are violations of these expectations detected; and/or how does the resultant attentional redirection affect behavioural report?
2. What processes lead to coupling in the activity of cortical neurons; how might these be detected and reconstructed from simultaneously recorded spike data?
3. How are statistical regularities in natural environmental sounds reflected in their perception and/or neural representation?
A. Afshar, G. Santhanam, B. M. Yu, S. I. Ryu, M. Sahani, and K. V.
Single-trial neural correlates of arm movement preparation.
Neuron, 71(3):555-564, 2011.
M. B. Ahrens and
Observers exploit stochastic models of sensory change to help judge the passage of time.
Current Biology, 21(3):200-206, 2011.
P. Berkes, R. E.
Turner, and M. Sahani.
A structured model of video reproduces primary visual cortical organisation.
PLoS Computational Biology, 5(9):e1000495, 2009.