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\centerline{\bf STANFORD UNIVERSITY}
\centerline{\bf DEPARTMENT OF STATISTICS}
\centerline{\big DEPARTMENTAL SEMINAR}
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\centerline{4:15 p.m., Tuesday, March 8, 2005}
\centerline{Sequoia Hall Room 200}
\centerline{(Cookies at 3:45 in 1st Floor Lounge)}

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\centerline{\sl Robert E. Kass}
\centerline{Department of Statistics and Center for the Neural Basis of Cognition}
\centerline{Carnegie Mellon University}
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\centerline{\bf Some Statistical Issues in the Analysis of Neuronal Data}
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Abstract:

One of the most important techniques in learning about the functioning
of the brain has involved examining neuronal activity in laboratory
animals under varying experimental conditions.  Neural information is
represented and communicated through series of action potentials, or
spike trains, and the central scientific issue in many studies
concerns the physiological significance that should be attached to a
particular neuron firing pattern in a particular part of the brain. In
addition, a major relatively new effort in neurophysiology involves
the use of multielectrode recording, in which responses from dozens of
neurons are recorded simultaneously. Among other things, this has
made possible the construction of brain-controlled robotic devices,
which could benefit people whose movement has been severely impaired.

My colleagues and I have formalized specific scientific questions in
terms of point process intensity functions, and have used Bayesian
methods to fit the point process models to neuronal data (though we
sometimes prefer simple smoothers and the Bootstrap).  In my talk I
will outline the substantive problems we are examining and the
progress we've made.

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