Robert E. Kass, Ph.D.
Professor and Department Head
Department of Statistics, Carnegie Mellon University
Center for the Neural Basis of Cognition
Carnegie Mellon University-University of Pittsburgh
Pittsburgh, Pennsylvania U.S.A.

One of the most important techniques in learning about brain function 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 a fundamental question in neuroscience is precisely how this is accomplished, that is, what physiological significance should be attached to a particular neuron firing pattern. My colleagues and I have framed scientific questions in terms of point process intensity functions, and have used Bayesian methods to fit the point process models to neuronal data.

I will describe the neurophysiological setting, and then use it as background to discuss a general approach to curve fitting with free-knot splines and reversible-jump MCMC, which may be applied in the point process setting.  With this analytical foundation in place I will outline the progress we’ve made and the substantive problems we hope to address in the next few years.