Robert E. Kass

Department of Statistics and Center for the Neural Basis of Cognition Carnegie Mellon University

Neuron Firing Patterns and Bayesian Curve Fitting

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.

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. In my talk 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 are examining.