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 likelihood-based methods to fit the point process models to neuronal data. In my talk I will very briefly outline some of the substantive problems we are examining and the progress being made, emphasizing the
role of Bayesian curve-fitting.