In this talk we discuss two main areas. We begin by introducing stochastic models of the state of an artificial or biological neuron. In that context we discuss methods of identification of interesting parameters of neural and artificial systems. We also consider application of experimentally generated data and simulation of more sophisticated neural networks.

We next consider transmission of trains of action potentials between neurons. This transmission is a primary means of communicating information in the nervous system. We model spike trains as stochastic point processes (e.g. Poisson, doubly stochastic Poisson, and point processes with random intensities). We then discuss statistical analysis of simultaneously recorded spike trains.