I've been learning about generalized linear models and Bayesian approaches for doing statistics on spike train data, in the Truccolo lab. Here are some notes on the subject.
In neuroscience, we are interested in the problem of how neurons encode, process, and communicate information. Neurons communicate over long distances using brief all-or-nothing events called spikes. We are often interested in how the spiking rate of a neuron depends on other variables, such as stimuli, motor output, or other ongoing signals in the brain.
To model this, we consider spikes as events that occur at a point in time with an underlying variable rate, or conditional intensity, $\lambda$. There are many approaches to estimating $\lambda$. These notes cover point-process generalized linear models, and Bayesian approaches. These are closely related, and in some cases the same thing.
