Schall Lab

Poisson Spike Train Analysis

To determine when a neuron modulates its firing rate within a single trials, we have implemented a Poisson spike train analysis. To derive a continuous function from a discrete spike train, we have calculate a spike density function by replacing the standard gaussian kernel with a kernel that looks like a post-synaptic potential (PSP, also referred to as an alpha function).
Poisson spike train analysis

Originally developed by Legendy and Salcman (1985, J Neurophysiol 53:926), we implemented this algorithm for use with data from awake behaving monkeys. Here is an example of its performance. Complete descriptions of its use can be found in Hanes, D.P., Thompson, K.G. and J.D. Schall (1995) Relationship of presaccadic activity in frontal eye field and supplementary eye field to saccade initiation in macaque: Poisson spike train analysis. Experimental Brain Research 103:85-96.

Thompson, K.G., D.P. Hanes, N.P. Bichot and J.D. Schall (1996) Perceptual and motor processing stages identified in the activity of macaque frontal eye field neurons during visual search. Journal of Neurophysiology 76:4040-4055.

Here is a Read me file, the code and an example spike train.

Convolving spike trains with gaussian and alpha (PSP) kernels

A problem with the gaussian formulation is that selection of the standard deviation is somewhat arbitrary. We elected to use a kernel that has a rapid growth and slower decay, just like a post-synaptic potential. In other words, it functions like a leaky integrator. Of course, the time constants of growth and decay can be set to arbitrary values. We have used values measured for excitatory post-synaptic potentials ((growth) = 1 msec, (decay) = 20 msec). Here is the code.

Matlab Code for Burst Analysis

Directions for p_burst.m: