Applications of the spike density function in analysis of neuronal firing patterns

Firing rate histogram is a widely used mathematical method for representing the activity of single neurons and small neural networks. Nevertheless, observation of fine temporal modulation or correlations of spike trains might be troublesome if the mean firing rate is low or rapid local changes occur. The spike density function (SDF) obtained by convolving the spike train with smooth and continuous kernel function proves to be a more appropriate approach in characterization of the firing pattern. The resulting time-function is a continuous and derivable one, thus it can be used as a dynamical variable of the neuronal activity. In the present paper applications of SDF in analysis of the firing patterns of Lymnaea neurons are described and its performance is compared to other quantitative methods.