Analysis and Interpretation of Interval and Count Variability in Neural Spike Trains

Abstract

Understanding the nature and origin of neural variability at the level of single neurons and neural networks is fundamental to our understanding of how neural systems can reliably process information. This chapter provides a starting point to the empirical analysis and interpretation of the variability of single neuron spike trains. In the first part, we cover a number of practical issues of measuring the inter-spike interval variability with the coefficient of variation (CV) and the trial- by-trial count variability with the Fano factor (FF), including the estimation bias for finite observations, the measurement from rate-modulated spike trains, and the time- resolved analysis of variability dynamics. In the second part, we specifically explore the effect of serial interval correlation in nonrenewal spike trains and the impact of slow fluctuations of neural activity on the relation of interval and count variability in stochastic models and in in vivo recordings from cortical neurons. Finally, we discuss how we can interpret the empirical results with respect to potential neuron- intrinsic and neuron-extrinsic sources of single neuron output variability.