Regularity in Spike Firing with Random Inputs Detected by Method Extracting Contribution of Temporal Integration of a Pair of Incoming Spikes to the Firing of a Neuron

Abstract

A multi-unit spike train analysis method is introduced to detect the contribution of temporal integration of a doublet firing (a pair of spikes) in one neuron to the probability of spike generation in another neuron within the same network. This is a conditional correlation method that estimates the conditional probability of firing of a spike in a neuron based on the probability of firing of not just a single spike but two sequential spikes (doublet) in another neuron prior to its firing. The duration of temporal integration on the firing characteristics of spikes can be revealed by examining the relationship between the preinterspike intervals (pre-ISIs) in one neuron and post-cross intervals (post-CIs) in another neuron statistically. The “integration period” between two spikes are revealed by the appearance of a finite horizontal band of points (or lack of points) in the “pre-ISI vs post-CI scatter plot.” Furthermore, this analysis also reveals whether the coupled spike firing in one neuron is correlated with the first preceding spike in the other neuron. Simulation results show that regularity in spike firing can be produced by a randomly firing driver neuron using this analysis. The results also show that the firing characteristics of the driven neuron can be determined by the properties of the driven neuron (such as the integration period) relatively independent of the incoming rate of firing of the driver neuron. The rhythmicity of synchronized firing in the driven neurons produced by entirely random inputs may provide insight in interpreting the significance of computational properties of a network of neurons.