Optimal burstiness in populations of spiking neurons facilitates decoding of decreases in tonic firing

Abstract

A stimulus can be encoded in a population of spiking neurons through any change in the statistics of the joint spike pattern, yet we commonly summarize single-trial population activity by the summed spike rate across cells: the population peri-stimulus time histogram (pPSTH). For neurons with low baseline spike rate that encode a stimulus with a rate increase, this simplified representation works well, but for populations with high baseline rates and heterogeneous response patterns, the pPSTH has limited utility in capturing the neural representation of the stimulus. We simulated populations of spiking neurons that varied in size, baseline rate, burst statistics, and correlation, and we measured how these populations represent decreases (gaps) in spike rate. We introduce a different representation of the population spike pattern which we call an "information train," and we show that it is more flexible and robust than the pPSTH in capturing stimulus information across different types of neuronal populations. In particular, we use this tool to study populations with varying levels of burstiness in their spiking statistics. We find that there is an optimal level of burstiness for gap detection that is robust to several other parameters of the population. Next, we consider this theoretical result in the context of experimental data from different types of retinal ganglion cells and determine that the baseline spike statistics of a particular, recently identified type support nearly optimal detection of both the onset and strength of a contrast step.