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Response variability, as measured by fluctuating responses upon repeatedperformance of trials, is a major component of neural responses, and itscharacterization is key to interpret high dimensional population recordings.Response variability and covariability display predictable changes upon changesin stimulus and cognitive or behavioral state, providing an opportunity to testthe predictive power of models of neural variability. Still, there is littleagreement on which model to use as a building block for population-levelanalyses, and models of variability are often treated as a subject of choice.We investigate two competing models, the Doubly Stochastic Poisson (DSP) modelassuming stochasticity at spike generation, and the Rectified Gaussian (RG)model that traces variability back to membrane potential variance, to analyzestimulus-dependent modulation of response statistics. Using a model of a pairof neurons, we demonstrate that the two models predict similar single-cellstatistics. However, DSP and RG models have contradicting predictions on thejoint statistics of spiking responses. In order to test the models againstdata, we build a population model to simulate stimulus change-relatedmodulations in response statistics. We use unit recordings from the primaryvisual cortex of monkeys to show that while model predictions for variance arequalitatively similar to experimental data, only the RG model's predictions arecompatible with joint statistics. These results suggest that models usingPoisson-like variability might fail to capture important properties of responsestatistics. We argue that membrane potential-level modelling of stochasticityprovides an efficient strategy to model correlations.

Response statistics dissect the contributions of different sources of variability to population activity in V1