A Population Coupling Model Identifies Reduced Propagation from V1 to Higher Visual Areas During Locomotion

Point process generalized linear models (GLMs) have been a major tool for studying coordinated activity across populations of neurons. These models typically quantify how the spiking of a single neuron depends on the past activity of other neurons at multiple time lags, and the resulting neuron-to-neuron interactions are then aggregated to obtain population-coupling effects. However, when neurons within the same population exhibit similar spiking patterns, explicitly modeling individual interactions can be redundant and can unnecessarily increase model complexity. In such cases, population-level formulations may offer a more efficient alternative. For example, biophysical population models often characterize circuit dynamics using the average firing rate across neurons within a population, and recent data-driven approaches have similarly demonstrated the utility of population-level statistics for capturing cross-population interactions. Motivated by this consideration, we reformulate the GLM framework to operate directly at the population level. The resulting model, which we call pop-GLM, provides a computationally efficient method for estimating coupling between populations. In a simulated dataset, we show that pop-GLM achieves greater sensitivity in detecting coupling effects and can account for trial-to-trial variation in stimulus drive, which would otherwise introduce bias. We also note that moving from single-neuron to population-level modeling requires a specific modification of the traditional GLM framework. We then apply pop-GLM to real data and find reduced functional connectivity from primary visual cortex (V1) to a higher visual area during locomotion, a change not detected by single-neuron GLMs. Author summaryA central goal of systems neuroscience is to understand how multiple populations of neurons across different brain areas interact as a coordinated circuit to produce perception and behavior. We formulated and investigated a new method for estimating functional interactions between two populations of spiking neurons, and we show that it can be more sensitive and robust than previous approaches. To illustrate, we discovered decreased interaction between two mouse visual areas during locomotion, a result that previous techniques did not detect. The method should aid investigators in searching for important functional relationships across populations of neurons, with precise time scale resolution.