Functional triplet motifs underlie accurate predictions of single-trial responses in populations of tuned and untuned V1 neurons

Abstract

Visual stimuli are encoded in the activity patterns of neocortical neuronal populations. Trial-averaged neuronal activity is selectively modulated by particular visual stimulus parameters, such as the direction of a moving bar of light, resulting in well-defined tuning properties. However, a large number of neurons in visual cortex remain unmodulated by any given stimulus parameter, and the role of this untuned population is not well understood. Here, we use two-photon calcium imaging to record, in an unbiased manner, from large populations of layer 2/3 excitatory neurons in mouse primary visual cortex to describe co-varying activity on single trials in populations consisting of tuned and untuned neurons. Specifically, we summarize pairwise covariability with an asymmetric partial correlation coefficient, allowing us to analyze the population correlation structure with graph theory. Using the graph neighbors of a neuron, we find that the local population, including tuned and untuned neurons, are able to predict individual neuron activity on a single trial basis and recapitulate average tuning properties of tuned neurons. We also find that a specific functional triplet motif in the graph results in the best predictions, suggesting a signature of informative correlations in these populations. Variance explained in total population activity scales with the number of neurons imaged, suggesting larger sample sizes are required to fully capture local network interactions. In summary, we show that unbiased sampling of the local population can explain single trial response variability as well as trial-averaged tuning properties in V1, and the ability to predict responses is tied to the occurrence of a functional triplet motif.\n\nAuthor summaryV1 populations have historically been characterized by single cell response properties and pairwise co-variability. Many cells, however, do not show obvious dependencies to a given stimulus or behavioral task, and have consequently gone unanalyzed. We densely record from V1 populations to measure how trial-to-trial response variability relates to these previously understudied neurons. We find that individual neurons, regardless of response properties, are inextricably dependent on the population in which they are embedded. By studying patterns of correlations between groups of neurons, we identify a specific triplet motif that predicts this dependence on local population activity. Only by studying large populations simultaneously were we able to find an emergent property of this information. These results imply that understanding how the visual system operates with substantial trial-to-trial variability will necessitate a network perspective that accounts for both visual stimuli and activity in the local population.