A sparse set of spikes corresponding to reliable correlations is highly informative of visual stimulus on single trials

Abstract

Spike trains in cortical neuronal populations vary in number and timing trial-to-trial, rendering a viable single trial coding scheme for sensory information elusive. Correlations between pairs of neocortical neurons can be segmented into either sensory or noise according to their stimulus specificity. Here we show that pairs of spikes, corresponding to reliable sensory correlations in imaged populations in layer 2/3 of mouse visual cortex are particularly informative of visual stimuli. This set of spikes is sparse and exhibits comparable levels of trial-to-trial variance relative to the full spike train. Despite this, correspondence of pairs of spikes to a specific set of sensory correlations identifies spikes that carry more information per spike about the visual stimulus than the full set or any other matched set of spikes. Moreover, this sparse subset is more accurately decoded, regardless of the decoding algorithm employed. Our findings suggest that consistent pairwise correlations between neurons, rather than first-order statistical features of spike trains, may be an organizational principle of a single trial sensory coding scheme.

A dynamic subset of network interactions underlies tuning to natural movements in marmoset sensorimotor cortex

“…The differences between the context-specific and context-invariant groups aligns with recent work demonstrating that reliable pairwise correlations, rather than first-order statistical features of spike trains, are the building blocks of coding in visual cortex 10 . The reorganization of the context-specific functional group, which was strongly tuned to kinematics, demonstrates a link between precisely structured interactions and kinematic encoding in sensorimotor cortex.…”

“…Recent work has also demonstrated that pairwise spike time correlations carry behaviorally relevant information in M1 7 . A large body of work in mostly primary visual cortex has shown that recurrent interactions within local functional groups can explain single-trial neural activity 8 , that both tuned and untuned units are essential components of the functional network (FN) 9 , and suggested that reliable pairwise correlations, rather than first-order statistical features of spike trains, are the building blocks of coding in visual cortex 10 .…”