Bridging scales from spiking activity to the local field potential through latent dynamics

Abstract

As emerging technologies enable measurement of precise details of the activity within microcircuits at ever-increasing scales, there is a growing need to identify the salient features and patterns within the neural populations that represent physiologically and behaviorally relevant aspects of the network. Accumulating evidence from recordings of large neural populations suggests that neural population activity frequently exhibits relatively low-dimensional structure, with a small number of variables explaining a substantial fraction of the structure of the activity. While such structure has been observed across the brain, it is not known how reduced-dimension representations of neural population activity relate to classical metrics of "brain state," typically described in terms of fluctuations in the local field potential (LFP), single-cell activity, and behavioral metrics. Here, we relate the latent dynamics of spiking activity of populations of neurons in the whisker area of primary somatosensory cortex of awake mice to classic measurements of cortical state in S1. We found that a hidden Markov model fit the population spiking data well with a relatively small number of states, and that putative inhibitory neurons played an outsize role in determining the latent state dynamics. Spiking states inferred from the model were more informative of the cortical state than a direct readout of the spiking activity of single neurons or of the population. Further, the spiking states predicted both the trial-by-trial variability in sensory responses and one aspect of behavior, whisking activity. Our results show how classical measurements of brain state relate to neural population spiking dynamics at the scale of the microcircuit and provide an approach for quantitative mapping of brain state dynamics across brain areas. Author SummaryBrain states have long been known to strongly shape sensory perception, decision making, cognition, and movement. Brain state during wakefulness changes constantly, classically assessed using changes in the spectral features of the local field potential (LFP) and behavioral measures. However, the connection between these classical measurements of brain state and the collective dynamics of populations of neurons is unclear. Here we fit a latent-variable model to population spiking activity, finding that latent variables inferred under the model are highly predictive of cortical state changes and that the latent dynamics are profoundly shaped by inhibitory cell activity. Our approach connects the activity patterns of ensembles of neurons to a classical measurement of brain state and opens new avenues for investigating brain state dynamics across diverse cortical areas.