State-Dependent Population Coding in Primary Auditory Cortex

Abstract

Sensory function is mediated by interactions between external stimuli and intrinsic cortical dynamics that are evident in the modulation of evoked responses by cortical state. A number of recent studies across different modalities have demonstrated that the patterns of activity in neuronal populations can vary strongly between synchronized and desynchronized cortical states, i.e., in the presence or absence of intrinsically generated up and down states. Here we investigated the impact of cortical state on the population coding of tones and speech in the primary auditory cortex (A1) of gerbils, and found that responses were qualitatively different in synchronized and desynchronized cortical states. Activity in synchronized A1 was only weakly modulated by sensory input, and the spike patterns evoked by tones and speech were unreliable and constrained to a small range of patterns. In contrast, responses to tones and speech in desynchronized A1 were temporally precise and reliable across trials, and different speech tokens evoked diverse spike patterns with extremely weak noise correlations, allowing responses to be decoded with nearly perfect accuracy. Restricting the analysis of synchronized A1 to activity within up states yielded similar results, suggesting that up states are not equivalent to brief periods of desynchronization. These findings demonstrate that the representational capacity of A1 depends strongly on cortical state, and suggest that cortical state should be considered as an explicit variable in all studies of sensory processing.

Origin of information-limiting noise correlations

“…However, recent studies have revealed that noise correlations in visual areas also reflect slow internal fluctuations that are shared between groups of neurons and proposed that such fluctuations are the main source of experimentally measured noise correlations (12)(13)(14)(15)(16). However, the impact of this prominent source of correlation on information is largely unknown, except for two empirical studies of auditory (43) and visual (44) cortex whose results are based on small groups of neurons and cannot be extrapolated to large populations. Extrapolation from small populations to large populations is indeed prone to very large errors (7).…”

Inhibitory control of correlated intrinsic variability in cortical networks

“…To identify sets of trials in each recording that were likely to have either strong or weak noise correlations, we measured the level of cortical synchronization. Previous studies have shown that noise correlations are strong when the cortex is in a synchronized state, where activity is dominated by concerted, large-scale fluctuations, and weak when the cortex is in a desynchronized state, where these fluctuations are suppressed (Pachitariu et al, 2015; Schölvinck et al, 2015). …”

“…These dynamics can create correlations in the activity of neuronal populations with important consequences for coding and computation (Shadlen et al, 1996; Abbott and Dayan, 1999; Averbeck et al, 2006). The correlations between pairs of neurons have been studied extensively (Cohen and Kohn, 2011; Ecker et al, 2010; Averbeck et al, 2006), and recent studies have demonstrated that they are driven by dynamics involving coordinated, large-scale fluctuations in the activity of many cortical neurons (Sakata and Harris, 2009; Pachitariu et al, 2015; Okun et al, 2015). Inactivation of the cortical circuit suppresses these synchronized fluctuations at the level of the membrane potential, in both awake and anesthetized animals, suggesting that this synchronization is cortical in origin (Cohen-Kashi Malina et al, 2016).…”

Correlations and Neuronal Population Information

“…A substantial portion of correlations may also be explained by global fluctuations in which the firing of many neurons increases or decreases together on each trial (Ecker et al 2014, Goris et al 2014, Pachitariu et al 2015, Schölvinck et al 2015). The widespread cofluctuations of cortical activity were first observed using voltage-sensitive dye imaging of macaque V1, which revealed spatially extensive regions of coherent hyper- and depolarization (Arieli et al 1996).…”

“…Two recent studies have reported that information is affected by global fluctuations, based on simulating (Lin et al 2015) or analyzing (Arandia-Romero et al 2016, Pachitariu et al 2015, using Shannon information) small ensembles. However, in large populations, only differential correlations can make information saturate.…”