The Nature of Shared Cortical Variability

Abstract

SummaryNeuronal responses of sensory cortex are highly variable, and this variability is correlated across neurons. To assess how variability reflects factors shared across a neuronal population, we analyzed the activity of many simultaneously recorded neurons in visual cortex. We developed a simple model that comprises two sources of shared variability: a multiplicative gain, which uniformly scales each neuron’s sensory drive, and an additive offset, which affects different neurons to different degrees. This model captured the variability of spike counts and reproduced the dependence of pairwise correlations on neuronal tuning and stimulus orientation. The relative contributions of the additive and multiplicative fluctuations could vary over time and had marked impact on population coding. These observations indicate that shared variability of neuronal populations in sensory cortex can be largely explained by two factors that modulate the whole population.

Correlations and Neuronal Population Information

“…Even worse, in some cases, one can reach opposite conclusions about how correlations affect information when analyzing small rather than large populations. For instance, Lin et al (2015) considered a code in which correlations are beneficial rather than detrimental for small populations. However, in their code, correlations reduce information when a larger population is considered (Figure 3 c ).…”

“…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.…”

Representation of memories in the cortical–hippocampal system: Results from the application of population similarity analyses

“…Trial-to-trial variability in how neurons fire to the same stimulus tends to be correlated (Averbeck and Lee, 2003; Deweese and Zador, 2004; Lin et al, 2015; Shadlen and Newsome, 1998) and these noise correlations may increase or decrease the ability to decode information from ensemble activity (Averbeck and Lee, 2006; Averbeck et al, 2006), and may or may not affect overall coding capacity of the network (Abbott and Dayan, 1999; Narayanan et al, 2005; Shamir and Sompolinsky, 2006; Zohary et al, 1994). Typically, the importance of these noise correlations is assessed by shuffling each cell’s trial-by-trial activity and testing whether there are any differences in the ability to discriminate stimuli based off of the observed versus shuffled ensemble activity patterns (Averbeck and Lee, 2006; Latham and Nirenberg, 2005).…”

Predictors and signatures of recovery from neglect in acute stroke

“…Being comparable in clinical characteristics and lesion size, the fav-REC and poorREC groups differed by strength of functional connectivity, which might be explained by possible group differences in (1) more critical damage of white matter tracts 67 or cortical areas 30 that was not captured by our analysis, (2) premorbid cognitive/neural reserves influencing how the brain responds to injury, 68 and (3) interindividual functional and anatomical variability. 69,70 Nonetheless, functional connectivity reflected the network capacity for early reorganization and recovery, and provided prognostic information for neglect patients at a very early stage. This information derived 2 to 3 days poststroke might eventually translate to better outcomes, 37 indicating patients requiring more intensive rehabilitation in the early stage.…”