Information-limiting correlations

Abstract

Computational strategies used by the brain strongly depend on the amount of information that can be stored in population activity, which in turn strongly depends on the pattern of noise correlations. In vivo, noise correlations tend to be positive and proportional to the similarity in tuning properties. Such correlations are thought to limit information, which has led to the suggestion that decorrelation increases information. In contrast, we found, analytically and numerically, that decorrelation does not imply an increase in information. Instead, the only information-limiting correlations are what we refer to as differential correlations: correlations proportional to the product of the derivatives of the tuning curves. Unfortunately, differential correlations are likely to be very small and buried under correlations that do not limit information, making them particularly difficult to detect. We found, however, that the effect of differential correlations on information can be detected with relatively simple decoders.

Statistical structure of neural spiking under non-Poissonian or other non-white stimulation

“…As shown in the Appendix A.2, the leading-order approximations for the CV and the SCC, Eqs. (9) and (13), are consistent with exact analytical results for the variability of the spike count on large time scales, which have been obtained previously (Middleton et al 2003;Sobie et al 2011;Moreno-Bote et al 2014). Indeed, the Fano factor F (t), defined as the variance to mean ratio of the spike count in a time window of length t, can be approximated for general t > 0 as…”

Correlated variability modifies working memory fidelity in primate prefrontal neuronal ensembles

“…Pairwise measurements of r sc are insufficient for predicting the effects of r sc structure on ensemble information in large, multidimensional ensembles with heterogeneous tuning (20). Furthermore, analytical methods for determining the effects of r sc structure on information content can be complicated to calculate for large stimulus sets and can also be inaccurate unless applied to data consisting of hundreds of trials per stimulus (20,37).…”

“…Furthermore, analytical methods for determining the effects of r sc structure on information content can be complicated to calculate for large stimulus sets and can also be inaccurate unless applied to data consisting of hundreds of trials per stimulus (20,37). Linear decoders are demonstrably well-suited for extracting lowdimensional representations from high-dimensional neuronal ensemble data and for directly assessing the impact of r sc structure on ensemble information content and thus offer a pragmatic solution to the issues of dimensionality and correlated variability (20,38).…”

“…Given a fixed ensemble of neurons (and thus a constant r signal structure), changes in r sc can have profound effects on information coding (19)(20)(21)(22)(23)(24). For example, spatial attention improves neural coding in the visual cortex primarily by reducing r sc (25)(26)(27)).…”

“…Furthermore, there are relatively few studies investigating r sc in the LPFC (21,27,(29)(30)(31)(32)(33); and only one of these studies directly examined the effects of r sc on information coding (27). Prior results examining pairwise correlations are also difficult to extrapolate to larger neuronal ensembles, which have a complex, multidimensional r sc structure that cannot be characterized by pairwise measurements alone (20).…”