Improved estimation and interpretation of correlations in neural circuits.

ABSTRACT: Ambitious projects aim to record the activity of ever larger and denser neuronal populations in vivo. Correlations in neural activity measured in such recordings can reveal important aspects of neural circuit organization. However, estimating and interpreting large correlation matrices is statistically challenging. Estimation can be improved by regularization, i.e. by imposing a structure on the estimate. The amount of improvement depends on how closely the assumed structure represents dependencies in the data. Therefore, the selection of the most efficient correlation matrix estimator for a given neural circuit must be determined empirically. Importantly, the identity and structure of the most efficient estimator informs about the types of dominant dependencies governing the system. We so

SUBMITTER: Yatsenko D 

PROVIDER: S-EPMC4380429 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature