Measuring Fisher information accurately in correlated neural populations.

ABSTRACT: Neural responses are known to be variable. In order to understand how this neural variability constrains behavioral performance, we need to be able to measure the reliability with which a sensory stimulus is encoded in a given population. However, such measures are challenging for two reasons: First, they must take into account noise correlations which can have a large influence on reliability. Second, they need to be as efficient as possible, since the number of trials available in a set of neural recording is usually limited by experimental constraints. Traditionally, cross-validated decoding has been used as a reliability measure, but it only provides a lower bound on reliability and underestimates reliability substantially in small datasets. We show that, if the number of trials per co

SUBMITTER: Kanitscheider I 

PROVIDER: S-EPMC4451760 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature