Bias-free estimation of information content in temporally sparse neuronal activity.

ABSTRACT: Applying information theoretic measures to neuronal activity data enables the quantification of neuronal encoding quality. However, when the sample size is limited, a naïve estimation of the information content typically contains a systematic overestimation (upward bias), which may lead to misinterpretation of coding characteristics. This bias is exacerbated in Ca2+ imaging because of the temporal sparsity of elevated Ca2+ signals. Here, we introduce methods to correct for the bias in the naïve estimation of information content from limited sample sizes and temporally sparse neuronal activity. We demonstrate the higher accuracy of our methods over previous ones, when applied to Ca2+ imaging data recorded from the mouse hippocampus and primary visual cortex, as well as to simulated data wit

SUBMITTER: Sheintuch L 

PROVIDER: S-EPMC8836373 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature