Topic: bias-correction methods

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 information typically contains an upward bias, which may lead to misinterpretation of coding characteristics. This bias is exacerbated in Ca2+ imaging because of the temporal sparsity of detected Ca2+ signals. Here, we introduce methods to correct the bias in the naïve estimation of information from limited sample sizes and temporally sparse activity. We demonstrate the higher accuracy of our methods over prev