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

Liron Sheintuch, Alon Rubin, Yaniv Ziv

Research output: Contribution to journalArticlepeer-review


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 with matching tuning properties and firing statistics. Our bias-correction methods allowed an accurate estimation of the information place cells carry about the animal's position (spatial information) and uncovered the spatial resolution of hippocampal coding. Furthermore, using our methods, we found that cells with higher peak firing rates carry higher spatial information per spike and exposed differences between distinct hippocampal subfields in the long-term evolution of the spatial code. These results could be masked by the bias when applying the commonly used naïve calculation of information content. Thus, a bias-free estimation of information content can uncover otherwise overlooked properties of the neural code.
Original languageEnglish
Article numbere1009832
Number of pages34
JournalPLoS Computational Biology
Issue number2
StatePublished - 11 Feb 2022


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