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

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 misinterp...

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Veröffentlicht in:PLoS computational biology Jg. 18; H. 2; S. e1009832
Hauptverfasser: Sheintuch, Liron, Rubin, Alon, Ziv, Yaniv
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 01.02.2022
Public Library of Science (PLoS)
Schlagworte:
Analysis
Bias
Bias (Statistics)
Biology and Life Sciences
Calcium imaging
Calcium ions
Calcium signalling
Coding
Cortex (temporal)
Estimation theory
Hippocampus
Hippocampus - physiology
Humans
Information processing
Information theory
Medicine and Health Sciences
Methods
Neural coding
Neuroimaging
Neurons
Neurons - physiology
Research and Analysis Methods
Social Sciences
Spatial data
Spatial discrimination
Spatial resolution
Statistical methods
Temporal lobe
Visual cortex
Israel
ISSN:1553-7358, 1553-734X, 1553-7358
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Zusammenfassung: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 Ca 2+ imaging because of the temporal sparsity of elevated Ca 2+ 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 Ca 2+ 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.
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The authors have declared that no competing interests exist.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1009832