Functional diversity among sensory neurons from efficient coding principles
Abstract
In many sensory systems the neural signal is coded by the coordinated response of heterogeneous populations of neurons. What computational benefit does this diversity confer on information processing? We derive an efficient coding framework assuming that neurons have evolved to communicate signals optimally given natural stimulus statistics and metabolic constraints. Incorporating nonlinearities and realistic noise, we study optimal population coding of the same sensory variable using two measures: maximizing the mutual information between stimuli and responses, and minimizing the error incurred by the optimal linear decoder of responses. Our theory is applied to a commonly observed splitting of sensory neurons into ON and OFF that signal stimulus increases or decreases, and to populations of monotonically increasing responses of the same type, ON. Depending on the optimality measure, we make different predictions about how to optimally split a population into ON and OFF, and how to allocate the firing thresholds of individual neurons given realistic stimulus distributions and noise, which accord with certain biases observed experimentally.
Additional Information
© 2019 Gjorgjieva et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Received: July 24, 2018; Accepted: October 10, 2019; Published: November 14, 2019. Data Availability Statement: All relevant data are within the paper and its Supporting Information files. All authors were supported by the NIH, the Gatsby Charitable Foundation and the Swartz Foundation. JG was supported by the Max Planck Society and a Burroughs-Wellcome Career Award at the Scientific Interface. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thanks Shuai Shao for careful reading of the analytical calculations. Author Contributions: Conceptualization: Julijana Gjorgjieva, Markus Meister, Haim Sompolinsky. Formal analysis: Julijana Gjorgjieva, Markus Meister, Haim Sompolinsky. Funding acquisition: Julijana Gjorgjieva, Markus Meister, Haim Sompolinsky. Investigation: Julijana Gjorgjieva. Methodology: Julijana Gjorgjieva, Markus Meister, Haim Sompolinsky. Software: Julijana Gjorgjieva. Supervision: Markus Meister, Haim Sompolinsky. Visualization: Julijana Gjorgjieva. Writing – original draft: Julijana Gjorgjieva. Writing – review & editing: Julijana Gjorgjieva, Markus Meister, Haim Sompolinsky.Attached Files
Published - journal.pcbi.1007476.pdf
Supplemental Material - journal.pcbi.1007476.s001.eps
Supplemental Material - journal.pcbi.1007476.s002.eps
Supplemental Material - journal.pcbi.1007476.s003.pdf
Supplemental Material - journal.pcbi.1007476.s004.pdf
Supplemental Material - journal.pcbi.1007476.s005.pdf
Supplemental Material - journal.pcbi.1007476.s006.pdf
Supplemental Material - journal.pcbi.1007476.s007.pdf
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Additional details
- PMCID
- PMC6890262
- Eprint ID
- 99887
- Resolver ID
- CaltechAUTHORS:20191118-080725855
- NIH
- Gatsby Charitable Foundation
- Swartz Foundation
- Max-Planck-Society
- Burroughs-Wellcome Fund
- Created
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2019-11-18Created from EPrint's datestamp field
- Updated
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2023-06-01Created from EPrint's last_modified field