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Sparse bursts optimize information transmission in a multiplexed neural code

Richard Naud and Henning Sprekeler
PNAS July 3, 2018 115 (27) E6329-E6338; published ahead of print June 22, 2018 https://doi.org/10.1073/pnas.1720995115
Richard Naud
aUniversity of Ottawa Brain and Mind Research Institute, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada;bDepartment of Physics, University of Ottawa, Ottawa, ON K1N 6N5, Canada;
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  • For correspondence: rnaud@uottawa.ca
Henning Sprekeler
cBernstein Center for Computational Neuroscience Berlin, 10115 Berlin, Germany;dModelling of Cognitive Processes, Institute of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, 10587 Berlin, Germany
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  1. Edited by Terrence J. Sejnowski, Salk Institute for Biological Studies, La Jolla, CA, and approved May 21, 2018 (received for review December 4, 2017)

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Significance

Understanding the neural code is to attribute proper meaning to temporal sequences of action potentials. We report a simple neural code based on distinguishing single spikes from spikes in close succession, commonly called “bursts.” By separating these two types of responses, we show that ensembles of neurons can communicate rapidly changing and graded information from two sources simultaneously and with minimal cross-talk. Second, we show that this multiplexing can optimize the information transferred per action potential when bursts are relatively rare. Finally, we show that neurons can demultiplex these two streams of information. We propose that this multiplexing may be particularly important in hierarchical communication where bottom–up and top–down information must be distinguished.

Abstract

Many cortical neurons combine the information ascending and descending the cortical hierarchy. In the classical view, this information is combined nonlinearly to give rise to a single firing-rate output, which collapses all input streams into one. We analyze the extent to which neurons can simultaneously represent multiple input streams by using a code that distinguishes spike timing patterns at the level of a neural ensemble. Using computational simulations constrained by experimental data, we show that cortical neurons are well suited to generate such multiplexing. Interestingly, this neural code maximizes information for short and sparse bursts, a regime consistent with in vivo recordings. Neurons can also demultiplex this information, using specific connectivity patterns. The anatomy of the adult mammalian cortex suggests that these connectivity patterns are used by the nervous system to maintain sparse bursting and optimal multiplexing. Contrary to firing-rate coding, our findings indicate that the physiology and anatomy of the cortex may be interpreted as optimizing the transmission of multiple independent signals to different targets.

  • neural coding
  • cerebral cortex
  • multiplexing
  • dendritic computation
  • short-term plasticity

Footnotes

  • ↵1To whom correspondence should be addressed. Email: rnaud{at}uottawa.ca.
  • Author contributions: R.N. and H.S. designed research; R.N. performed research; R.N. analyzed data; and R.N. and H.S. wrote the paper.

  • The authors declare no conflict of interest.

  • This article is a PNAS Direct Submission.

  • This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1720995115/-/DCSupplemental.

  • Copyright © 2018 the Author(s). Published by PNAS.

This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

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Sparse bursts optimize information transmission in a multiplexed neural code
Richard Naud, Henning Sprekeler
Proceedings of the National Academy of Sciences Jul 2018, 115 (27) E6329-E6338; DOI: 10.1073/pnas.1720995115

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Sparse bursts optimize information transmission in a multiplexed neural code
Richard Naud, Henning Sprekeler
Proceedings of the National Academy of Sciences Jul 2018, 115 (27) E6329-E6338; DOI: 10.1073/pnas.1720995115
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