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High-Fidelity Coding with Correlated Neurons

Author

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  • Rava Azeredo da Silveira
  • Michael J Berry II

Abstract

Positive correlations in the activity of neurons are widely observed in the brain. Previous studies have shown these correlations to be detrimental to the fidelity of population codes, or at best marginally favorable compared to independent codes. Here, we show that positive correlations can enhance coding performance by astronomical factors. Specifically, the probability of discrimination error can be suppressed by many orders of magnitude. Likewise, the number of stimuli encoded—the capacity—can be enhanced more than tenfold. These effects do not necessitate unrealistic correlation values, and can occur for populations with a few tens of neurons. We further show that both effects benefit from heterogeneity commonly seen in population activity. Error suppression and capacity enhancement rest upon a pattern of correlation. Tuning of one or several effective parameters can yield a limit of perfect coding: the corresponding pattern of positive correlation leads to a ‘lock-in’ of response probabilities that eliminates variability in the subspace relevant for stimulus discrimination. We discuss the nature of this pattern and we suggest experimental tests to identify it.Author Summary: Traditionally, sensory neuroscience has focused on correlating inputs from the physical world with the response of a single neuron. Two stimuli can be distinguished solely from the response of one neuron if one stimulus elicits a response and the other does not. But as soon as one departs from extremely simple stimuli, single-cell coding becomes less effective, because cells often respond weakly and unreliably. High fidelity coding then relies upon populations of cells, and correlation among those cells can greatly affect the neural code. While previous theoretical studies have demonstrated a potential coding advantage of correlation, they allowed only a marginal improvement in coding power. Here, we present a scenario in which a pattern of correlation among neurons in a population yields an improvement in coding performance by several orders of magnitude. By “improvement” we mean that a neural population is much better at both distinguishing stimuli and at encoding a large number of them. The scenario we propose does not invoke unrealistic values of correlation. What is more, it is even effective for small neural populations and in subtle cases in which single-cell coding fails utterly. These results demonstrate a previously unappreciated potential for correlated population coding.

Suggested Citation

  • Rava Azeredo da Silveira & Michael J Berry II, 2014. "High-Fidelity Coding with Correlated Neurons," PLOS Computational Biology, Public Library of Science, vol. 10(11), pages 1-25, November.
  • Handle: RePEc:plo:pcbi00:1003970
    DOI: 10.1371/journal.pcbi.1003970
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    References listed on IDEAS

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    1. Diego A. Gutnisky & Valentin Dragoi, 2008. "Adaptive coding of visual information in neural populations," Nature, Nature, vol. 452(7184), pages 220-224, March.
    2. József Fiser & Chiayu Chiu & Michael Weliky, 2004. "Small modulation of ongoing cortical dynamics by sensory input during natural vision," Nature, Nature, vol. 431(7008), pages 573-578, September.
    3. Elad Schneidman & Michael J. Berry & Ronen Segev & William Bialek, 2006. "Weak pairwise correlations imply strongly correlated network states in a neural population," Nature, Nature, vol. 440(7087), pages 1007-1012, April.
    4. Daniel A. Butts & Chong Weng & Jianzhong Jin & Chun-I Yeh & Nicholas A. Lesica & Jose-Manuel Alonso & Garrett B. Stanley, 2007. "Temporal precision in the neural code and the timescales of natural vision," Nature, Nature, vol. 449(7158), pages 92-95, September.
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    Cited by:

    1. Vikranth R Bejjanki & Rava Azeredo da Silveira & Jonathan D Cohen & Nicholas B Turk-Browne, 2017. "Noise correlations in the human brain and their impact on pattern classification," PLOS Computational Biology, Public Library of Science, vol. 13(8), pages 1-23, August.
    2. Volker Pernice & Rava Azeredo da Silveira, 2018. "Interpretation of correlated neural variability from models of feed-forward and recurrent circuits," PLOS Computational Biology, Public Library of Science, vol. 14(2), pages 1-26, February.
    3. Andrea K Barreiro & Cheng Ly, 2017. "When do correlations increase with firing rates in recurrent networks?," PLOS Computational Biology, Public Library of Science, vol. 13(4), pages 1-30, April.
    4. Mark L Ioffe & Michael J Berry II, 2017. "The structured ‘low temperature’ phase of the retinal population code," PLOS Computational Biology, Public Library of Science, vol. 13(10), pages 1-31, October.
    5. Jason S Prentice & Olivier Marre & Mark L Ioffe & Adrianna R Loback & Gašper Tkačik & Michael J Berry II, 2016. "Error-Robust Modes of the Retinal Population Code," PLOS Computational Biology, Public Library of Science, vol. 12(11), pages 1-32, November.
    6. Takuya Ito & Scott L Brincat & Markus Siegel & Ravi D Mill & Biyu J He & Earl K Miller & Horacio G Rotstein & Michael W Cole, 2020. "Task-evoked activity quenches neural correlations and variability across cortical areas," PLOS Computational Biology, Public Library of Science, vol. 16(8), pages 1-39, August.
    7. Joel Zylberberg & Alexandre Pouget & Peter E Latham & Eric Shea-Brown, 2017. "Robust information propagation through noisy neural circuits," PLOS Computational Biology, Public Library of Science, vol. 13(4), pages 1-35, April.

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