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A hierarchy of intrinsic timescales across primate cortex

Nature Neuroscience volume 17, pages 1661–1663 (2014)Cite this article

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Abstract

Specialization and hierarchy are organizing principles for primate cortex, yet there is little direct evidence for how cortical areas are specialized in the temporal domain. We measured timescales of intrinsic fluctuations in spiking activity across areas and found a hierarchical ordering, with sensory and prefrontal areas exhibiting shorter and longer timescales, respectively. On the basis of our findings, we suggest that intrinsic timescales reflect areal specialization for task-relevant computations over multiple temporal ranges.

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Figure 1: Spike-count autocorrelation reveals a hierarchical ordering of intrinsic timescales.
Figure 2: Links between intrinsic timescales and longer functional timescales.

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Acknowledgements

We thank R. Chaudhuri and H.F. Song for discussions, and W. Chaisangmongkon and A. Ponce-Alvarez for assistance with data sets. Funding was provided by US Office of Naval Research grant N00014-13-1-0297 and US National Institutes of Health (NIH) grant R01MH062349 (X.-J.W.); NIH grant R01DA029330 (D.L.); NIH grants R01EY11749 and T32EY07125 (T.P.); NIH grant R01DA032758 and Whitehall Foundation grant 2010-12-13 (C.P.-S.); NIH grants R01DA19028 and P01NS040813 (J.D.W.); grants from Dirección General de Asuntos del Personal Académico–Universidad Nacional Autónoma de México and Consejo Nacional de Ciencia y Tecnología México (R.R.); and NIH grant R01EY019041 (D.J.F.).

Author information

Authors and Affiliations

  1. Center for Neural Science, New York University, New York, New York, USA

    John D Murray & Xiao-Jing Wang

  2. Department of Neurobiology, Yale University School of Medicine, New Haven, Connecticut, USA

    John D Murray, Alberto Bernacchia, Hyojung Seo, Daeyeol Lee & Xiao-Jing Wang

  3. School of Engineering and Science, Jacobs University, Bremen, Germany

    Alberto Bernacchia

  4. Department of Neurobiology, The University of Chicago, Chicago, Illinois, USA

    David J Freedman

  5. Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, México D.F., Mexico

    Ranulfo Romo

  6. El Colegio Nacional, México D.F., Mexico

    Ranulfo Romo

  7. Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, USA.,

    Jonathan D Wallis

  8. Department of Psychology, University of California, Berkeley, Berkeley, California, USA.,

    Jonathan D Wallis

  9. NYU-ECNU Institute of Brain and Cognitive Science, NYU Shanghai, Shanghai, China

    Xinying Cai & Xiao-Jing Wang

  10. Department of Anatomy and Neurobiology, Washington University in St. Louis, St. Louis, Missouri, USA

    Xinying Cai & Camillo Padoa-Schioppa

  11. Department of Neurobiology and Anatomy, University of Rochester, Rochester, New York, USA

    Tatiana Pasternak

  12. Center for Visual Science, University of Rochester, Rochester, New York, USA

    Tatiana Pasternak

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  1. John D Murray
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Contributions

J.D.M., A.B. and X.-J.W. designed the research and wrote the manuscript. J.D.M. analyzed the data and prepared the figures. D.J.F., R.R., J.D.W., X.C., C.P.-S., T.P., H.S. and D.L. contributed the electrophysiological data. All authors contributed to editing and revising the manuscript.

Corresponding author

Correspondence to Xiao-Jing Wang.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Spike-count autocorrelations in time.

Normalized autocorrelation matrices are shown for each area in a dataset. The matrix shows the mean correlation of the spike count in each time bin with the spike count in every other time bin, averaged across neurons. These show that the autocorrelation is roughly stationary across time during the foreperiod.

Supplementary Figure 2 Single neurons exhibit heterogeneous autocorrelations.

Light grey traces show the spike-count autocorrelation as function of time lag for single neurons, averaged across time points. Circles mark the population mean at each time lag, and the curve shows the exponential fit to the population data. The observation of single-neuron heterogeneity reinforces the interpretation of intrinsic timescale as a characteristic at the population level rather than at the single-neuron level.

Supplementary Figure 3 Differences in mean firing rates across areas do not account for hierarchy of intrinsic timescales.

Mean firing rates varied substantially across datasets and across areas within datasets. There was no significant dependence of intrinsic timescale on mean firing rate (P = 0.51, t(9) = −0.69, two-tailed t-test, regression slope m = −5.5 ± 7.9 ms/Hz; P = 0.16, rs = −0.34, Spearman’s rank correlation, two-tailed). Error bars mark s.e.

Supplementary Figure 4 Autocorrelation offset reflects trial-to-trial correlation.

Trial-to-trial correlation was calculated as the Pearson correlation coefficient between the foreperiod spike count in each trial and the spike count in the next trial. We hypothesized that autocorrelation offset would positively correlate with trial-to-trial correlation, and found a significant positive correlation between them. This indicates that the autocorrelation offset includes contributions from variability at timescales are comparable to or longer than the trial duration. Colored lines show trends for individual datasets. The arrow shows the slope of dependence from a regression analysis (slope m = 1.3 ± 0.3). Error bars mark s.e.

Supplementary Figure 5 Hierarchical ordering of areas by timescale of reward memory.

In the Lee dataset, we previously measured timescales of the decay of memory traces for past rewards in single-neuron firing rates, while monkeys performed a competitive decision-making task. (a) The cumulative distribution of reward timescales in LIP (n = 160), LPFC (n = 243), and ACC (n = 134). For neurons fit with the sum of two reward timescales, we used the harmonic mean of the two timescales. (b) Median reward timescale for the three areas. Error bars mark s.e.

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Supplementary Figures 1–5 and Supplementary Note (PDF 7450 kb)

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Murray, J., Bernacchia, A., Freedman, D. et al. A hierarchy of intrinsic timescales across primate cortex. Nat Neurosci 17, 1661–1663 (2014). https://doi.org/10.1038/nn.3862

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