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The intrinsic attractor manifold and population dynamics of a canonical cognitive circuit across waking and sleep

Nature Neuroscience volume 22, pages 1512–1520 (2019)Cite this article

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Abstract

Neural circuits construct distributed representations of key variables—external stimuli or internal constructs of quantities relevant for survival, such as an estimate of one’s location in the world—as vectors of population activity. Although population activity vectors may have thousands of entries (dimensions), we consider that they trace out a low-dimensional manifold whose dimension and topology match the represented variable. This manifold perspective enables blind discovery and decoding of the represented variable using only neural population activity (without knowledge of the input, output, behavior or topography). We characterize and directly visualize manifold structure in the mammalian head direction circuit, revealing that the states form a topologically nontrivial one-dimensional ring. The ring exhibits isometry and is invariant across waking and rapid eye movement sleep. This result directly demonstrates that there are continuous attractor dynamics and enables powerful inference about mechanism. Finally, external rather than internal noise limits memory fidelity, and the manifold approach reveals new dynamical trajectories during sleep.

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Fig. 1: Population activity as a manifold and a method for manifold characterization.
Fig. 2: Unsupervised discovery and time-resolved decoding of encoded variables through manifold characterization.
Fig. 3: REM sleep states, fluxes and dynamics suggest that the manifold is internally generated and attractive.
Fig. 4: Diffusive dynamics during REM sleep.
Fig. 5: Higher-dimensional states and coherent dynamics during nREM sleep.

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Data availability

Data have been previously reported21 and are available on the CRCNS website at http://crcns.org/data-sets/thalamus/th-1.

Code availability

The code is available at https://fietelab.mit.edu/code/.

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Acknowledgements

The authors thank D. Tank, S. Lewallen and R. Low for insightful discussions, and F. Caccuci, T. Wills, S. Deneve, Y. Burak, J. Murray, J. Pillow, L. Paninski and M. Sahani for comments on the work or manuscript. I.F. is grateful to G. Prasad for pointing her to the field of topological data analysis several years ago, and to W. Bialek for raising the possibility of unsupervised discovery of encoded variables from neural data, also several years ago. This work was supported in part by grants from the NIH (U01-NS094330-03), the Simons Foundation (SCGB and the International Brain Laboratory) and the Howard Hughes Medical Institute through the Faculty Scholars Program to I.F., and by the Canadian Research Chair in Systems Neuroscience (245716), a CIHR Project Grant (155957), a NSERC Discovery Grant (RGPIN-2018-04600) and the IRDC (108877-001) to A.P. Part of this work was performed by R.C. and I.F. in residence at the Simons Institute for the Theory of Computing at UC Berkeley, where R.C. was a Google Research Fellow.

Author information

Author notes

  1. These authors contributed equally: Berk Gerçek and Biraj Pandey.

Authors and Affiliations

  1. Center for Learning and Memory, The University of Texas at Austin, Austin, TX, USA

    Rishidev Chaudhuri, Berk Gerçek, Biraj Pandey & Ila Fiete

  2. Center for Neuroscience, University of California, Davis, Davis, CA, USA

    Rishidev Chaudhuri

  3. Department of Neurobiology, Physiology and Behavior, University of California, Davis, Davis, CA, USA

    Rishidev Chaudhuri

  4. Department of Mathematics, University of California, Davis, Davis, CA, USA

    Rishidev Chaudhuri

  5. Department of Basic Neuroscience, University of Geneva, Geneva, Switzerland

    Berk Gerçek

  6. Department of Applied Mathematics, University of Washington, Seattle, WA, USA

    Biraj Pandey

  7. Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada

    Adrien Peyrache

  8. Department of Brain and Cognitive Sciences and McGovern Institute, MIT, Cambridge, MA, USA

    Ila Fiete

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Contributions

R.C. and I.F. conceived the decoding method and analyses. R.C., B.G. and B.P. implemented the analyses. A.P. collected the original data and advised on the data and analyses. R.C. and I.F. wrote the paper with input from the other authors.

Corresponding authors

Correspondence to Rishidev Chaudhuri or Ila Fiete.

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The authors declare no competing interests.

Additional information

Peer review information: Nature Neuroscience thanks Vivek Jayaraman, Kate Jeffery, Sung Soo Kim, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Integrated supplementary information

Supplementary Figure 1

Low-dimensional structure in the head direction circuit.

Supplementary Figure 2

Distortion of manifold by linear methods and by inhomogeneous sampling.

Supplementary Figure 3

Toroidal manifold in simulated grid cell activity.

Supplementary Figure 4

Decoding performance does not strongly depend on embedding dimension or number of spline knots and performance is good with ~20 neurons and several minutes of data.

Supplementary Figure 5

Wake dynamics for other animals.

Supplementary Figure 6

Spiking variance and tuning curves explained by decoding.

Supplementary Figure 7

Ring manifold preserved during REM across animals.

Supplementary Figure 8

REM occupancy and flows.

Supplementary Figure 9

REM decoding across animals.

Supplementary Figure 10

Waking and REM states and dynamics replicated in a continuous attractor model.

Supplementary Figure 11

Loss of ring and higher-dimensional nREM manifold across animals.

Supplementary Figure 12

nREM decoding and dynamics controls.

Supplementary Figure 13

Decoding, dynamics, and trajectory statistics during nREM for Mouse 25 are similar to those observed for Mouse 28.

Supplementary Figure 14

Qualitative reproduction of nREM trajectory, dynamics, and statistics in continuous attractor ring network model.

Supplementary information

Supplementary Information

Supplementary Figs. 1–14 and Supplementary Notes 1–4.

Reporting Summary

Supplementary Video 1

Wake manifold. Three-dimensional Isomap embedding of the waking manifold from Mouse 28, session 140313.

Supplementary Video 2

REM manifold. Three-dimensional Isomap embedding of the REM manifold from mouse 28, session 140313.

Supplementary Video 3

Waking dynamics. Dynamics during 200 s of waking (projection generated using Isomap). The moving trace shows 1 s (most recent point is darkest). The blue points in the background show the waking manifold.

Supplementary Video 4

REM dynamics. Dynamics during 200 s of REM sleep (projection generated using Isomap). The moving trace shows 1 s (most recent point is darkest). The green scatter plot in the background shows the REM manifold. Note that REM sleep intervals are typically quite short, so the 200 s comprises multiple concatenated intervals.

Supplementary Video 5

nREM manifold. Three-dimensional Isomap embedding of the nREM manifold from mouse 28, session 140313, shown in mustard yellow, along with the waking manifold for comparison, shown in blue.

Supplementary Video 6

nREM dynamics. Dynamics during 200 s of nREM sleep (projection generated using Isomap). The moving trace shows 1 s (most recent point is darkest). The mustard yellow scatter plot in the background shows the nREM manifold, and the blue points show the waking manifold.

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Chaudhuri, R., Gerçek, B., Pandey, B. et al. The intrinsic attractor manifold and population dynamics of a canonical cognitive circuit across waking and sleep. Nat Neurosci 22, 1512–1520 (2019). https://doi.org/10.1038/s41593-019-0460-x

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