Latent Dynamical Variables Produce Signatures of Spatiotemporal Criticality in Large Biological Systems

Mia C. Morrell, Audrey J. Sederberg, Ilya Nemenman

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8 Scopus citations

Abstract

Understanding the activity of large populations of neurons is difficult due to the combinatorial complexity of possible cell-cell interactions. To reduce the complexity, coarse graining had been previously applied to experimental neural recordings, which showed over two decades of apparent scaling in free energy, activity variance, eigenvalue spectra, and correlation time, hinting that the mouse hippocampus operates in a critical regime. We model such data by simulating conditionally independent binary neurons coupled to a small number of long-timescale stochastic fields and then replicating the coarse-graining procedure and analysis. This reproduces the experimentally observed scalings, suggesting that they do not require fine-tuning of internal parameters, but will arise in any system, biological or not, where activity variables are coupled to latent dynamic stimuli. Parameter sweeps for our model suggest that emergence of scaling requires most of the cells in a population to couple to the latent stimuli, predicting that even the celebrated place cells must also respond to nonplace stimuli.

Original languageEnglish (US)
Article number118302
JournalPhysical review letters
Volume126
Issue number11
DOIs
StatePublished - Mar 19 2021

Bibliographical note

Funding Information:
We thank L. Meshulam and W. Bialek for helping us understand their work, and S. Boettcher and G. Berman for valuable feedback. This work was supported in part by NIH Grants No. R01-NS084844 (A. J. S. and I. N.), No. R01-EB022872, and No. R01-NS099375 (I. N.), and by NSF Grant No. BCS-1822677 (I. N.).

Publisher Copyright:
© 2021 American Physical Society.

PubMed: MeSH publication types

  • Journal Article

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