A reservoir of timescales emerges in recurrent circuits with heterogeneous neural assemblies

  1. Merav Stern
  2. Nicolae Istrate
  3. Luca Mazzucato  Is a corresponding author
  1. Hebrew University of Jerusalem, Israel
  2. University of Oregon, United States

Abstract

The temporal activity of many physical and biological systems, from complex networks to neural circuits, exhibits fluctuations simultaneously varying over a large range of timescales. Long-tailed distributions of intrinsic timescales have been observed across neurons simultaneously recorded within the same cortical circuit. The mechanisms leading to this striking temporal heterogeneity are yet unknown. Here we show that neural circuits, endowed with heterogeneous neural assemblies of different sizes, naturally generate multiple timescales of activity spanning several orders of magnitude. We develop an analytical theory using rate networks, supported by simulations of spiking network with cell-type specific connectivity, to explain how neural timescales depend on assembly size and show that our model can naturally explain the long-tailed timescale distribution observed in awake primate cortex. When driving recurrent networks of heterogeneous neural assemblies by a time-dependent broadband input, we found that large and small assemblies preferentially entrain slow and fast spectral components of the input, respectively. Our results suggest that heterogeneous assemblies can provide a biologically plausible mechanism for neural circuits to demix complex temporal input signals by transforming temporal into spatial neural codes via frequency-selective neural assemblies.

Data availability

https://github.com/nistrate/multipleTimescalesRNN

The following previously published data sets were used

Article and author information

Author details

  1. Merav Stern

    Faculty of Medicine, Hebrew University of Jerusalem, Jerusalemn, Israel
    Competing interests
    The authors declare that no competing interests exist.
  2. Nicolae Istrate

    Institute of Neuroscience, University of Oregon, Eugene, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Luca Mazzucato

    Institute of Neuroscience, University of Oregon, Eugene, United States
    For correspondence
    lmazzuca@uoregon.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8525-7539

Funding

National Institute of Neurological Disorders and Stroke (R01-NS118461)

  • Luca Mazzucato

National Institute on Drug Abuse (R01-DA055439)

  • Luca Mazzucato

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Srdjan Ostojic, École Normale Supérieure - PSL, France

Version history

  1. Preprint posted: October 12, 2021 (view preprint)
  2. Received: January 31, 2023
  3. Accepted: December 7, 2023
  4. Accepted Manuscript published: December 12, 2023 (version 1)
  5. Version of Record published: January 25, 2024 (version 2)

Copyright

© 2023, Stern et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 1,079
    views
  • 198
    downloads
  • 2
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Merav Stern
  2. Nicolae Istrate
  3. Luca Mazzucato
(2023)
A reservoir of timescales emerges in recurrent circuits with heterogeneous neural assemblies
eLife 12:e86552.
https://doi.org/10.7554/eLife.86552

Share this article

https://doi.org/10.7554/eLife.86552

Further reading

    1. Neuroscience
    Tianhao Chu, Zilong Ji ... Si Wu
    Research Article

    Hippocampal place cells in freely moving rodents display both theta phase precession and procession, which is thought to play important roles in cognition, but the neural mechanism for producing theta phase shift remains largely unknown. Here, we show that firing rate adaptation within a continuous attractor neural network causes the neural activity bump to oscillate around the external input, resembling theta sweeps of decoded position during locomotion. These forward and backward sweeps naturally account for theta phase precession and procession of individual neurons, respectively. By tuning the adaptation strength, our model explains the difference between ‘bimodal cells’ showing interleaved phase precession and procession, and ‘unimodal cells’ in which phase precession predominates. Our model also explains the constant cycling of theta sweeps along different arms in a T-maze environment, the speed modulation of place cells’ firing frequency, and the continued phase shift after transient silencing of the hippocampus. We hope that this study will aid an understanding of the neural mechanism supporting theta phase coding in the brain.

    1. Neuroscience
    Josue M Regalado, Ariadna Corredera Asensio ... Priyamvada Rajasethupathy
    Research Article

    Learning requires the ability to link actions to outcomes. How motivation facilitates learning is not well understood. We designed a behavioral task in which mice self-initiate trials to learn cue-reward contingencies and found that the anterior cingulate region of the prefrontal cortex (ACC) contains motivation-related signals to maximize rewards. In particular, we found that ACC neural activity was consistently tied to trial initiations where mice seek to leave unrewarded cues to reach reward-associated cues. Notably, this neural signal persisted over consecutive unrewarded cues until reward-associated cues were reached, and was required for learning. To determine how ACC inherits this motivational signal we performed projection-specific photometry recordings from several inputs to ACC during learning. In doing so, we identified a ramp in bulk neural activity in orbitofrontal cortex (OFC)-to-ACC projections as mice received unrewarded cues, which continued ramping across consecutive unrewarded cues, and finally peaked upon reaching a reward-associated cue, thus maintaining an extended motivational state. Cellular resolution imaging of OFC confirmed these neural correlates of motivation, and further delineated separate ensembles of neurons that sequentially tiled the ramp. Together, these results identify a mechanism by which OFC maps out task structure to convey an extended motivational state to ACC to facilitate goal-directed learning.