Fast-local and slow-global neural ensembles in the mouse brain

Thomas J. Delaney, Cian O’Donnell

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Abstract

Ensembles of neurons are thought to be coactive when participating in brain computations. However, it is unclear what principles determine whether an ensemble remains localised within a single brain region, or spans multiple brain regions. To address this, we analysed electrophysiological neural population data from hundreds of neurons recorded simultaneously across nine brain regions in awake mice. At fast subsecond timescales, spike count correlations between pairs of neurons in the same brain region were stronger than for pairs of neurons spread across different brain regions. In contrast at slower timescales, within- and between-region spike count correlations were similar. Correlations between high-firing-rate neuron pairs showed a stronger dependence on timescale than low-firing-rate neuron pairs. We applied an ensemble detection algorithm to the neural correlation data and found that at fast timescales each ensemble was mostly contained within a single brain region, whereas at slower timescales ensembles spanned multiple brain regions. These results suggest that the mouse brain may perform fast-local and slow-global computations in parallel.
Original languageEnglish
Pages (from-to)731-742
Number of pages12
JournalNetwork Neuroscience
Volume7
Issue number2
Early online date6 Apr 2023
DOIs
Publication statusPublished online - 6 Apr 2023

Bibliographical note

Funding Information:
Cian O’Donnell, Medical Research Council (https://dx.doi.org/10.13039/501100000265), Award ID: MR/S026630/1. Cian O’Donnell, Engineering and Physical Sciences Research Council, Award ID: EP/N509619/1.

Publisher Copyright:
© 2023.

Keywords

  • Applied Mathematics
  • Artificial Intelligence
  • Computer Science Applications
  • General Neuroscience
  • Multi-timescale
  • Neural correlations
  • Whole-brain computation
  • Electrophysiology
  • Neural ensembles

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