Spatial and temporal correlations in neural networks with structured connectivity

Yan Liang Shi, Roxana Zeraati, Anna Levina, Tatiana A. Engel

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


This article is part of the Physical Review Research collection titled Physics of Neuroscience. Correlated fluctuations in the activity of neural populations reflect the network's dynamics and connectivity. The temporal and spatial dimensions of neural correlations are interdependent. However, prior theoretical work mainly analyzed correlations in either spatial or temporal domains, oblivious to their interplay. We show that the network dynamics and connectivity jointly define the spatiotemporal profile of neural correlations. We derive analytical expressions for pairwise correlations in networks of binary units with spatially arranged connectivity in one and two dimensions. We find that spatial interactions among units generate multiple timescales in auto- and cross-correlations. Each timescale is associated with fluctuations at a particular spatial frequency, making a hierarchical contribution to the correlations. External inputs can modulate the correlation timescales when spatial interactions are nonlinear, and the modulation effect depends on the operating regime of network dynamics. These theoretical results open new ways to relate connectivity and dynamics in cortical networks via measurements of spatiotemporal neural correlations.

Original languageEnglish (US)
Article number013005
JournalPhysical Review Research
Issue number1
StatePublished - Jan 2023
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Physics and Astronomy


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