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  • Poster presentation
  • Open Access

The influence of network structure on neuronal dynamics

BMC Neuroscience201314 (Suppl 1) :P45

  • Published:


  • Network Structure
  • Brain Function
  • Order Approximation
  • Neuronal Network
  • Directed Path
Understanding the influence of network structure on neural dynamics is a fundamental step toward deciphering brain function, yet presents many challenges. We show how networks may be described in terms of the occurrences of certain patterns of edges, and how the frequency of these motifs (see Figure 1) impacts global dynamics. Through analysis and simulation of neuronal networks, we have found that two edge directed paths (two-chains) have the most dramatic effect on dynamics. Our analytic results are based on equations for mean population activity and correlations that we derive using path integrals and moment hierarchy expansions. These equations reveal the primary ways in which the network motifs influence dynamics. For example, the equations indicate that the propensity of a network to globally synchronize increases with the prevalence of two-chains, and we verify this result with network simulations. Finally, we present ongoing work investigating when these second-order equations break down, and how they might be corrected by higher order approximations to the network structure, such as the prevalence of three edge chains beyond that predicted by the two-chains.
Figure 1
Figure 1

The four second order edge motifs of reciprocal, convergent, divergent, and causal connections. The frequency of these motifs determine the second order statistics, or correlations, among the network connections.

Authors’ Affiliations

School of Mathematics, University of Minnesota, Mineapolis, MN 55455, USA
Allen Institute for Brain Science, Seattle, WA 98103, USA


© Campbell et al; licensee BioMed Central Ltd. 2013

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.