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From Structure to Activity: Using Centrality Measures to Predict Neuronal Activity.

Jack McKay Fletcher1, Thomas Wennekers1

  • 11 Centre for Robotic and Neural Systems, University of Plymouth, Drake Circus, Plymouth PL48AA, UK.

International Journal of Neural Systems
|January 13, 2017
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Summary

Katz centrality best predicts neuron firing rates based on network structure. This finding applies to various neural networks and offers insights for neuroscience research.

Keywords:
Katz centralityPageRankSpiking neuronsnetwork centralitynetwork topologystructure function relationship

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Area of Science:

  • Computational Neuroscience
  • Network Science
  • Systems Neuroscience

Background:

  • Neural network topology influences neuronal activity.
  • Understanding the relationship between network structure and function is crucial for neuroscience.

Purpose of the Study:

  • To investigate the correlation between neuronal centrality measures and firing rates in neural networks.
  • To identify which centrality measures best predict neuronal activity.

Main Methods:

  • Applied various centrality measures (In-Degree, Closeness, Betweenness, Eigenvector, Katz, PageRank, HITS, NeuronRank) to Leaky-Integrate and Fire neural networks.
  • Studied networks with different connectivity schemes, including purely excitatory and excitatory-inhibitory networks with homogeneous or small-world structures.

Main Results:

  • Katz centrality demonstrated an almost perfect correlation with neuronal firing rates across all studied network types.
  • Identified specific network properties that underpin this strong correlation.

Conclusions:

  • Katz centrality is a superior predictor of neuronal firing rates compared to other measures, likely due to its ability to capture network disinhibition.
  • Findings are relevant for neuroscientists analyzing functional brain networks and for cognitive models utilizing centrality measures.