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Study of synaptic plasticity via random graphs.

Tatyana S Turova1

  • 1Mathematical Center, Lund University, S-22100 Lund, Sweden. tatyana@maths.lth.se

Bio Systems
|December 3, 2002
PubMed
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This study introduces dynamical random graphs for biological neural networks. Cycles emerge as stable structures, crucial for neuronal synchronization when using the Hebb rule.

Area of Science:

  • Computational Neuroscience
  • Network Science
  • Systems Biology

Background:

  • Biological neural networks exhibit complex dynamics.
  • Understanding network stability and information processing is crucial.
  • Dynamical random graphs offer a framework to model these networks.

Purpose of the Study:

  • To introduce and study dynamical random graphs for biological neural networks.
  • To identify parameters enabling stable, strongly connected neuronal groups.
  • To investigate the role of network structures in neuronal synchronization.

Main Methods:

  • Introduction and analysis of dynamical random graphs.
  • Phase diagram analysis of single neuron and synaptic strength parameters.
  • Implementation of the Hebb rule in excitatory neuronal network dynamics.

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Main Results:

  • Identification of parameters for stable, strongly connected neuronal groups.
  • Demonstration that cycles are the most stable structures under Hebbian dynamics.
  • Revealed the role of cycles in synchronizing neuronal activity.

Conclusions:

  • Dynamical random graphs provide insights into neural network organization.
  • Cyclical structures are key to stability and synchronization in Hebbian networks.
  • This framework aids in understanding large-scale neuronal coordination.