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Mass synchronization: occurrence and its control with possible applications to brain dynamics.

V K Chandrasekar1, Jane H Sheeba, M Lakshmanan

  • 1Centre for Nonlinear Dynamics, School of Physics, Bharathidasan University, Tiruchirappalli, Tamilnadu 620 024, India. chandru25nld@gmail.com

Chaos (Woodbury, N.Y.)
|January 5, 2011
PubMed
Summary

Pathological brain synchronization arises from strong coupling and synchronized drive populations. A novel feedback method effectively controls this synchronization by adjusting feedback strength and delay based on synchronization characteristics.

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Area of Science:

  • Computational neuroscience
  • Brain dynamics modeling

Background:

  • Pathological brain states are characterized by mass synchronization of neuronal populations.
  • Understanding the mechanisms driving this synchronization is crucial for therapeutic interventions.

Purpose of the Study:

  • To model and analyze the occurrence of mass synchronization in coupled neuronal populations.
  • To identify the underlying causes of pathological synchronization.
  • To propose a novel method for controlling pathological synchronization.

Main Methods:

  • Developed a computational model of coupled phase oscillators representing neuronal populations.
  • Utilized numerical analysis to investigate mass synchronization phenomena.
  • Proposed and analyzed a demand-controlled delayed feedback mechanism.

Main Results:

  • Identified that pathological synchronization results from both increased coupling strength and strong synchronization of the drive population.
  • Demonstrated that a demand-controlled delayed feedback method can effectively control pathological synchronization.
  • Provided an analytical explanation for synchronization occurrence and control in the thermodynamic limit.

Conclusions:

  • Pathological synchronization is a complex phenomenon influenced by coupling and drive synchronization.
  • The proposed feedback control method offers a promising strategy for managing pathological brain synchronization.
  • The study provides a theoretical framework for understanding and intervening in aberrant neural network activity.