Suppression of phase synchronisation in network based on cat's brain

Area of Science:

• Neuroscience
• Computational Neuroscience
• Network Science

Background:

• The cat's cerebral cortex exhibits a clustered network structure.
• Neuronal networks can display small-world properties within individual areas.

Purpose of the Study:

• To investigate methods for suppressing neuronal phase synchronisation in a simulated cat cerebral cortex.
• To compare the efficacy of different control interventions for abnormal brain rhythms.

Main Methods:

• Constructed a clustered network model of the cat's cerebral cortex.
• Simulated three control interventions: delayed feedback, time-periodic driving, and neuron activation.
• Analyzed the suppression of neuronal phase synchronisation under various perturbations.

Main Results:

• Delayed feedback control demonstrated higher efficiency in suppressing synchronisation compared to other methods.
• All tested interventions showed potential for managing abnormal neuronal rhythms.
• Suppression efficiency was evaluated at equal coupling strengths across interventions.

Conclusions:

• Delayed feedback control is an effective strategy for suppressing pathological synchronisation in the cat's cerebral cortex model.
• The findings offer a procedure for mitigating undesired synchronised neuronal activity.
• Network modeling provides insights into controlling brain dynamics.