Neuron dynamics variability and anomalous phase synchronization of neural networks

Area of Science:

• Neuroscience
• Computational Neuroscience
• Complex Systems

Background:

• Anomalous phase synchronization (APS) is observed in weakly coupled networks, with implications for neurological disorders like Parkinson's and epilepsy.
• The precise origins of APS and the mechanisms for its suppression remain incompletely understood.
• Existing methods for controlling APS involve external perturbations or ambient changes.

Purpose of the Study:

• To investigate the relationship between individual neuron behavior and APS in small-world networks.
• To identify mechanisms underlying the suppression of APS.
• To explore quantifiable measures for assessing APS and its suppression.

Main Methods:

• Simulations of coupled neurons using Hodgkin-Huxley-like and Hindmarsh-Rose models in a small-world topology.
• Analysis of neuron variability, specifically inter-bursting intervals.
• Quantification of APS suppression using the recurrence quantifier 'determinism' on mean field potential.

Main Results:

• A strong correlation was found between neuron variability (inter-bursting intervals) and the occurrence of APS.
• External perturbations or parameter changes that suppress APS also promote small changes in individual neuron dynamics, increasing variability.
• Increasing individual neuron variability was shown to decrease APS.
• The 'determinism' measure effectively quantified the suppression of APS using only the network's mean field potential.

Conclusions:

• Neuron variability is a key factor in the emergence and suppression of anomalous phase synchronization.
• Determinism, calculated from mean field potential, offers a practical method for quantifying APS in experimental settings.
• These findings provide insights into the neural dynamics underlying certain neurological conditions and potential control strategies.