Macroscopic Gamma Oscillation With Bursting Neuron Model Under Stochastic Fluctuation

Area of Science:

• Computational Neuroscience
• Neural Dynamics
• Systems Neuroscience

Background:

• Gamma oscillations are crucial for brain information processing.
• Bursting neurons are key contributors to gamma oscillations, but their specific impact remains unclear.
• Understanding bursting neuron properties is vital for explaining gamma oscillation emergence and synchronization.

Purpose of the Study:

• To investigate how bursting neuron properties influence gamma oscillation dynamics.
• To analyze the effects of bursting ratio and stochastic fluctuations on neuronal population synchronization.
• To elucidate the role of bursting neurons in generating and synchronizing gamma oscillations.

Main Methods:

• Developed a novel bursting neuron model to analyze bursting ratio and phase response functions.
• Theoretically analyzed neuronal population dynamics using excitatory and inhibitory bursting neurons.
• Employed bifurcation analysis of the Fokker-Planck equation and adjoint methods for macroscopic phase response function analysis.

Main Results:

• Identified three types of gamma oscillations (unimodal, bimodal in inhibitory, bimodal in excitatory populations) based on interaction strengths.
• Demonstrated that inhibitory neuron doublets facilitate high-frequency oscillation synchronization.
• Found that decreasing the bursting ratio enhances the high-gamma component and improves synchronization with faster oscillatory input.

Conclusions:

• Bursting neuron properties critically shape population-level gamma oscillations.
• The bursting ratio is a key parameter influencing neuronal synchronization and response to oscillatory input.
• This study provides analytical insights into the complex dynamics of bursting neuron populations and their role in rhythmic brain activity.