Cross frequency coupling in next generation inhibitory neural mass models.

Area of Science:

• Computational neuroscience
• Systems neuroscience
• Theoretical neuroscience

Background:

• Neural rhythm coupling is fundamental to brain cognitive processes.
• Understanding collective oscillations (COs) in neural networks is key.

Purpose of the Study:

• To develop a rigorous neural mass model for COs from spiking network dynamics.
• To investigate dynamical regimes and cross-frequency couplings (CFCs) in coupled inhibitory neural populations.

Main Methods:

• Derivation of a neural mass model from microscopic inhibitory spiking network dynamics with exponential synapses.
• Analysis of collective oscillations emerging via a super-critical Hopf bifurcation.
• Simulation of master-slave and bidirectional coupling configurations between two inhibitory populations.

Main Results:

• Model autonomously generates COs, with frequencies tunable by synaptic parameters and excitability.
• Demonstrated various dynamical regimes (damped oscillations, periodic, quasi-periodic, chaos) in master-slave configurations.
• Observed phase-phase and phase-amplitude theta-gamma CFCs in bidirectionally coupled populations.
• External theta forcing enhanced theta-gamma COs coupling, mimicking biological circuit modulation.

Conclusions:

• The developed neural mass model accurately captures essential dynamics of inhibitory neural networks.
• The study elucidates mechanisms of complex dynamics and CFCs, relevant for understanding brain function.
• Findings provide insights into neural synchrony and information processing in cognitive tasks.