Bursting gamma oscillations in neural mass models.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Theoretical Neuroscience

Background:

• Gamma oscillations (30-120 Hz) in the brain manifest as transient bursts, not continuous cycles.
• The precise origin of these oscillatory bursts remains an open question in neuroscience.
• Current hypotheses often attribute gamma bursts to external or internal noise in neural networks.

Purpose of the Study:

• To investigate an alternative, noise-free mechanism for the generation of gamma oscillatory bursts.
• To theoretically predict and computationally verify a novel regime of intrinsic bursting gamma (IBG) oscillations.
• To explore the properties and implications of IBG oscillations for neural information processing.

Main Methods:

• Utilized an exact neural mass model comprising excitatory and inhibitory quadratic-integrate and fire-spiking neurons.
• Performed direct simulations of spiking neural networks, both globally coupled and sparse.
• Analyzed the emergent oscillatory dynamics, focusing on bursting phenomena and phase-amplitude coupling.

Main Results:

• The neural mass model theoretically predicted a novel regime of intrinsic bursting gamma (IBG) oscillations.
• Direct simulations confirmed the emergence of IBG oscillations, characterized by irregular spiking activity.
• IBG oscillations exhibited stronger phase-amplitude coupling with theta oscillations compared to noise-induced bursts.
• This phenomenon was observed in both globally coupled and sparse neural network configurations.

Conclusions:

• Deterministic collective chaos, rather than noise, can intrinsically generate gamma oscillatory bursts.
• IBG oscillations represent a distinct mode of gamma activity with enhanced information transfer capacity.
• Collective chaos provides a plausible new mechanism for the generation of gamma bursts in the brain.