Bursting gamma oscillations in neural mass models - Nandi, Manoj Kumar; Valla, Michele; di Volo, Matteo (Frontiers in Computational Neuroscience, 2024)
Gamma oscillations (30-120 Hz) in the brain are not periodic cycles but they typically appear in short time windows often called oscillatory bursts. While the origin of this bursting phenomenon is still unclear, some recent works hypothesize its origin in external or endogenous noise of neural networks. We show that an exact neural mass model of excitatory and inhibitory quadraticintegrate and fire spiking neurons theoretically predicts the emergence of a different regime of intrinsic bursting gamma oscillations (IBG) without any noise source, a phenomenon due to collective chaos. This regime is indeed observed in direct simulation of spiking neurons, characterised by highly irregular spiking activity. IBG oscillations are characterised by higher phase amplitude coupling to slower theta oscillations with respect to noise-induced bursting oscillations, thus indicating an increased capacity for information transfer between brain regions. We show that this phenomenon is present in both globally coupled and sparse networks of spiking neurons. These results propose a new mechanism for gamma oscillatory activity, suggesting deterministic collective chaos as a good candidate for the origin of gamma bursts.