Abstract

Oscillations are ubiquitous in the brain, and they have been associated with several cognitive functions, such as perception and attention. The Communication Through Coherence theory (Fries, 2005, 2015) proposes that synchronized oscillations at specific phases are essential for effective neuronal communication. In this presentation, we study oscillations in neural networks using exact mean-field models, which provide a precise description of a network's macroscopic activity. Through detailed analysis, we will explore phase-locking patterns and optimal conditions for communication. Our approach integrates tools from the field of dynamical systems, such as the parameterization method for invariant manifolds, along with analysis of dynamics through phase-amplitude description.  We also consider strategies for regulating oscillatory frequency and phase alignment for communication.

This methodology enhances our understanding of oscillatory dynamics in neural networks, providing insights particularly relevant for communication.