BIMSA > BIMSA Computational Math Seminar Phase-locking patterns in oscillatory neural networks described by exact mean-field models
Phase-locking patterns in oscillatory neural networks described by exact mean-field models
Organizers
Pipi Hu , Xin Liang , Zhiting Ma , Hamid Mofidi , Axel G.R. Turnquist , Li Wang , Fansheng Xiong , Shuo Yang , Wuyue Yang
Speaker
Gemma Huguet
Time
Thursday, October 16, 2025 3:15 PM - 4:15 PM
Venue
Online
Online
Zoom 928 682 9093 (BIMSA)
Abstract
Macroscopic oscillatory patterns are a ubiquitous feature of brain dynamics and have been linked to diverse cognitive functions, such as perception, attention and memory. Within the Communication Through Coherence framework (Fries, 2005, 2015), oscillations are thought to enable effective neuronal communication between neural populations by aligning phases appropriately across interacting populations. To investigate how such coordination arises, we consider a new generation of neural mass models that provide an exact description of the macroscopic activity of populations of spiking neurons. Through detailed analysis, we will explore phase-locking patterns and optimal conditions for communication. Our approach integrates advanced tools from dynamical systems, such as the parameterization method for invariant manifolds, with recent numerical advancements that enable accurate analysis of dynamics via phase–amplitude descriptions and control of oscillatory frequency and phase alignment for effective communication.