Higher-Order Networks in Biological and Neural Systems

Overview

Recent advances across applied mathematics, cell biology, and neuroscience have shown that pairwise network interactions are often insufficient to capture the complex collective behavior of biological and neural systems. Instead, many fundamental processes—from protein interactions and cell signaling to neural computation and synchronization—are best described by higher-order networks, in which dynamical processes are governed by group-level interactions among three or more components. Understanding when such higher-order effects arise, how they process information, and how to model them are key challenges in modern network science.

This workshop will explore the mathematical foundations, computational methods, and biological implications of higher-order interactions in living and bioinspired systems. A central objective is to bridge theoretical advances with biological and neural applications. Topics will include: 1) the role of higher-order structure in dynamical processes such as communication, synchronization, and information processing; 2) the inference of higher-order interactions from high-dimensional biomolecular data; 3) the modeling of collective dynamics in neural systems; and 4) the design of bioinspired and neuromorphic models that explore higher-order network principles. Each thematic session will feature keynote speakers, structured discussions, and sessions for posters and contributed talks. Particular emphasis will be placed on identifying common mathematical structures across these domains and emergent research directions to be addressed.

The workshop is primarily intended for researchers and graduate students working at the intersection of applied mathematics, cell biology, neuroscience, computational modeling, and network science. To promote broad participation, the program will start with a full day tutorial on Monday (Nov 15, 2027) introducing foundational tools in the analysis of higher-order systems.