For many scientific domains, it is important to model the relationship between structure and function of systems with complex interactions among its parts. Graphs are mathematical objects that capture connections between parts of a system, providing a natural language for this. Examples can be found across all levels of neuroscience: chemical structures, network connectivity, functional interactions between brain regions, cognitive models of relational reasoning, and collective behaviors. Machine learning innovation on these models has been rapid in recent years, particularly with the advent of graph neural network methods, and their application to neuro-adjacent domains (e.g. chemistry, molecular biology, dynamical systems) has illustrated their potential. We believe these models can contribute significantly to progress in multiple neuroscience specialties, but overwhelmingly members of the Cosyne community seem to not know about them. This workshop seeks to correct this by highlighting recent applications of graph-based methods in different areas of neuroscience, and by bringing together experimentalists with graph-structured data and theorists with expertise in graph methods. Given the breadth of applications of graph-based approaches, we believe our workshop offers significant potential for catalyzing new research directions for members of the Cosyne community.

Organizers: Brian DePasquale (BU); Kim Stachenfeld (Deepmind & Columbia), Sam Lewallen