Overview
This full-day workshop explores renormalization as a unifying framework for understanding neural computation across scales: from local circuit dynamics to cognitive representations. We gather leading theorists, experimentalists, and modelers to investigate systematic coarse‑graining in neural systems: from local interactions to global dynamics, across‑level information transformation, and the rise of abstraction and generalization.
Designed for those coming from various backgrounds but similar research goals, the workshop aims to align concepts and methods so attendees leave with a common vocabulary and a shared understanding of the key principles and methods of renormalization in neuroscience.
Key Themes
Multi-Scale Dynamics
How local interactions generate emergent global behavior across temporal and spatial scales
Higher-Order Structure
The role of simplicial complexes and topological features in neural computation
Cognitive Representations
Geometric and topological structure of neural representations from cells to systems
Scale Invariance
Principles of coarse-graining that preserve essential computational properties
Keynote Speakers
Talk: Neural representational geometry as a lens to understand cognitive strategies in monkeys and recurrent neural networks
Talk: Neural population geometry and the emergence of abstraction across scales
Talk: Maximizing computational capacity and adaptability in an unpredictable world.
Talk: Synaptic plasticity shapes network structure and function across scales
Talk: A scale-dependent neural code: from local lack of structure to the large-scale organization of the brain
Workshop Schedule
Organizers
Jesseba Fernando
Network Science Institute, Northeastern University
Network reorganization during learning, bridging systems neuroscience and AI through information-theoretic approaches.
Giovanni Petri
Network Science Institute, Northeastern University London
Topological data analysis of brain networks, higher-order interactions, and information geometry. Develops frameworks for analyzing multi-scale brain dynamics through algebraic topology.
Andrea Santoro
ISI Foundation
Higher-order interaction modeling, time-series analysis, and representation geometry in neural systems. Bridges information theory and empirical neuroscience.