Functional Percolation: A Perspective on Criticality of Form and Function
This addresses the challenge of understanding minimal conditions for information processing in extended systems like neuroscience and AI, offering a universal organizing principle, though it is incremental as it builds on percolation theory.
The study investigated how network connectivity enables information processing by analyzing random networks across the structural percolation transition, finding that the emergence of a giant connected component coincides with sharp transitions in functional diversity, output entropy, and directed information flow, defining a regime of functional percolation.
Understanding the physical constraints and minimal conditions that enable information processing in extended systems remains a central challenge across disciplines, from neuroscience and artificial intelligence to social and physical networks. Here we study how network connectivity both limits and enables information processing by analyzing random networks across the structural percolation transition. Using cascade-mediated dynamics as a minimal and universal mechanism for propagating state-dependent responses, we examine structural, functional, and information-theoretic observables as functions of mean degree in Erdos-Renyi networks. We find that the emergence of a giant connected component coincides with a sharp transition in realizable information processing: complex input-output response functions become accessible, functional diversity increases rapidly, output entropy rises, and directed information flow quantified by transfer entropy extends beyond local neighborhoods. These coincident transitions define a regime of functional percolation, referring to a sharp expansion of the space of realizable input-output functions at the structural percolation transition. Near criticality, networks exhibit a Pareto-optimal tradeoff between functional complexity and diversity, suggesting that percolation criticality provides a universal organizing principle for information processing in systems with local interactions and propagating influences.