L. Peliti

The paper introduces a model showing cooperators can thrive in structured populations through cycles of isolation and merging, despite defectors having an advantage within groups.

The paper introduces an energy-based framework to model memory strategies for evolving patterns, showing that moderate risk tolerance improves classification of dynamic stimuli.

This paper develops a theoretical framework using artificial neural networks to analyze optimal memory strategies for static and evolving patterns, finding that compartmentalized networks are better for dynamic patterns.

The paper shows that transient compartmentalization enables survival of self-replicating molecules and allows sustained concentration oscillations in a system with a replicase and parasite molecule.

The paper extends a framework for transient compartmentalization with selection dynamics by incorporating mutations and noise, showing that noise has minimal impact on phase diagrams due to replication-limited dynamics.

The paper demonstrates that selection dynamics in transient compartments can maintain functional replicators by varying parasite amplification and compartment size, with implications for virus-host ecology.