Fabrizio Damicelli

This study explores how real brain connectivity patterns can inform artificial neural network architectures, finding that biologically inspired networks perform as well as classical ones with sufficient randomness and diversity in connections.

This paper explores bio-instantiated recurrent neural networks, examining their performance in working memory tasks and demonstrating how empirical neural network data can be used to construct biologically realistic topologies.

This paper introduces a topological reinforcement model that evolves synthetic networks toward modularity, suggesting it may underlie modular structure in brain networks.