The Topological Reinforcement Operator (TRO): A Parsimony Principle for Memory Consolidation in Complex Networks

The Topological Reinforcement Operator (TRO): A Parsimony Principle for Memory Consolidation in Complex Networks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Topological Reinforcement Operator (TRO): A Parsimony Principle for Memory Consolidation in Complex Networks José Ignacio Peinador Sala This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7808963/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Memory engram consolidation is a central challenge in computational neuroscience. This work introduces the Topological Reinforcement Operator (ORT), a posttraining mechanism that reinforces topologically relevant nodes to induce functional engrams in complex networks. We validated the ORT using a robust functional protocol based on normalized diffusion and F1-score, applied to citation networks (Cora, Citeseer, Pubmed) and biological connectomes (macaque, human). The results reveal a dual consolidation principle: in information networks, memory resilience emerges from broad critical mass cores (P90), whereas in optimized biological networks, a smaller elite core (P95) predominates, achieving a performance of up to 87.4% in the human connectome. Finally, we demonstrate that the ORT, based on degree centrality, is ∼96 times faster than PageRank, establishing a principle of computational parsimony that links structure, function, and efficiency in neural networks. Computational Neuroscience Systems and Networking Artificial Intelligence and Machine Learning Cognitive Neuroscience Theoretical Computer Science Memory Consolidation Computational Neuroscience Complex Networks Connectome Network Topology Computational Parsimony Graph Neural Networks Continual Learning Graph Pruning Engram Connectome Rich-Club Organization Brain Networks Network Neuroscience Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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