{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/optimal-modularity-and-memory-capacity-of","title":"Optimal modularity and memory capacity of neural reservoirs","arxiv_id":"1706.06511","date":"2017-06-20","proceeding":null,"authors":["Nathaniel Rodriguez","Eduardo Izquierdo","Yong-Yeol Ahn"],"abstract":"The neural network is a powerful computing framework that has been exploited\nby biological evolution and by humans for solving diverse problems. Although\nthe computational capabilities of neural networks are determined by their\nstructure, the current understanding of the relationships between a neural\nnetwork's architecture and function is still primitive. Here we reveal that\nneural network's modular architecture plays a vital role in determining the\nneural dynamics and memory performance of the network of threshold neurons. In\nparticular, we demonstrate that there exists an optimal modularity for memory\nperformance, where a balance between local cohesion and global connectivity is\nestablished, allowing optimally modular networks to remember longer. Our\nresults suggest that insights from dynamical analysis of neural networks and\ninformation spreading processes can be leveraged to better design neural\nnetworks and may shed light on the brain's modular organization.","url_abs":"http://arxiv.org/abs/1706.06511v3","url_pdf":"http://arxiv.org/pdf/1706.06511v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"optimal-modularity-and-memory-capacity-of","repo_url":"https://github.com/Nathaniel-Rodriguez/reservoirlib","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1706.06511","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1706.06511"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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