{"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/cortical-circuits-from-scratch-a-metaplastic","title":"Cortical Circuits from Scratch: A Metaplastic Architecture for the Emergence of Lognormal Firing Rates and Realistic Topology","arxiv_id":"1706.00133","date":"2017-06-01","proceeding":null,"authors":["Zoe Tosi","John Beggs"],"abstract":"Our current understanding of neuroplasticity paints a picture of a complex\ninterconnected system of dependent processes which shape cortical structure so\nas to produce an efficient information processing system. Indeed, the\ncooperation of these processes is associated with robust, stable, adaptable\nnetworks with characteristic features of activity and synaptic topology.\nHowever, combining the actions of these mechanisms in models has proven\nexceptionally difficult and to date no model has been able to do so without\nsignificant hand-tuning. Until such a model exists that can successfully\ncombine these mechanisms to form a stable circuit with realistic features, our\nability to study neuroplasticity in the context of (more realistic) dynamic\nnetworks and potentially reap whatever rewards these features and mechanisms\nimbue biological networks with is hindered. We introduce a model which combines\nfive known plasticity mechanisms that act on the network as well as a unique\nmetaplastic mechanism which acts on other plasticity mechanisms, to produce a\nneural circuit model which is both stable and capable of broadly reproducing\nmany characteristic features of cortical networks. The MANA (metaplastic\nartificial neural architecture) represents the first model of its kind in that\nit is able to self-organize realistic, nonrandom features of cortical networks,\nfrom a null initial state (no synaptic connectivity or neuronal\ndifferentiation). In the same vein as models like the SORN (self-organizing\nrecurrent network) MANA represents further progress toward the reverse\nengineering of the brain at the network level.","url_abs":"http://arxiv.org/abs/1706.00133v2","url_pdf":"http://arxiv.org/pdf/1706.00133v2.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":"cortical-circuits-from-scratch-a-metaplastic","repo_url":"https://github.com/ztosi/MANA-The-Metaplastic-Artificial-Neural-Architecture","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}