{"citation":{"short":"B.J. Confavreux, E.J. Agnes, F. Zenke, H. Sprekeler, T.P. Vogels, PLoS Computational Biology 21 (2025).","ieee":"B. J. Confavreux, E. J. Agnes, F. Zenke, H. Sprekeler, and T. P. Vogels, “Balancing complexity, performance and plausibility to meta learn plasticity rules in recurrent spiking networks,” <i>PLoS Computational Biology</i>, vol. 21, no. 4. Public Library of Science, 2025.","ista":"Confavreux BJ, Agnes EJ, Zenke F, Sprekeler H, Vogels TP. 2025. Balancing complexity, performance and plausibility to meta learn plasticity rules in recurrent spiking networks. PLoS Computational Biology. 21(4), e1012910.","mla":"Confavreux, Basile J., et al. “Balancing Complexity, Performance and Plausibility to Meta Learn Plasticity Rules in Recurrent Spiking Networks.” <i>PLoS Computational Biology</i>, vol. 21, no. 4, e1012910, Public Library of Science, 2025, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1012910\">10.1371/journal.pcbi.1012910</a>.","ama":"Confavreux BJ, Agnes EJ, Zenke F, Sprekeler H, Vogels TP. Balancing complexity, performance and plausibility to meta learn plasticity rules in recurrent spiking networks. <i>PLoS Computational Biology</i>. 2025;21(4). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1012910\">10.1371/journal.pcbi.1012910</a>","apa":"Confavreux, B. J., Agnes, E. J., Zenke, F., Sprekeler, H., &#38; Vogels, T. P. (2025). Balancing complexity, performance and plausibility to meta learn plasticity rules in recurrent spiking networks. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1012910\">https://doi.org/10.1371/journal.pcbi.1012910</a>","chicago":"Confavreux, Basile J, Everton J. Agnes, Friedemann Zenke, Henning Sprekeler, and Tim P Vogels. “Balancing Complexity, Performance and Plausibility to Meta Learn Plasticity Rules in Recurrent Spiking Networks.” <i>PLoS Computational Biology</i>. Public Library of Science, 2025. <a href=\"https://doi.org/10.1371/journal.pcbi.1012910\">https://doi.org/10.1371/journal.pcbi.1012910</a>."},"publication":"PLoS Computational Biology","date_created":"2025-05-04T22:02:31Z","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"oa":1,"external_id":{"pmid":["40273284 "]},"article_number":"e1012910","language":[{"iso":"eng"}],"has_accepted_license":"1","date_updated":"2025-05-05T11:19:58Z","year":"2025","pmid":1,"author":[{"last_name":"Confavreux","full_name":"Confavreux, Basile J","id":"C7610134-B532-11EA-BD9F-F5753DDC885E","first_name":"Basile J"},{"first_name":"Everton J.","full_name":"Agnes, Everton J.","last_name":"Agnes"},{"last_name":"Zenke","full_name":"Zenke, Friedemann","first_name":"Friedemann"},{"first_name":"Henning","full_name":"Sprekeler, Henning","last_name":"Sprekeler"},{"orcid":"0000-0003-3295-6181","last_name":"Vogels","full_name":"Vogels, Tim P","first_name":"Tim P","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425"}],"title":"Balancing complexity, performance and plausibility to meta learn plasticity rules in recurrent spiking networks","quality_controlled":"1","issue":"4","department":[{"_id":"TiVo"}],"month":"04","doi":"10.1371/journal.pcbi.1012910","file_date_updated":"2025-05-05T11:17:49Z","acknowledgement":"We would like to thank Chaitanya Chintaluri, Nicoleta Condruz and Douglas Feitosa Tomé for insightful discussions.","publication_status":"published","day":"24","related_material":{"link":[{"url":"https://github.com/VogelsLab/SpikES","relation":"software"}]},"corr_author":"1","type":"journal_article","OA_type":"gold","intvolume":"        21","volume":21,"publisher":"Public Library of Science","ddc":["570"],"article_processing_charge":"Yes","file":[{"content_type":"application/pdf","checksum":"6437a1aab52813ab7e310e3b4fb36e3b","creator":"dernst","access_level":"open_access","relation":"main_file","file_size":9771636,"date_created":"2025-05-05T11:17:49Z","success":1,"file_name":"2025_PLoSCompBio_Confavreux.pdf","file_id":"19654","date_updated":"2025-05-05T11:17:49Z"}],"_id":"19640","DOAJ_listed":"1","status":"public","abstract":[{"text":"Synaptic plasticity is a key player in the brain’s life-long learning abilities. However, due to experimental limitations, the mechanistic link between synaptic plasticity rules and the network-level computations they enable remain opaque. Here we use evolutionary strategies (ES) to meta learn local co-active plasticity rules in large recurrent spiking networks with excitatory (E) and inhibitory (I) neurons, using parameterizations of increasing complexity. We discover rules that robustly stabilize network dynamics for all four synapse types acting in isolation (E-to-E, E-to-I, I-to-E and I-to-I). More complex functions such as familiarity detection can also be included in the search constraints. However, our meta learning strategy begins to fail for co-active rules of increasing complexity, as it is challenging to devise loss functions that effectively constrain network dynamics to plausible solutions a priori. Moreover, in line with previous work, we can find multiple degenerate solutions with identical network behaviour. As a local optimization strategy, ES provides one solution at a time and makes exploration of this degeneracy cumbersome. Regardless, we can glean the interdependecies of various plasticity parameters by considering the covariance matrix learned alongside the optimal rule with ES. Our work provides a proof of principle for the success of machine-learning-guided discovery of plasticity rules in large spiking networks, and points at the necessity of more elaborate search strategies going forward.","lang":"eng"}],"scopus_import":"1","publication_identifier":{"issn":["1553-734X"],"eissn":["1553-7358"]},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_type":"original","date_published":"2025-04-24T00:00:00Z","oa_version":"Published Version","OA_place":"publisher"}