{"type":"journal_article","citation":{"short":"E.J. Agnes, T.P. Vogels, Nature Neuroscience (2024).","apa":"Agnes, E. J., &#38; Vogels, T. P. (2024). Co-dependent excitatory and inhibitory plasticity accounts for quick, stable and long-lasting memories in biological networks. <i>Nature Neuroscience</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41593-024-01597-4\">https://doi.org/10.1038/s41593-024-01597-4</a>","ista":"Agnes EJ, Vogels TP. 2024. Co-dependent excitatory and inhibitory plasticity accounts for quick, stable and long-lasting memories in biological networks. Nature Neuroscience.","ama":"Agnes EJ, Vogels TP. Co-dependent excitatory and inhibitory plasticity accounts for quick, stable and long-lasting memories in biological networks. <i>Nature Neuroscience</i>. 2024. doi:<a href=\"https://doi.org/10.1038/s41593-024-01597-4\">10.1038/s41593-024-01597-4</a>","chicago":"Agnes, Everton J., and Tim P Vogels. “Co-Dependent Excitatory and Inhibitory Plasticity Accounts for Quick, Stable and Long-Lasting Memories in Biological Networks.” <i>Nature Neuroscience</i>. Springer Nature, 2024. <a href=\"https://doi.org/10.1038/s41593-024-01597-4\">https://doi.org/10.1038/s41593-024-01597-4</a>.","ieee":"E. J. Agnes and T. P. Vogels, “Co-dependent excitatory and inhibitory plasticity accounts for quick, stable and long-lasting memories in biological networks,” <i>Nature Neuroscience</i>. Springer Nature, 2024.","mla":"Agnes, Everton J., and Tim P. Vogels. “Co-Dependent Excitatory and Inhibitory Plasticity Accounts for Quick, Stable and Long-Lasting Memories in Biological Networks.” <i>Nature Neuroscience</i>, Springer Nature, 2024, doi:<a href=\"https://doi.org/10.1038/s41593-024-01597-4\">10.1038/s41593-024-01597-4</a>."},"oa_version":"Published Version","month":"03","title":"Co-dependent excitatory and inhibitory plasticity accounts for quick, stable and long-lasting memories in biological networks","publication_identifier":{"eissn":["1546-1726"],"issn":["1097-6256"]},"doi":"10.1038/s41593-024-01597-4","_id":"15171","publication_status":"epub_ahead","oa":1,"scopus_import":"1","publisher":"Springer Nature","department":[{"_id":"TiVo"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"first_name":"Everton J.","full_name":"Agnes, Everton J.","last_name":"Agnes"},{"last_name":"Vogels","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","orcid":"0000-0003-3295-6181","first_name":"Tim P","full_name":"Vogels, Tim P"}],"language":[{"iso":"eng"}],"date_published":"2024-03-20T00:00:00Z","article_processing_charge":"Yes (via OA deal)","year":"2024","date_created":"2024-03-24T23:01:00Z","publication":"Nature Neuroscience","article_type":"original","project":[{"grant_number":"819603","call_identifier":"H2020","_id":"0aacfa84-070f-11eb-9043-d7eb2c709234","name":"Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning."}],"ec_funded":1,"main_file_link":[{"open_access":"1","url":"https://doi.org/10.1038/s41593-024-01597-4"}],"acknowledgement":"We thank C. Currin, B. Podlaski and the members of the Vogels group for fruitful discussions. E.J.A. and T.P.V. were supported by a Research Project Grant from the Leverhulme Trust (RPG-2016-446; TPV), a Sir Henry Dale Fellowship from the Wellcome Trust and the Royal Society (WT100000; T.P.V.), a Wellcome Trust Senior Research Fellowship (214316/Z/18/Z; T.P.V.) and a European Research Council Consolidator Grant (SYNAPSEEK, 819603; T.P.V.). For the purpose of open access, the authors have applied a CC BY public copyright license to any author accepted manuscript version arising from this submission. Open access funding provided by University of Basel.","quality_controlled":"1","day":"20","status":"public","abstract":[{"text":"The brain’s functionality is developed and maintained through synaptic plasticity. As synapses undergo plasticity, they also affect each other. The nature of such ‘co-dependency’ is difficult to disentangle experimentally, because multiple synapses must be monitored simultaneously. To help understand the experimentally observed phenomena, we introduce a framework that formalizes synaptic co-dependency between different connection types. The resulting model explains how inhibition can gate excitatory plasticity while neighboring excitatory–excitatory interactions determine the strength of long-term potentiation. Furthermore, we show how the interplay between excitatory and inhibitory synapses can account for the quick rise and long-term stability of a variety of synaptic weight profiles, such as orientation tuning and dendritic clustering of co-active synapses. In recurrent neuronal networks, co-dependent plasticity produces rich and stable motor cortex-like dynamics with high input sensitivity. Our results suggest an essential role for the neighborly synaptic interaction during learning, connecting micro-level physiology with network-wide phenomena.","lang":"eng"}],"date_updated":"2024-03-25T07:04:05Z"}