{"type":"journal_article","quality_controlled":"1","title":"Co-dependent excitatory and inhibitory plasticity accounts for quick, stable and long-lasting memories in biological networks","date_published":"2024-03-20T00:00:00Z","article_processing_charge":"Yes (via OA deal)","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.","doi":"10.1038/s41593-024-01597-4","main_file_link":[{"url":"https://doi.org/10.1038/s41593-024-01597-4","open_access":"1"}],"_id":"15171","scopus_import":"1","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public","date_updated":"2024-03-25T07:04:05Z","publication_status":"epub_ahead","publication_identifier":{"issn":["1097-6256"],"eissn":["1546-1726"]},"ec_funded":1,"publication":"Nature Neuroscience","abstract":[{"lang":"eng","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."}],"oa_version":"Published Version","citation":{"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.” Nature Neuroscience, Springer Nature, 2024, doi:10.1038/s41593-024-01597-4.","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,” Nature Neuroscience. Springer Nature, 2024.","short":"E.J. Agnes, T.P. Vogels, Nature Neuroscience (2024).","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.” Nature Neuroscience. Springer Nature, 2024. https://doi.org/10.1038/s41593-024-01597-4.","apa":"Agnes, E. J., & Vogels, T. P. (2024). Co-dependent excitatory and inhibitory plasticity accounts for quick, stable and long-lasting memories in biological networks. Nature Neuroscience. Springer Nature. https://doi.org/10.1038/s41593-024-01597-4","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. Nature Neuroscience. 2024. doi:10.1038/s41593-024-01597-4"},"publisher":"Springer Nature","date_created":"2024-03-24T23:01:00Z","oa":1,"department":[{"_id":"TiVo"}],"project":[{"call_identifier":"H2020","grant_number":"819603","_id":"0aacfa84-070f-11eb-9043-d7eb2c709234","name":"Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning."}],"month":"03","article_type":"original","day":"20","author":[{"full_name":"Agnes, Everton J.","last_name":"Agnes","first_name":"Everton J."},{"first_name":"Tim P","orcid":"0000-0003-3295-6181","last_name":"Vogels","full_name":"Vogels, Tim P","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425"}],"year":"2024","language":[{"iso":"eng"}]}