{"year":"2023","abstract":[{"lang":"eng","text":"Animals exhibit a remarkable ability to learn and remember new behaviors, skills, and associations throughout their lifetime. These capabilities are made possible thanks to a variety of\r\nchanges in the brain throughout adulthood, regrouped under the term \"plasticity\". Some cells\r\nin the brain —neurons— and specifically changes in the connections between neurons, the\r\nsynapses, were shown to be crucial for the formation, selection, and consolidation of memories\r\nfrom past experiences. These ongoing changes of synapses across time are called synaptic\r\nplasticity. Understanding how a myriad of biochemical processes operating at individual\r\nsynapses can somehow work in concert to give rise to meaningful changes in behavior is a\r\nfascinating problem and an active area of research.\r\nHowever, the experimental search for the precise plasticity mechanisms at play in the brain\r\nis daunting, as it is difficult to control and observe synapses during learning. Theoretical\r\napproaches have thus been the default method to probe the plasticity-behavior connection. Such\r\nstudies attempt to extract unifying principles across synapses and model all observed synaptic\r\nchanges using plasticity rules: equations that govern the evolution of synaptic strengths across\r\ntime in neuronal network models. These rules can use many relevant quantities to determine\r\nthe magnitude of synaptic changes, such as the precise timings of pre- and postsynaptic\r\naction potentials, the recent neuronal activity levels, the state of neighboring synapses, etc.\r\nHowever, analytical studies rely heavily on human intuition and are forced to make simplifying\r\nassumptions about plasticity rules.\r\nIn this thesis, we aim to assist and augment human intuition in this search for plasticity rules.\r\nWe explore whether a numerical approach could automatically discover the plasticity rules\r\nthat elicit desired behaviors in large networks of interconnected neurons. This approach is\r\ndubbed meta-learning synaptic plasticity: learning plasticity rules which themselves will make\r\nneuronal networks learn how to solve a desired task. We first write all the potential plasticity\r\nmechanisms to consider using a single expression with adjustable parameters. We then optimize\r\nthese plasticity parameters using evolutionary strategies or Bayesian inference on tasks known\r\nto involve synaptic plasticity, such as familiarity detection and network stabilization.\r\nWe show that these automated approaches are powerful tools, able to complement established\r\nanalytical methods. By comprehensively screening plasticity rules at all synapse types in\r\nrealistic, spiking neuronal network models, we discover entire sets of degenerate plausible\r\nplasticity rules that reliably elicit memory-related behaviors. Our approaches allow for more\r\nrobust experimental predictions, by abstracting out the idiosyncrasies of individual plasticity\r\nrules, and provide fresh insights on synaptic plasticity in spiking network models.\r\n"}],"language":[{"iso":"eng"}],"day":"12","author":[{"first_name":"Basile J","last_name":"Confavreux","full_name":"Confavreux, Basile J","id":"C7610134-B532-11EA-BD9F-F5753DDC885E"}],"page":"148","ddc":["610"],"publication_identifier":{"issn":["2663 - 337X"]},"article_processing_charge":"No","related_material":{"record":[{"id":"9633","status":"public","relation":"part_of_dissertation"}]},"title":"Synapseek: Meta-learning synaptic plasticity rules","citation":{"ieee":"B. J. Confavreux, “Synapseek: Meta-learning synaptic plasticity rules,” Institute of Science and Technology Austria, 2023.","short":"B.J. Confavreux, Synapseek: Meta-Learning Synaptic Plasticity Rules, Institute of Science and Technology Austria, 2023.","apa":"Confavreux, B. J. (2023). <i>Synapseek: Meta-learning synaptic plasticity rules</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/at:ista:14422\">https://doi.org/10.15479/at:ista:14422</a>","chicago":"Confavreux, Basile J. “Synapseek: Meta-Learning Synaptic Plasticity Rules.” Institute of Science and Technology Austria, 2023. <a href=\"https://doi.org/10.15479/at:ista:14422\">https://doi.org/10.15479/at:ista:14422</a>.","ama":"Confavreux BJ. Synapseek: Meta-learning synaptic plasticity rules. 2023. doi:<a href=\"https://doi.org/10.15479/at:ista:14422\">10.15479/at:ista:14422</a>","mla":"Confavreux, Basile J. <i>Synapseek: Meta-Learning Synaptic Plasticity Rules</i>. Institute of Science and Technology Austria, 2023, doi:<a href=\"https://doi.org/10.15479/at:ista:14422\">10.15479/at:ista:14422</a>.","ista":"Confavreux BJ. 2023. Synapseek: Meta-learning synaptic plasticity rules. Institute of Science and Technology Austria."},"tmp":{"short":"CC BY-NC-SA (4.0)","image":"/images/cc_by_nc_sa.png","name":"Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode"},"has_accepted_license":"1","date_published":"2023-10-12T00:00:00Z","publication_status":"published","alternative_title":["ISTA Thesis"],"month":"10","date_created":"2023-10-12T14:13:25Z","license":"https://creativecommons.org/licenses/by-nc-sa/4.0/","supervisor":[{"id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","full_name":"Vogels, Tim P","orcid":"0000-0003-3295-6181","last_name":"Vogels","first_name":"Tim P"}],"publisher":"Institute of Science and Technology Austria","ec_funded":1,"file_date_updated":"2023-10-18T07:56:08Z","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","type":"dissertation","date_updated":"2023-10-18T09:20:56Z","doi":"10.15479/at:ista:14422","oa_version":"Published Version","department":[{"_id":"GradSch"},{"_id":"TiVo"}],"project":[{"_id":"0aacfa84-070f-11eb-9043-d7eb2c709234","name":"Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning.","grant_number":"819603","call_identifier":"H2020"}],"status":"public","degree_awarded":"PhD","file":[{"date_created":"2023-10-12T14:53:50Z","embargo":"2024-10-12","file_size":30599717,"access_level":"closed","creator":"cchlebak","date_updated":"2023-10-12T14:54:52Z","relation":"main_file","file_id":"14424","content_type":"application/pdf","embargo_to":"open_access","checksum":"7f636555eae7803323df287672fd13ed","file_name":"Confavreux_Thesis_2A.pdf"},{"file_name":"Confavreux Thesis.zip","checksum":"725e85946db92290a4583a0de9779e1b","content_type":"application/x-zip-compressed","file_id":"14440","relation":"source_file","date_updated":"2023-10-18T07:56:08Z","creator":"cchlebak","access_level":"closed","file_size":68406739,"date_created":"2023-10-18T07:38:34Z"}],"_id":"14422"}