[{"oa_version":"Published Version","has_accepted_license":"1","year":"2023","publication_identifier":{"issn":["2663 - 337X"]},"date_updated":"2023-10-18T09:20:56Z","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","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"}],"_id":"14422","date_published":"2023-10-12T00:00:00Z","file":[{"embargo_to":"open_access","date_created":"2023-10-12T14:53:50Z","relation":"main_file","file_id":"14424","content_type":"application/pdf","checksum":"7f636555eae7803323df287672fd13ed","access_level":"closed","date_updated":"2023-10-12T14:54:52Z","file_size":30599717,"creator":"cchlebak","file_name":"Confavreux_Thesis_2A.pdf","embargo":"2024-10-12"},{"date_created":"2023-10-18T07:38:34Z","file_id":"14440","relation":"source_file","content_type":"application/x-zip-compressed","access_level":"closed","date_updated":"2023-10-18T07:56:08Z","checksum":"725e85946db92290a4583a0de9779e1b","creator":"cchlebak","file_size":68406739,"file_name":"Confavreux Thesis.zip"}],"article_processing_charge":"No","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode","image":"/images/cc_by_nc_sa.png","name":"Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)","short":"CC BY-NC-SA (4.0)"},"file_date_updated":"2023-10-18T07:56:08Z","publication_status":"published","author":[{"last_name":"Confavreux","full_name":"Confavreux, Basile J","first_name":"Basile J","id":"C7610134-B532-11EA-BD9F-F5753DDC885E"}],"type":"dissertation","alternative_title":["ISTA Thesis"],"day":"12","title":"Synapseek: Meta-learning synaptic plasticity rules","citation":{"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>.","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>.","ista":"Confavreux BJ. 2023. Synapseek: Meta-learning synaptic plasticity rules. Institute of Science and Technology Austria.","ieee":"B. J. Confavreux, “Synapseek: Meta-learning synaptic plasticity rules,” Institute of Science and Technology Austria, 2023.","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>","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>","short":"B.J. Confavreux, Synapseek: Meta-Learning Synaptic Plasticity Rules, Institute of Science and Technology Austria, 2023."},"ec_funded":1,"doi":"10.15479/at:ista:14422","ddc":["610"],"language":[{"iso":"eng"}],"project":[{"_id":"0aacfa84-070f-11eb-9043-d7eb2c709234","call_identifier":"H2020","grant_number":"819603","name":"Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning."}],"related_material":{"record":[{"status":"public","id":"9633","relation":"part_of_dissertation"}]},"supervisor":[{"orcid":"0000-0003-3295-6181","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","last_name":"Vogels","full_name":"Vogels, Tim P","first_name":"Tim P"}],"date_created":"2023-10-12T14:13:25Z","month":"10","page":"148","status":"public","department":[{"_id":"GradSch"},{"_id":"TiVo"}],"degree_awarded":"PhD","publisher":"Institute of Science and Technology Austria"},{"scopus_import":"1","external_id":{"pmid":["35114107"],"isi":["000751819100005"]},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_updated":"2023-10-03T10:53:17Z","publication_identifier":{"eissn":["1097-4199"]},"article_type":"letter_note","year":"2022","oa_version":"Published Version","volume":110,"publication_status":"published","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1016/j.neuron.2022.01.014"}],"oa":1,"date_published":"2022-02-02T00:00:00Z","_id":"10753","abstract":[{"text":"This is a comment on \"Meta-learning synaptic plasticity and memory addressing for continual familiarity detection.\" Neuron. 2022 Feb 2;110(3):544-557.e8.","lang":"eng"}],"article_processing_charge":"No","issue":"3","language":[{"iso":"eng"}],"doi":"10.1016/j.neuron.2022.01.014","pmid":1,"day":"02","author":[{"id":"C7610134-B532-11EA-BD9F-F5753DDC885E","first_name":"Basile J","full_name":"Confavreux, Basile J","last_name":"Confavreux"},{"first_name":"Tim P","full_name":"Vogels, Tim P","last_name":"Vogels","orcid":"0000-0003-3295-6181","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425"}],"type":"journal_article","citation":{"short":"B.J. Confavreux, T.P. Vogels, Neuron 110 (2022) 361–362.","apa":"Confavreux, B. J., &#38; Vogels, T. P. (2022). A familiar thought: Machines that replace us? <i>Neuron</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.neuron.2022.01.014\">https://doi.org/10.1016/j.neuron.2022.01.014</a>","ama":"Confavreux BJ, Vogels TP. A familiar thought: Machines that replace us? <i>Neuron</i>. 2022;110(3):361-362. doi:<a href=\"https://doi.org/10.1016/j.neuron.2022.01.014\">10.1016/j.neuron.2022.01.014</a>","ieee":"B. J. Confavreux and T. P. Vogels, “A familiar thought: Machines that replace us?,” <i>Neuron</i>, vol. 110, no. 3. Elsevier, pp. 361–362, 2022.","ista":"Confavreux BJ, Vogels TP. 2022. A familiar thought: Machines that replace us? Neuron. 110(3), 361–362.","chicago":"Confavreux, Basile J, and Tim P Vogels. “A Familiar Thought: Machines That Replace Us?” <i>Neuron</i>. Elsevier, 2022. <a href=\"https://doi.org/10.1016/j.neuron.2022.01.014\">https://doi.org/10.1016/j.neuron.2022.01.014</a>.","mla":"Confavreux, Basile J., and Tim P. Vogels. “A Familiar Thought: Machines That Replace Us?” <i>Neuron</i>, vol. 110, no. 3, Elsevier, 2022, pp. 361–62, doi:<a href=\"https://doi.org/10.1016/j.neuron.2022.01.014\">10.1016/j.neuron.2022.01.014</a>."},"title":"A familiar thought: Machines that replace us?","publication":"Neuron","quality_controlled":"1","department":[{"_id":"TiVo"}],"status":"public","intvolume":"       110","publisher":"Elsevier","isi":1,"month":"02","date_created":"2022-02-13T23:01:34Z","page":"361-362"},{"publication_status":"published","main_file_link":[{"open_access":"1","url":"https://proceedings.neurips.cc/paper/2020/hash/bdbd5ebfde4934142c8a88e7a3796cd5-Abstract.html"}],"oa":1,"volume":33,"article_processing_charge":"No","abstract":[{"lang":"eng","text":"The search for biologically faithful synaptic plasticity rules has resulted in a large body of models. They are usually inspired by – and fitted to – experimental data, but they rarely produce neural dynamics that serve complex functions. These failures suggest that current plasticity models are still under-constrained by existing data. Here, we present an alternative approach that uses meta-learning to discover plausible synaptic plasticity rules. Instead of experimental data, the rules are constrained by the functions they implement and the structure they are meant to produce. Briefly, we parameterize synaptic plasticity rules by a Volterra expansion and then use supervised learning methods (gradient descent or evolutionary strategies) to minimize a problem-dependent loss function that quantifies how effectively a candidate plasticity rule transforms an initially random network into one with the desired function. We first validate our approach by re-discovering previously described plasticity rules, starting at the single-neuron level and “Oja’s rule”, a simple Hebbian plasticity rule that captures the direction of most variability of inputs to a neuron (i.e., the first principal component). We expand the problem to the network level and ask the framework to find Oja’s rule together with an anti-Hebbian rule such that an initially random two-layer firing-rate network will recover several principal components of the input space after learning. Next, we move to networks of integrate-and-fire neurons with plastic inhibitory afferents. We train for rules that achieve a target firing rate by countering tuned excitation. Our algorithm discovers a specific subset of the manifold of rules that can solve this task. Our work is a proof of principle of an automated and unbiased approach to unveil synaptic plasticity rules that obey biological constraints and can solve complex functions."}],"_id":"9633","date_published":"2020-12-06T00:00:00Z","date_updated":"2023-10-18T09:20:55Z","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","scopus_import":"1","publication_identifier":{"issn":["1049-5258"]},"year":"2020","oa_version":"Published Version","quality_controlled":"1","department":[{"_id":"TiVo"}],"publication":"Advances in Neural Information Processing Systems","intvolume":"        33","status":"public","page":"16398-16408","month":"12","date_created":"2021-07-04T22:01:27Z","acknowledgement":"We would like to thank Chaitanya Chintaluri, Georgia Christodoulou, Bill Podlaski and Merima Šabanovic for useful discussions and comments. This work was supported by a Wellcome Trust ´ Senior Research Fellowship (214316/Z/18/Z), a BBSRC grant (BB/N019512/1), an ERC consolidator Grant (SYNAPSEEK), a Leverhulme Trust Project Grant (RPG-2016-446), and funding from École Polytechnique, Paris.","related_material":{"record":[{"status":"public","id":"14422","relation":"dissertation_contains"}],"link":[{"url":"https://doi.org/10.1101/2020.10.24.353409","relation":"is_continued_by"}]},"project":[{"grant_number":"214316/Z/18/Z","name":"What’s in a memory? Spatiotemporal dynamics in strongly coupled recurrent neuronal networks.","_id":"c084a126-5a5b-11eb-8a69-d75314a70a87"},{"_id":"0aacfa84-070f-11eb-9043-d7eb2c709234","call_identifier":"H2020","name":"Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning.","grant_number":"819603"}],"language":[{"iso":"eng"}],"ec_funded":1,"citation":{"ama":"Confavreux BJ, Zenke F, Agnes EJ, Lillicrap T, Vogels TP. A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network. In: <i>Advances in Neural Information Processing Systems</i>. Vol 33. ; 2020:16398-16408.","apa":"Confavreux, B. J., Zenke, F., Agnes, E. J., Lillicrap, T., &#38; Vogels, T. P. (2020). A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network. In <i>Advances in Neural Information Processing Systems</i> (Vol. 33, pp. 16398–16408). Vancouver, Canada.","short":"B.J. Confavreux, F. Zenke, E.J. Agnes, T. Lillicrap, T.P. Vogels, in:, Advances in Neural Information Processing Systems, 2020, pp. 16398–16408.","mla":"Confavreux, Basile J., et al. “A Meta-Learning Approach to (Re)Discover Plasticity Rules That Carve a Desired Function into a Neural Network.” <i>Advances in Neural Information Processing Systems</i>, vol. 33, 2020, pp. 16398–408.","chicago":"Confavreux, Basile J, Friedemann Zenke, Everton J. Agnes, Timothy Lillicrap, and Tim P Vogels. “A Meta-Learning Approach to (Re)Discover Plasticity Rules That Carve a Desired Function into a Neural Network.” In <i>Advances in Neural Information Processing Systems</i>, 33:16398–408, 2020.","ista":"Confavreux BJ, Zenke F, Agnes EJ, Lillicrap T, Vogels TP. 2020. A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network. Advances in Neural Information Processing Systems. NeurIPS: Conference on Neural Information Processing Systems vol. 33, 16398–16408.","ieee":"B. J. Confavreux, F. Zenke, E. J. Agnes, T. Lillicrap, and T. P. Vogels, “A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network,” in <i>Advances in Neural Information Processing Systems</i>, Vancouver, Canada, 2020, vol. 33, pp. 16398–16408."},"conference":{"location":"Vancouver, Canada","end_date":"2020-12-12","start_date":"2020-12-06","name":"NeurIPS: Conference on Neural Information Processing Systems"},"title":"A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network","day":"06","author":[{"id":"C7610134-B532-11EA-BD9F-F5753DDC885E","last_name":"Confavreux","full_name":"Confavreux, Basile J","first_name":"Basile J"},{"last_name":"Zenke","first_name":"Friedemann","full_name":"Zenke, Friedemann"},{"last_name":"Agnes","first_name":"Everton J.","full_name":"Agnes, Everton J."},{"last_name":"Lillicrap","full_name":"Lillicrap, Timothy","first_name":"Timothy"},{"orcid":"0000-0003-3295-6181","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","full_name":"Vogels, Tim P","first_name":"Tim P","last_name":"Vogels"}],"type":"conference"}]