[{"status":"public","month":"12","article_processing_charge":"No","title":"Modelling the spinal cord of a tadpole: Exploring different ways to model the spinal cord in the Xenopus frog","publisher":"Institute of Science and Technology Austria","publication_status":"published","date_updated":"2026-07-29T12:55:12Z","oa_version":"Published Version","publication_identifier":{"issn":["2791-4585"]},"department":[{"_id":"GradSch"},{"_id":"TiVo"},{"_id":"LoSw"}],"date_published":"2025-12-09T00:00:00Z","_id":"20735","file_date_updated":"2026-01-04T12:58:49Z","author":[{"orcid":"0000-0001-6191-1367","first_name":"Alexia C","full_name":"Wilson, Alexia C","id":"5230e794-15b2-11ec-abd3-e2d5335ebd1d","last_name":"Wilson"}],"supervisor":[{"id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","last_name":"Vogels","orcid":"0000-0003-3295-6181","full_name":"Vogels, Tim P","first_name":"Tim P"},{"id":"56BE8254-C4F0-11E9-8E45-0B23E6697425","last_name":"Sweeney","orcid":"0000-0001-9242-5601","full_name":"Sweeney, Lora Beatrice Jaeger","first_name":"Lora Beatrice Jaeger"}],"language":[{"iso":"eng"}],"file":[{"creator":"awilson","access_level":"closed","relation":"source_file","checksum":"9e3b6b73f8cbec2c3687d17fe8e30410","file_size":566072368,"date_created":"2026-01-01T17:26:30Z","file_id":"20919","content_type":"application/zip","file_name":"tadpoleAdEx.zip","date_updated":"2026-01-02T13:05:07Z"},{"file_name":"Masters_Thesis_Alexia_Wilson_FINAL_pdfA.pdf","success":1,"date_updated":"2026-01-04T12:58:49Z","content_type":"application/pdf","file_id":"20923","checksum":"13f4c0d33923e9d5c9d56731345cf21d","file_size":7170097,"date_created":"2026-01-04T12:58:49Z","creator":"awilson","relation":"main_file","access_level":"open_access"}],"date_created":"2025-12-08T09:49:41Z","doi_confirm":"1","ddc":["570","596","005"],"abstract":[{"text":"Left–right alternation is a defining feature of spinal locomotor circuits, yet the level of neuronal\r\ndetail required to generate and maintain this pattern remains unclear. This thesis investigates how\r\nmodels spanning multiple levels of abstraction—from biophysically detailed Hodgkin–Huxley (HH)\r\nneurons to adaptive integrate–and–fire (I&F) formulations and synfire-chain modules—can account\r\nfor the generation of fictive swimming in the spinal cord of the Xenopus laevis tadpole. The guiding\r\nhypothesis is that a small set of neuronal mechanisms is sufficient to reproduce the essential features\r\nof rhythmic alternation, and that moving between modeling scales helps distinguish core principles\r\nfrom biological detail.\r\nA minimal bilateral HH network comprising only four canonical neuron classes—excitatory\r\ndescending interneurons (dINs), inhibitory commissural interneurons (cINs), ipsilateral inhibitory\r\ninterneurons (aINs) and motoneurons—served as a biophysical proof of concept. Tuned to reproduce\r\nexperimentally observed firing modes, the model demonstrated that rebound-prone dIN excitability,\r\ncontralateral inhibition and modest electrical coupling are sufficient to generate stable alternating\r\nactivity, even in very small networks. These results motivated the transition to simpler models\r\ncapable of efficient analysis and scaling.\r\nAdaptive exponential I&F (AdEx) neurons were calibrated to physiological recordings using\r\nsimulation-based inference, yielding tonic and phasic/rebound templates that preserved the key\r\ndynamical signatures of the HH model. Phase-plane analysis clarified the mechanisms underlying\r\nsingle-spike responses and rebound firing in dINs. At network level, the I&F models robustly\r\nreproduced left–right alternation, while highlighting constraints on synaptic kinetics and adaptation\r\nneeded to avoid multi-spike responses.\r\nFinally, a synfire-chain framework provided a complementary, timing-centric perspective, demonstrating how precise spike synchrony, synaptic delays and minimal inhibitory coupling can generate\r\nalternating left–right sequences in a feedforward setting. Together, these approaches converge on a\r\ncommon conclusion: rebound-prone ipsilateral excitation combined with precisely timed contralateral inhibition constitutes a sufficient substrate for alternating spinal rhythms.\r\nBy integrating bottom-up and top-down modeling strategies, this thesis provides a unified, extensible framework for studying spinal pattern generation. The results show that essential locomotor\r\ndynamics can be captured across multiple abstraction levels, offering both mechanistic insight and\r\npractical tools for future data-driven investigations of spinal circuit development, robustness and\r\nmodulation.","lang":"eng"}],"doi":"10.15479/AT-ISTA-20735","oa":1,"day":"09","page":"110","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","has_accepted_license":"1","degree_awarded":"MS","OA_place":"publisher","related_material":{"record":[{"status":"public","relation":"part_of_dissertation","id":"13097"}]},"citation":{"ama":"Wilson AC. Modelling the spinal cord of a tadpole: Exploring different ways to model the spinal cord in the Xenopus frog. 2025. doi:<a href=\"https://doi.org/10.15479/AT-ISTA-20735\">10.15479/AT-ISTA-20735</a>","short":"A.C. Wilson, Modelling the Spinal Cord of a Tadpole: Exploring Different Ways to Model the Spinal Cord in the Xenopus Frog, Institute of Science and Technology Austria, 2025.","ista":"Wilson AC. 2025. Modelling the spinal cord of a tadpole: Exploring different ways to model the spinal cord in the Xenopus frog. Institute of Science and Technology Austria.","ieee":"A. C. Wilson, “Modelling the spinal cord of a tadpole: Exploring different ways to model the spinal cord in the Xenopus frog,” Institute of Science and Technology Austria, 2025.","mla":"Wilson, Alexia C. <i>Modelling the Spinal Cord of a Tadpole: Exploring Different Ways to Model the Spinal Cord in the Xenopus Frog</i>. Institute of Science and Technology Austria, 2025, doi:<a href=\"https://doi.org/10.15479/AT-ISTA-20735\">10.15479/AT-ISTA-20735</a>.","chicago":"Wilson, Alexia C. “Modelling the Spinal Cord of a Tadpole: Exploring Different Ways to Model the Spinal Cord in the Xenopus Frog.” Institute of Science and Technology Austria, 2025. <a href=\"https://doi.org/10.15479/AT-ISTA-20735\">https://doi.org/10.15479/AT-ISTA-20735</a>.","apa":"Wilson, A. C. (2025). <i>Modelling the spinal cord of a tadpole: Exploring different ways to model the spinal cord in the Xenopus frog</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT-ISTA-20735\">https://doi.org/10.15479/AT-ISTA-20735</a>"},"alternative_title":["ISTA Master's Thesis"],"year":"2025","corr_author":"1","type":"dissertation"},{"language":[{"iso":"eng"}],"tmp":{"name":"Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)","image":"/images/cc_by_nc_sa.png","legal_code_url":"https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode","short":"CC BY-NC-SA (4.0)"},"supervisor":[{"last_name":"Vogels","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","first_name":"Tim P","full_name":"Vogels, Tim P","orcid":"0000-0003-3295-6181"}],"author":[{"full_name":"Confavreux, Basile J","first_name":"Basile J","id":"C7610134-B532-11EA-BD9F-F5753DDC885E","last_name":"Confavreux"}],"file_date_updated":"2024-10-13T22:30:04Z","_id":"14422","license":"https://creativecommons.org/licenses/by-nc-sa/4.0/","publication_identifier":{"issn":["2663-337X"]},"ec_funded":1,"department":[{"_id":"GradSch"},{"_id":"TiVo"}],"date_published":"2023-10-12T00:00:00Z","oa_version":"Published Version","date_updated":"2026-07-07T06:53:33Z","publication_status":"published","title":"Synapseek: Meta-learning synaptic plasticity rules","publisher":"Institute of Science and Technology Austria","article_processing_charge":"No","month":"10","status":"public","type":"dissertation","year":"2023","alternative_title":["ISTA Thesis"],"corr_author":"1","citation":{"short":"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>","ista":"Confavreux BJ. 2023. Synapseek: Meta-learning synaptic plasticity rules. Institute of Science and Technology Austria.","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>.","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>.","ieee":"B. J. Confavreux, “Synapseek: Meta-learning synaptic plasticity rules,” Institute of Science and Technology Austria, 2023."},"related_material":{"record":[{"relation":"part_of_dissertation","status":"public","id":"9633"}]},"OA_place":"publisher","has_accepted_license":"1","degree_awarded":"PhD","user_id":"ba8df636-2132-11f1-aed0-ed93e2281fdd","page":"148","day":"12","oa":1,"doi":"10.15479/at:ista:14422","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"}],"project":[{"call_identifier":"H2020","_id":"0aacfa84-070f-11eb-9043-d7eb2c709234","grant_number":"819603","name":"Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning."}],"ddc":["610"],"date_created":"2023-10-12T14:13:25Z","file":[{"file_id":"14424","content_type":"application/pdf","embargo":"2024-10-12","date_updated":"2024-10-13T22:30:04Z","file_name":"Confavreux_Thesis_2A.pdf","creator":"cchlebak","access_level":"open_access","relation":"main_file","date_created":"2023-10-12T14:53:50Z","checksum":"7f636555eae7803323df287672fd13ed","file_size":30599717},{"creator":"cchlebak","embargo_to":"open_access","access_level":"closed","relation":"source_file","date_created":"2023-10-18T07:38:34Z","checksum":"725e85946db92290a4583a0de9779e1b","file_size":68406739,"content_type":"application/x-zip-compressed","file_id":"14440","date_updated":"2024-10-13T22:30:04Z","file_name":"Confavreux Thesis.zip"}]}]