[{"volume":17,"DOAJ_listed":"1","intvolume":" 17","publication":"Nature Communications","publication_identifier":{"eissn":["2041-1723"]},"publication_status":"published","file":[{"relation":"main_file","content_type":"application/pdf","creator":"dernst","date_created":"2026-05-21T07:01:35Z","file_name":"2026_NatureComm_Koolschijn.pdf","date_updated":"2026-05-21T07:01:35Z","success":1,"checksum":"1b529e06b1c5d6e085d60743317fd4f9","file_size":2059139,"file_id":"21905","access_level":"open_access"}],"fulldoi":"https://doi.org/10.1038/s41467-026-70659-x","date_created":"2026-05-20T14:30:37Z","date_updated":"2026-05-21T07:05:01Z","PlanS_conform":"1","type":"journal_article","year":"2026","article_type":"original","oa_version":"Published Version","tmp":{"image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"ddc":["570"],"pmid":1,"quality_controlled":"1","has_accepted_license":"1","date_published":"2026-05-01T00:00:00Z","OA_place":"publisher","doi":"10.1038/s41467-026-70659-x","title":"Noradrenaline causes a spread of association in the hippocampal cognitive map","article_processing_charge":"Yes","month":"05","oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_number":"3961","scopus_import":"1","_id":"21895","department":[{"_id":"TiVo"}],"citation":{"chicago":"Koolschijn, Renée S., Prakriti Parthasarathy, Michael Browning, Xenia Przygodda, Liliana P. Capitão, William T. Clarke, Tim P Vogels, Jill X. O’Reilly, and Helen C. Barron. “Noradrenaline Causes a Spread of Association in the Hippocampal Cognitive Map.” Nature Communications. Springer Nature, 2026. https://doi.org/10.1038/s41467-026-70659-x.","ista":"Koolschijn RS, Parthasarathy P, Browning M, Przygodda X, Capitão LP, Clarke WT, Vogels TP, O’Reilly JX, Barron HC. 2026. Noradrenaline causes a spread of association in the hippocampal cognitive map. Nature Communications. 17, 3961.","apa":"Koolschijn, R. S., Parthasarathy, P., Browning, M., Przygodda, X., Capitão, L. P., Clarke, W. T., … Barron, H. C. (2026). Noradrenaline causes a spread of association in the hippocampal cognitive map. Nature Communications. Springer Nature. https://doi.org/10.1038/s41467-026-70659-x","short":"R.S. Koolschijn, P. Parthasarathy, M. Browning, X. Przygodda, L.P. Capitão, W.T. Clarke, T.P. Vogels, J.X. O’Reilly, H.C. Barron, Nature Communications 17 (2026).","mla":"Koolschijn, Renée S., et al. “Noradrenaline Causes a Spread of Association in the Hippocampal Cognitive Map.” Nature Communications, vol. 17, 3961, Springer Nature, 2026, doi:10.1038/s41467-026-70659-x.","ieee":"R. S. Koolschijn et al., “Noradrenaline causes a spread of association in the hippocampal cognitive map,” Nature Communications, vol. 17. Springer Nature, 2026.","ama":"Koolschijn RS, Parthasarathy P, Browning M, et al. Noradrenaline causes a spread of association in the hippocampal cognitive map. Nature Communications. 2026;17. doi:10.1038/s41467-026-70659-x"},"acknowledgement":"We would like to thank Chamith Halahakoon, Phil Cowen, Angharad De Cates, Beata Godlewska, Riccardo De Giorgi, Katherine Smith and Edoardo Ostinelli for enabling this study by providing medical cover. We would like to thank Douglas F. Tomé and Everton J. Agnes for their guidance and advice with earlier versions of the neural network model. We would like to thank Rob Froemke for helpful discussion when preparing the experiments. We thank Leonie Glitz and Valentina Mancini for comments on an earlier version of the manuscript. R.S.K. was supported by an EPSRC/MRC-funded studentship (EP/L016052/1). P.P. was supported by the Cambridge Trust, Trinity Henry Barlow Scholarship and Trinity Hall Brockhouse Scholarship. L.C. is supported by the Foundation for Science and Technology (FCT) (Portuguese State Budget: UID/PSI/01662/2020; Research fellowship: 2021.00415.CEECIND). W.T.C. is funded by the Wellcome Trust [225924/Z/22/Z]. H.C.B. is supported by a UKRI Future Leaders Fellowship (MR/W008939/1) and the Wellcome Institutional Strategic Support Fund. H.C.B. and J.X.O. are supported by the Medical Research Council (MR/W01971X/1). The study was supported by the NIHR Oxford Health Biomedical Research Centre (NIHR203316). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust (203139/Z/16/Z and 203139/A/16/Z). This research was funded in part by the Wellcome Trust. For the purpose of open access, the author(s) have applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission.","publisher":"Springer Nature","language":[{"iso":"eng"}],"author":[{"first_name":"Renée S.","last_name":"Koolschijn","full_name":"Koolschijn, Renée S."},{"last_name":"Parthasarathy","full_name":"Parthasarathy, Prakriti","first_name":"Prakriti"},{"last_name":"Browning","full_name":"Browning, Michael","first_name":"Michael"},{"first_name":"Xenia","full_name":"Przygodda, Xenia","last_name":"Przygodda"},{"last_name":"Capitão","full_name":"Capitão, Liliana P.","first_name":"Liliana P."},{"full_name":"Clarke, William T.","last_name":"Clarke","first_name":"William T."},{"first_name":"Tim P","orcid":"0000-0003-3295-6181","last_name":"Vogels","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","full_name":"Vogels, Tim P"},{"last_name":"O’Reilly","full_name":"O’Reilly, Jill X.","first_name":"Jill X."},{"first_name":"Helen C.","full_name":"Barron, Helen C.","last_name":"Barron"}],"file_date_updated":"2026-05-21T07:01:35Z","day":"01","status":"public","abstract":[{"lang":"eng","text":"The mammalian brain organises knowledge about entities in the world and relationships between them using cognitive maps. When forming a cognitive map, there is a necessary trade-off between extending the map to make novel inferences, and storing a veridical copy of past experience. However, the neural mechanisms that control this trade-off remain unknown. Using a cross-scale approach that combines a pharmacological intervention in humans with neural network modelling, we show that the neuromodulator noradrenaline elicits a significant ‘spread of association’ across hippocampal cognitive maps. This neural spread of association can be explained by changes in synaptic plasticity that predict overgeneralisation in behaviour. Thus, elevated noradrenaline during learning increases the ‘smoothing kernel’ for plasticity across the cognitive map, allowing disparate memories to become linked and distorted."}],"external_id":{"pmid":["41832186"]},"OA_type":"gold"},{"date_updated":"2026-07-13T12:30:14Z","PlanS_conform":"1","publication":"Nature Metabolism","publication_status":"published","publication_identifier":{"eissn":["2522-5812"]},"file":[{"access_level":"open_access","file_size":5326608,"file_id":"21392","checksum":"365932a599d05bc9ce8a57204e7a1465","date_updated":"2026-03-02T15:21:27Z","success":1,"file_name":"2026_NatureMetab_AmrapaliVishwanath.pdf","date_created":"2026-03-02T15:21:27Z","creator":"dernst","content_type":"application/pdf","relation":"main_file"}],"fulldoi":"https://doi.org/10.1038/s42255-026-01451-w","date_created":"2026-03-02T10:04:49Z","type":"journal_article","year":"2026","volume":8,"intvolume":" 8","article_processing_charge":"Yes (in subscription journal)","title":"Mitochondrial Ca2+ efflux controls neuronal metabolism and long-term memory across species","oa":1,"month":"02","page":"467-488","has_accepted_license":"1","oa_version":"Published Version","article_type":"original","ddc":["570"],"tmp":{"image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"pmid":1,"quality_controlled":"1","doi":"10.1038/s42255-026-01451-w","date_published":"2026-02-11T00:00:00Z","OA_place":"publisher","das_tickbox":"1","citation":{"ama":"Amrapali Vishwanath A, Comyn T, Mira RG, et al. Mitochondrial Ca2+ efflux controls neuronal metabolism and long-term memory across species. Nature Metabolism. 2026;8(2):467-488. doi:10.1038/s42255-026-01451-w","mla":"Amrapali Vishwanath, Anjali, et al. “Mitochondrial Ca2+ Efflux Controls Neuronal Metabolism and Long-Term Memory across Species.” Nature Metabolism, vol. 8, no. 2, Springer Nature, 2026, pp. 467–88, doi:10.1038/s42255-026-01451-w.","ieee":"A. Amrapali Vishwanath et al., “Mitochondrial Ca2+ efflux controls neuronal metabolism and long-term memory across species,” Nature Metabolism, vol. 8, no. 2. Springer Nature, pp. 467–488, 2026.","short":"A. Amrapali Vishwanath, T. Comyn, R.G. Mira, C. Brossier, C. Pascual-Caro, M. Faour, K. Boumendil, C. Chintaluri, C. Ramon-Duaso, R. Fan, K. Ghosh, H. Farrants, J.-P. Berwick, R. Sivakumar, M. Lopez-Manzaneda, E.R. Schreiter, T. Preat, T.P. Vogels, V. Rangaraju, A. Busquets-Garcia, P.-Y. Plaçais, A. Pavlowsky, J. de Juan-Sanz, Nature Metabolism 8 (2026) 467–488.","apa":"Amrapali Vishwanath, A., Comyn, T., Mira, R. G., Brossier, C., Pascual-Caro, C., Faour, M., … de Juan-Sanz, J. (2026). Mitochondrial Ca2+ efflux controls neuronal metabolism and long-term memory across species. Nature Metabolism. Springer Nature. https://doi.org/10.1038/s42255-026-01451-w","ista":"Amrapali Vishwanath A, Comyn T, Mira RG, Brossier C, Pascual-Caro C, Faour M, Boumendil K, Chintaluri C, Ramon-Duaso C, Fan R, Ghosh K, Farrants H, Berwick J-P, Sivakumar R, Lopez-Manzaneda M, Schreiter ER, Preat T, Vogels TP, Rangaraju V, Busquets-Garcia A, Plaçais P-Y, Pavlowsky A, de Juan-Sanz J. 2026. Mitochondrial Ca2+ efflux controls neuronal metabolism and long-term memory across species. Nature Metabolism. 8(2), 467–488.","chicago":"Amrapali Vishwanath, Anjali, Typhaine Comyn, Rodrigo G. Mira, Claire Brossier, Carlos Pascual-Caro, Maya Faour, Kahina Boumendil, et al. “Mitochondrial Ca2+ Efflux Controls Neuronal Metabolism and Long-Term Memory across Species.” Nature Metabolism. Springer Nature, 2026. https://doi.org/10.1038/s42255-026-01451-w."},"acknowledgement":"We thank all members of the laboratory of J.d.J.-S. for insightful discussions and comments. We thank S. Perez for technical assistance. This work was made possible by the Paris Brain Institute Diane Barriere Chair in Synaptic Bioenergetics awarded to J.d.J.-S., who is also supported by an ERC Starting Grant (SynaptoEnergy, European Research Council; ERC-StG-852873), 2019 ATIP-Avenir Grant (CNRS, Inserm), a Big Brain Theory Grant (ICM Foundation) and a Kavli Exploratory Award (Kavli Foundation). This work was also supported by an ERC Advanced Grant (EnergyMeMo; ERC-AdG-741550) to T.P. and grants from the Agence Nationale de la Recherche to P.Y.P. (ANR-20-CE92-0047-01), T.P. (ANR-23-CE16-0029-01), A.P. and J.d.J.-S. (ANR-22-CE16-0020) and J.d.J.-S. (ANR-24-CE16-0221). T.P., P.Y.P. and J.d.J.-S. are permanent CNRS researchers. A.P. is a permanent ESPCI associate professor. T.C. was funded by the French Ministry of Research and the Fondation pour la Recherche Médicale. V.R. was funded by the Max Planck Society, the Chan Zuckerberg Initiative DAF, an advised fund of the Silicon Valley Community Foundation grant number 2024-349543 and the NIH Director’s New Innovator Award (DP2 MH140148). A.B.-G. and C.R.-D. received funding from an ERC Starting Grant (HighMemory; ERC-StG-948217), the Ministry of Economy and Competitiveness (PID2021-122795OB-I00) and the Departament d’Economia i Coneixement de la Generalitat de Catalunya (SGR 00022). T.P.V. was funded by the Wellcome Trust and a Royal Society Sir Henry Dale Research Fellowship (WT100000) and a Wellcome Trust Senior Research Fellowship (214316/Z/18/Z). K.G. was supported by the DIM C-BRAINS, funded by the Conseil Régional d’Ile-de-France. The contributions of H.F. and E.R.S. were supported by the Howard Hughes Medical Institute. The PHENO-ICMice animal Core at ICM is supported by two ‘Investissements d’avenir’ (ANR-10- IAIHU-06 and ANR-11-INBS-0011-NeurATRIS) and the Fondation pour la Recherche Médicale.","department":[{"_id":"TiVo"}],"_id":"21378","scopus_import":"1","publisher":"Springer Nature","issue":"2","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","external_id":{"pmid":["41673453"]},"abstract":[{"text":"From insects to mammals, essential brain functions, such as forming long-term memories (LTMs), increase metabolic activity in stimulated neurons to meet the energetic demand associated with brain activation. However, while impairing neuronal metabolism limits brain performance, whether expanding the metabolic capacity of neurons boosts brain function remains poorly understood. Here, we show that LTM formation of flies and mice can be enhanced by increasing mitochondrial metabolism in central memory circuits. By knocking down the mitochondrial Ca2+ exporter Letm1, we favour Ca2+ retention in the mitochondrial matrix of neurons due to reduction of mitochondrial H+/Ca2+ exchange. The resulting increase in mitochondrial Ca2+ over-activates mitochondrial metabolism in neurons of central memory circuits, leading to improved LTM storage in training paradigms in which wild-type counterparts of both species fail to remember. Our findings unveil an evolutionarily conserved mechanism that controls mitochondrial metabolism in neurons and indicate its involvement in shaping higher brain functions, such as LTM.","lang":"eng"}],"OA_type":"hybrid","file_date_updated":"2026-03-02T15:21:27Z","day":"11","language":[{"iso":"eng"}],"author":[{"last_name":"Amrapali Vishwanath","full_name":"Amrapali Vishwanath, Anjali","first_name":"Anjali"},{"first_name":"Typhaine","full_name":"Comyn, Typhaine","last_name":"Comyn"},{"full_name":"Mira, Rodrigo G.","last_name":"Mira","first_name":"Rodrigo G."},{"last_name":"Brossier","full_name":"Brossier, Claire","first_name":"Claire"},{"last_name":"Pascual-Caro","full_name":"Pascual-Caro, Carlos","first_name":"Carlos"},{"last_name":"Faour","full_name":"Faour, Maya","first_name":"Maya"},{"first_name":"Kahina","last_name":"Boumendil","full_name":"Boumendil, Kahina"},{"full_name":"Chintaluri, Chaitanya","id":"BA06AFEE-A4BA-11EA-AE5C-14673DDC885E","last_name":"Chintaluri","first_name":"Chaitanya","orcid":"0000-0003-4252-1608"},{"full_name":"Ramon-Duaso, Carla","last_name":"Ramon-Duaso","first_name":"Carla"},{"first_name":"Ruolin","last_name":"Fan","full_name":"Fan, Ruolin"},{"full_name":"Ghosh, Kishalay","last_name":"Ghosh","first_name":"Kishalay"},{"last_name":"Farrants","full_name":"Farrants, Helen","first_name":"Helen"},{"first_name":"Jean-Paul","full_name":"Berwick, Jean-Paul","last_name":"Berwick"},{"last_name":"Sivakumar","full_name":"Sivakumar, Riya","first_name":"Riya"},{"last_name":"Lopez-Manzaneda","full_name":"Lopez-Manzaneda, Mario","first_name":"Mario"},{"last_name":"Schreiter","full_name":"Schreiter, Eric R.","first_name":"Eric R."},{"first_name":"Thomas","full_name":"Preat, Thomas","last_name":"Preat"},{"orcid":"0000-0003-3295-6181","first_name":"Tim P","last_name":"Vogels","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","full_name":"Vogels, Tim P"},{"last_name":"Rangaraju","full_name":"Rangaraju, Vidhya","first_name":"Vidhya"},{"first_name":"Arnau","last_name":"Busquets-Garcia","full_name":"Busquets-Garcia, Arnau"},{"full_name":"Plaçais, Pierre-Yves","last_name":"Plaçais","first_name":"Pierre-Yves"},{"last_name":"Pavlowsky","full_name":"Pavlowsky, Alice","first_name":"Alice"},{"first_name":"Jaime","last_name":"de Juan-Sanz","full_name":"de Juan-Sanz, Jaime"}],"status":"public","project":[{"name":"What’s in a memory? Spatiotemporal dynamics in strongly coupled recurrent neuronal networks.","_id":"c084a126-5a5b-11eb-8a69-d75314a70a87","grant_number":"214316/Z/18/Z"}]},{"article_type":"original","oa_version":"Published Version","ddc":["570","577"],"tmp":{"image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"quality_controlled":"1","pmid":1,"has_accepted_license":"1","page":"791-798.e6","date_published":"2026-02-02T00:00:00Z","OA_place":"publisher","doi":"10.1016/j.cub.2025.12.025","title":"Interplay between syllable duration and pitch during whistle matching in wild nightingales","article_processing_charge":"Yes (in subscription journal)","dataavailabilitystatement":"All data have been deposited at https://github.com/vallentinlab/NG-whistle-durations and are publicly available as of the date of publication.\r\nAll original code has been deposited at https://github.com/vallentinlab/NG-whistle-durations and is publicly available as of the date of publication.\r\nAny additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.","month":"02","oa":1,"volume":36,"intvolume":" 36","publication_status":"published","publication":"Current Biology","publication_identifier":{"eissn":["1879-0445"],"issn":["0960-9822"]},"fulldoi":"https://doi.org/10.1016/j.cub.2025.12.025","file":[{"creator":"dernst","content_type":"application/pdf","relation":"main_file","access_level":"open_access","checksum":"e17c3537193d5ab4886596d1a04f9b0e","file_id":"22416","file_size":7120959,"file_name":"2026_CurrentBiology_CalderonGarcia.pdf","success":1,"date_updated":"2026-07-27T10:47:55Z","date_created":"2026-07-27T10:47:55Z"}],"date_created":"2026-01-14T12:00:29Z","date_updated":"2026-07-27T10:48:35Z","PlanS_conform":"1","type":"journal_article","year":"2026","language":[{"iso":"eng"}],"author":[{"full_name":"Calderon Garcia, Juan Sebastian","id":"1271b54b-dbcd-11ea-9d1d-d92da838fe2c","last_name":"Calderon Garcia","first_name":"Juan Sebastian"},{"first_name":"Giacomo","last_name":"Costalunga","full_name":"Costalunga, Giacomo"},{"last_name":"Vogels","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","full_name":"Vogels, Tim P","first_name":"Tim P","orcid":"0000-0003-3295-6181"},{"first_name":"Daniela","last_name":"Vallentin","full_name":"Vallentin, Daniela"}],"file_date_updated":"2026-07-27T10:47:55Z","day":"02","status":"public","project":[{"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.","call_identifier":"H2020"}],"abstract":[{"lang":"eng","text":"During complex vocal interactions, different features of acoustic stimuli are integrated to produce appropriate vocal responses,1 such as copying sounds during vocal matching behavior in some animals.2,3,4,5,6,7,8,9,10,11,12 However, little is known about the interplay and possible trade-offs between the different temporal and spectral acoustic features during these vocal exchanges.2,13,14 Nightingales can flexibly match the pitch of their tonal “whistle songs” in real time during counter-singing duels.15,16 Here, we show that the syllable duration of whistle playbacks could alter the song responses of wild nightingales, causing their whistle duration distribution to shift toward the presented stimulus duration. When exposed to whistle playbacks featuring unnatural combinations of pitch and duration, nightingales demonstrate a flexible trade-off between pitch matching and temporal imitation, yet they are constrained by their vocal repertoire. They selectively adapted their vocal responses to approximate these novel stimuli, aligning them with their natural whistle repertoire. We developed a computational model of nightingale whistle-matching behavior that revealed a hierarchical organization of acoustic feature production. During whistle matching, the feature integration process is constrained by the duration of syllables, and pitch matching follows within this temporal framework, forcing a trade-off between the two features. Our findings reveal a complex interplay between the spectral and temporal domains that shapes song-matching behavior."}],"external_id":{"pmid":["41529680"]},"OA_type":"hybrid","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","issue":"3","ec_funded":1,"supplementarymaterial":"yes","_id":"20986","scopus_import":"1","department":[{"_id":"GradSch"},{"_id":"TiVo"}],"das_tickbox":"1","citation":{"short":"J.S. Calderon Garcia, G. Costalunga, T.P. Vogels, D. Vallentin, Current Biology 36 (2026) 791–798.e6.","apa":"Calderon Garcia, J. S., Costalunga, G., Vogels, T. P., & Vallentin, D. (2026). Interplay between syllable duration and pitch during whistle matching in wild nightingales. Current Biology. Elsevier. https://doi.org/10.1016/j.cub.2025.12.025","ama":"Calderon Garcia JS, Costalunga G, Vogels TP, Vallentin D. Interplay between syllable duration and pitch during whistle matching in wild nightingales. Current Biology. 2026;36(3):791-798.e6. doi:10.1016/j.cub.2025.12.025","ieee":"J. S. Calderon Garcia, G. Costalunga, T. P. Vogels, and D. Vallentin, “Interplay between syllable duration and pitch during whistle matching in wild nightingales,” Current Biology, vol. 36, no. 3. Elsevier, p. 791–798.e6, 2026.","mla":"Calderon Garcia, Juan Sebastian, et al. “Interplay between Syllable Duration and Pitch during Whistle Matching in Wild Nightingales.” Current Biology, vol. 36, no. 3, Elsevier, 2026, p. 791–798.e6, doi:10.1016/j.cub.2025.12.025.","chicago":"Calderon Garcia, Juan Sebastian, Giacomo Costalunga, Tim P Vogels, and Daniela Vallentin. “Interplay between Syllable Duration and Pitch during Whistle Matching in Wild Nightingales.” Current Biology. Elsevier, 2026. https://doi.org/10.1016/j.cub.2025.12.025.","ista":"Calderon Garcia JS, Costalunga G, Vogels TP, Vallentin D. 2026. Interplay between syllable duration and pitch during whistle matching in wild nightingales. Current Biology. 36(3), 791–798.e6."},"acknowledgement":"We would like to thank J. Benichov and N. Hein for their help with fieldwork; M. Ramadas for helping with the segmentation analysis; T. Eliav, C. Chintaluri, G. Tkacik, and A. Navas for providing helpful comments to the project and manuscript; and A. Costalunga for the drawings of nightingales. Funding sources: The Joachim Herz Stiftung Add-on Fellowships for Interdisciplinary Life Science, awarded to G.C.; the ERC Consolidator Grant 819603 SYNAPSEEK, awarded to T.P.V.; and DFG Research Unit 5768–532521431, DFG Research Grant-547921981, DFG SFB 1315–327654276, and the ERC Starting Grant 757459 MIDNIGHT, awarded to D.V.","publisher":"Elsevier","researchdata_availability":"yes"},{"ddc":["570"],"tmp":{"image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"quality_controlled":"1","pmid":1,"main_file_link":[{"url":"https://doi.org/10.1038/s41586-026-10679-1","open_access":"1"}],"oa_version":"Published Version","article_type":"original","has_accepted_license":"1","corr_author":"1","OA_place":"publisher","date_published":"2026-06-17T00:00:00Z","doi":"10.1038/s41586-026-10679-1","title":"Cortical development dynamics across autism spectrum disorder mouse models","dataavailabilitystatement":"Single-nucleus multiomics data are available from the Gene Expression Omnibus (GSE328363). The mm10 reference genome was used for the alignment (refdata-cellranger-arc-mm10-2020-A-2.0.0, obtained from https://cf.10xgenomics.com/supp/cell-arc/refdata-cellranger-arc-mm10-2020-A-2.0.0.tar.gz). Single-cell data can be accessed and visualized through a CELLxGENE database (https://adameykolab.hifo.meduniwien.ac.at/cellxgene_public/filecrawl/.2026_Nature_Schwarz). Source data are provided with this paper. Scripts and analyses that support the main findings of this study are accessible in a GitHub repository (https://git.ista.ac.at/research-sofware/mouseome).","article_processing_charge":"Yes (via OA deal)","month":"06","oa":1,"related_material":{"link":[{"relation":"press_release","description":"News on ISTA website","url":"https://ista.ac.at/en/news/patterns-in-genetic-chaos/"}]},"acknowledged_ssus":[{"_id":"Bio"},{"_id":"LifeSc"}],"date_created":"2026-07-13T09:47:21Z","fulldoi":"https://doi.org/10.1038/s41586-026-10679-1","publication_status":"epub_ahead","publication":"Nature","publication_identifier":{"issn":["0028-0836"],"eissn":["1476-4687"]},"PlanS_conform":"1","date_updated":"2026-08-04T09:29:55Z","year":"2026","type":"journal_article","author":[{"first_name":"Lena A","id":"29A8453C-F248-11E8-B48F-1D18A9856A87","full_name":"Schwarz, Lena A","last_name":"Schwarz"},{"first_name":"Christoph","orcid":"0000-0002-9033-9096","last_name":"Dotter","full_name":"Dotter, Christoph","id":"4C66542E-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Isaev, Sergey","last_name":"Isaev","first_name":"Sergey"},{"first_name":"Michela","last_name":"Lisi","id":"39383c1b-d3eb-11ef-8d6c-c8cdf4e10c8c","full_name":"Lisi, Michela"},{"full_name":"Malzl, Daniel","last_name":"Malzl","first_name":"Daniel"},{"first_name":"Christoph","full_name":"Büschl, Christoph","id":"2a8c054c-0913-11ee-9159-f8ef515809ed","last_name":"Büschl"},{"full_name":"Ladstätter, Sabrina","last_name":"Ladstätter","first_name":"Sabrina"},{"first_name":"Bárbara","id":"3B03AA1A-F248-11E8-B48F-1D18A9856A87","full_name":"Oliveira, Bárbara","last_name":"Oliveira"},{"first_name":"Matteo","full_name":"Barel, Matteo","id":"8959927b-2236-11ed-bd6e-ea83d94ade0e","last_name":"Barel"},{"orcid":"0000-0003-1843-3173","first_name":"Bernadette","last_name":"Basilico","full_name":"Basilico, Bernadette","id":"36035796-5ACA-11E9-A75E-7AF2E5697425"},{"id":"BA06AFEE-A4BA-11EA-AE5C-14673DDC885E","full_name":"Chintaluri, Chaitanya","last_name":"Chintaluri","first_name":"Chaitanya","orcid":"0000-0003-4252-1608"},{"full_name":"Gorkiewicz, Sarah","id":"f141a35d-15a9-11ec-9fb2-fef6becc7b6f","last_name":"Gorkiewicz","first_name":"Sarah"},{"first_name":"Mohammad","id":"3384113A-F248-11E8-B48F-1D18A9856A87","full_name":"Goudarzi, Mohammad","last_name":"Goudarzi"},{"id":"0bf89b6a-d28b-11eb-8bd6-f43768e4d368","full_name":"Belinova, Tereza","last_name":"Belinova","first_name":"Tereza"},{"first_name":"Stephan","last_name":"Reichl","full_name":"Reichl, Stephan"},{"last_name":"Sendžikaitė","id":"dd6d52f2-c50d-11eb-9548-bcf0ff82b344","full_name":"Sendžikaitė, Gintarė","first_name":"Gintarė"},{"last_name":"Arcot Jayaram","full_name":"Arcot Jayaram, Satish","id":"b0bbee33-09f7-11eb-909c-8b358058d28a","first_name":"Satish","orcid":"0000-0002-2479-2669"},{"first_name":"Peter","orcid":"0000-0002-3509-1948","full_name":"Koppensteiner, Peter","id":"3B8B25A8-F248-11E8-B48F-1D18A9856A87","last_name":"Koppensteiner"},{"last_name":"Sommer","id":"4DF26D8C-F248-11E8-B48F-1D18A9856A87","full_name":"Sommer, Christoph M","orcid":"0000-0003-1216-9105","first_name":"Christoph M"},{"last_name":"Vogels","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","full_name":"Vogels, Tim P","first_name":"Tim P","orcid":"0000-0003-3295-6181"},{"last_name":"Menche","full_name":"Menche, Jörg","first_name":"Jörg"},{"first_name":"Igor","full_name":"Adameyko, Igor","last_name":"Adameyko"},{"last_name":"Kharchenko","id":"0095641e-7eb7-11f1-8665-aec51a2ab5e0","full_name":"Kharchenko, Peter Vasili","first_name":"Peter Vasili"},{"full_name":"Bock, Christoph","last_name":"Bock","first_name":"Christoph"},{"last_name":"Novarino","full_name":"Novarino, Gaia","id":"3E57A680-F248-11E8-B48F-1D18A9856A87","first_name":"Gaia","orcid":"0000-0002-7673-7178"}],"language":[{"iso":"eng"}],"day":"17","project":[{"_id":"34ba8964-11ca-11ed-8bc3-e15864e7e9a6","name":"Toward an understanding of the brain interstitial system and the extracellular proteome in health and autism spectrum disorders","grant_number":"101044865"},{"name":"Critical windows and reversibility of ASD associated with mutations in chromatin remodelers","_id":"9B91375C-BA93-11EA-9121-9846C619BF3A","grant_number":"707964"},{"call_identifier":"FWF","_id":"2548AE96-B435-11E9-9278-68D0E5697425","name":"Molecular Drug Targets","grant_number":"W1232"},{"grant_number":"FG1803 49015","_id":"ebb38b5d-77a9-11ec-83b8-a42e08120a88","name":"Neurobiology of anxiety in autism spectrum disorders"}],"status":"public","OA_type":"hybrid","external_id":{"pmid":["42310454"]},"abstract":[{"lang":"eng","text":"Despite the functional diversity of over 100 causal genes1,2,3, phenotypic convergence across models may reveal common neurobiological processes in autism spectrum disorder (ASD). Here we profiled 251 samples from 11 monogenic mouse models of ASD using single-nucleus multi-omic sequencing across three developmental stages, both sexes and two brain regions. Despite genetic heterogeneity, ASD-linked mutations converged on perturbations of the radial glial cell lineage. These alterations reflect a transient developmental delay rather than lasting lineage misspecification and resolve by postnatal stages. Molecularly, the largest transcriptional differences emerged in neurons at early postnatal stages. These changes included downregulation of synaptic and ion channel-related genes, consistent with homeostatic adaptation or delayed maturation. Network analysis showed molecular convergence across models within each developmental stage, suggesting that diverse mutations linked to ASD impinge on common, stage-specific processes. Convergence becomes less pronounced by postnatal day 14, highlighting the dynamic nature of ASD-associated changes. Cross-genotype heterogeneity is superimposed on stage-specific effects. Electrophysiology corroborated this pattern: mutants generally showed altered neuronal excitability and synaptic properties with model-specific nuances. Our study also highlighted sex-specific gene expression alterations, with female mice often displaying larger effect sizes than male mice. Together, our findings provide a comprehensive view of developmental cellular and molecular dynamics across models of ASD."}],"user_id":"ba8df636-2132-11f1-aed0-ed93e2281fdd","scopus_import":"1","_id":"22295","supplementarymaterial":"yes","department":[{"_id":"AnKi"},{"_id":"GaNo"},{"_id":"TiVo"},{"_id":"ScienComp"},{"_id":"GradSch"},{"_id":"Bio"},{"_id":"PreCl"}],"acknowledgement":"We thank F. Freeman, V. Voronin and M. Ladron de Guevara for technical assistance; A. Stichelberger and S. Liegenfeld for the management of our animal colony; M. Schunn, C. Gold and the Preclinical Facility team for technical assistance; C. Jansen and the Scientific Computing Facility for bioinformatics support and technical assistance; the Biomedical Sequencing Facility at CeMM for assistance with next-generation sequencing; and J. Lin and T. Krausgruber in the laboratory of C. Bock for support with flow cytometry; J. Kirchner for illustrating the multi-omics approach depicted in Fig. 1; and all members of the laboratory of G.N. for their support and discussions. This study was supported by the Scientific Service Units of ISTA through resources provided by the Imaging & Optics Facility and the Laboratory Support Facility. Bulk RNA-seq was performed by the Next Generation Sequencing Facility at Vienna BioCenter Core Facilities, member of the Vienna BioCenter. This work was supported by a European Research Council Consolidator Grant (PR1028ERC02), by SFARI (PR1028SIM02) and by the Austrian Science Fund (PE1028W1232 and PR1028FG1803) to G.N. Open access funding provided by Institute of Science and Technology (IST Austria).","citation":{"ista":"Schwarz LA, Dotter C, Isaev S, Lisi M, Malzl D, Büschl C, Ladstätter S, Oliveira B, Barel M, Basilico B, Chintaluri C, Gorkiewicz S, Goudarzi M, Belinova T, Reichl S, Sendžikaitė G, Arcot Jayaram S, Koppensteiner P, Sommer CM, Vogels TP, Menche J, Adameyko I, Kharchenko PV, Bock C, Novarino G. 2026. Cortical development dynamics across autism spectrum disorder mouse models. Nature.","chicago":"Schwarz, Lena A, Christoph Dotter, Sergey Isaev, Michela Lisi, Daniel Malzl, Christoph Büschl, Sabrina Ladstätter, et al. “Cortical Development Dynamics across Autism Spectrum Disorder Mouse Models.” Nature. Springer Nature, 2026. https://doi.org/10.1038/s41586-026-10679-1.","mla":"Schwarz, Lena A., et al. “Cortical Development Dynamics across Autism Spectrum Disorder Mouse Models.” Nature, Springer Nature, 2026, doi:10.1038/s41586-026-10679-1.","ieee":"L. A. Schwarz et al., “Cortical development dynamics across autism spectrum disorder mouse models,” Nature. Springer Nature, 2026.","ama":"Schwarz LA, Dotter C, Isaev S, et al. Cortical development dynamics across autism spectrum disorder mouse models. Nature. 2026. doi:10.1038/s41586-026-10679-1","short":"L.A. Schwarz, C. Dotter, S. Isaev, M. Lisi, D. Malzl, C. Büschl, S. Ladstätter, B. Oliveira, M. Barel, B. Basilico, C. Chintaluri, S. Gorkiewicz, M. Goudarzi, T. Belinova, S. Reichl, G. Sendžikaitė, S. Arcot Jayaram, P. Koppensteiner, C.M. Sommer, T.P. Vogels, J. Menche, I. Adameyko, P.V. Kharchenko, C. Bock, G. Novarino, Nature (2026).","apa":"Schwarz, L. A., Dotter, C., Isaev, S., Lisi, M., Malzl, D., Büschl, C., … Novarino, G. (2026). Cortical development dynamics across autism spectrum disorder mouse models. Nature. Springer Nature. https://doi.org/10.1038/s41586-026-10679-1"},"researchdata_availability":"yes","publisher":"Springer Nature"},{"_id":"19640","scopus_import":"1","department":[{"_id":"TiVo"}],"acknowledgement":"We would like to thank Chaitanya Chintaluri, Nicoleta Condruz and Douglas Feitosa Tomé for insightful discussions. This project has received funding from the HORIZON EUROPE European Research Council (ERC) consolidator grant\r\n(SYNAPSEEK, awarded to TV), a Wellcome Trust Sir Henry Dale Research Fellowship (WT100000, awarded to TV), a Wellcome Trust Senior Research Fellowship (214316/Z/18/Z, awarded to TV), and a Sir Henry Wellcome\r\nFellowship (110124/Z/15/Z, awarded to FZ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.","citation":{"chicago":"Confavreux, Basile J, Everton J. Agnes, Friedemann Zenke, Henning Sprekeler, and Tim P Vogels. “Balancing Complexity, Performance and Plausibility to Meta Learn Plasticity Rules in Recurrent Spiking Networks.” PLoS Computational Biology. Public Library of Science, 2025. https://doi.org/10.1371/journal.pcbi.1012910.","ista":"Confavreux BJ, Agnes EJ, Zenke F, Sprekeler H, Vogels TP. 2025. Balancing complexity, performance and plausibility to meta learn plasticity rules in recurrent spiking networks. PLoS Computational Biology. 21(4), e1012910.","short":"B.J. Confavreux, E.J. Agnes, F. Zenke, H. Sprekeler, T.P. Vogels, PLoS Computational Biology 21 (2025).","apa":"Confavreux, B. J., Agnes, E. J., Zenke, F., Sprekeler, H., & Vogels, T. P. (2025). Balancing complexity, performance and plausibility to meta learn plasticity rules in recurrent spiking networks. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1012910","ama":"Confavreux BJ, Agnes EJ, Zenke F, Sprekeler H, Vogels TP. Balancing complexity, performance and plausibility to meta learn plasticity rules in recurrent spiking networks. PLoS Computational Biology. 2025;21(4). doi:10.1371/journal.pcbi.1012910","ieee":"B. J. Confavreux, E. J. Agnes, F. Zenke, H. Sprekeler, and T. P. Vogels, “Balancing complexity, performance and plausibility to meta learn plasticity rules in recurrent spiking networks,” PLoS Computational Biology, vol. 21, no. 4. Public Library of Science, 2025.","mla":"Confavreux, Basile J., et al. “Balancing Complexity, Performance and Plausibility to Meta Learn Plasticity Rules in Recurrent Spiking Networks.” PLoS Computational Biology, vol. 21, no. 4, e1012910, Public Library of Science, 2025, doi:10.1371/journal.pcbi.1012910."},"publisher":"Public Library of Science","article_number":"e1012910","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","issue":"4","ec_funded":1,"OA_type":"gold","abstract":[{"text":"Synaptic plasticity is a key player in the brain’s life-long learning abilities. However, due to experimental limitations, the mechanistic link between synaptic plasticity rules and the network-level computations they enable remain opaque. Here we use evolutionary strategies (ES) to meta learn local co-active plasticity rules in large recurrent spiking networks with excitatory (E) and inhibitory (I) neurons, using parameterizations of increasing complexity. We discover rules that robustly stabilize network dynamics for all four synapse types acting in isolation (E-to-E, E-to-I, I-to-E and I-to-I). More complex functions such as familiarity detection can also be included in the search constraints. However, our meta learning strategy begins to fail for co-active rules of increasing complexity, as it is challenging to devise loss functions that effectively constrain network dynamics to plausible solutions a priori. Moreover, in line with previous work, we can find multiple degenerate solutions with identical network behaviour. As a local optimization strategy, ES provides one solution at a time and makes exploration of this degeneracy cumbersome. Regardless, we can glean the interdependecies of various plasticity parameters by considering the covariance matrix learned alongside the optimal rule with ES. Our work provides a proof of principle for the success of machine-learning-guided discovery of plasticity rules in large spiking networks, and points at the necessity of more elaborate search strategies going forward.","lang":"eng"}],"external_id":{"pmid":["40273284 "],"isi":["001474257000002"]},"author":[{"id":"C7610134-B532-11EA-BD9F-F5753DDC885E","full_name":"Confavreux, Basile J","last_name":"Confavreux","first_name":"Basile J"},{"first_name":"Everton J.","last_name":"Agnes","full_name":"Agnes, Everton J."},{"first_name":"Friedemann","last_name":"Zenke","full_name":"Zenke, Friedemann"},{"full_name":"Sprekeler, Henning","last_name":"Sprekeler","first_name":"Henning"},{"first_name":"Tim P","orcid":"0000-0003-3295-6181","full_name":"Vogels, Tim P","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","last_name":"Vogels"}],"language":[{"iso":"eng"}],"day":"24","file_date_updated":"2025-05-05T11:17:49Z","status":"public","project":[{"grant_number":"819603","call_identifier":"H2020","name":"Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning.","_id":"0aacfa84-070f-11eb-9043-d7eb2c709234"},{"_id":"c084a126-5a5b-11eb-8a69-d75314a70a87","name":"What’s in a memory? Spatiotemporal dynamics in strongly coupled recurrent neuronal networks.","grant_number":"214316/Z/18/Z"}],"fulldoi":"https://doi.org/10.1371/journal.pcbi.1012910","file":[{"relation":"main_file","content_type":"application/pdf","creator":"dernst","date_created":"2025-05-05T11:17:49Z","file_name":"2025_PLoSCompBio_Confavreux.pdf","date_updated":"2025-05-05T11:17:49Z","success":1,"checksum":"6437a1aab52813ab7e310e3b4fb36e3b","file_size":9771636,"file_id":"19654","access_level":"open_access"}],"date_created":"2025-05-04T22:02:31Z","publication_status":"published","publication":"PLoS Computational Biology","publication_identifier":{"issn":["1553-734X"],"eissn":["1553-7358"]},"PlanS_conform":"1","date_updated":"2026-05-06T13:17:52Z","year":"2025","type":"journal_article","volume":21,"isi":1,"related_material":{"link":[{"url":"https://github.com/VogelsLab/SpikES","relation":"software"}]},"DOAJ_listed":"1","intvolume":" 21","title":"Balancing complexity, performance and plausibility to meta learn plasticity rules in recurrent spiking networks","article_processing_charge":"Yes","month":"04","oa":1,"pmid":1,"tmp":{"image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"quality_controlled":"1","ddc":["570"],"oa_version":"Published Version","article_type":"original","has_accepted_license":"1","corr_author":"1","OA_place":"publisher","date_published":"2025-04-24T00:00:00Z","APC_amount":"3237,62 EUR","doi":"10.1371/journal.pcbi.1012910"},{"title":"High capacity and dynamic accessibility in associative memory networks with context-dependent neuronal and synaptic gating","article_processing_charge":"Yes","month":"03","oa":1,"tmp":{"image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"ddc":["530"],"quality_controlled":"1","oa_version":"Published Version","article_type":"original","has_accepted_license":"1","corr_author":"1","OA_place":"publisher","APC_amount":"4910,08 EUR","date_published":"2025-03-13T00:00:00Z","doi":"10.1103/PhysRevX.15.011057","fulldoi":"https://doi.org/10.1103/PhysRevX.15.011057","file":[{"creator":"dernst","content_type":"application/pdf","relation":"main_file","file_id":"19432","file_size":1373704,"checksum":"1f27ee469ab51a3e1ce1e2df0022e81d","access_level":"open_access","date_created":"2025-03-20T12:47:17Z","date_updated":"2025-03-20T12:47:17Z","success":1,"file_name":"2025_PhysReviewX_Podlaski.pdf"}],"date_created":"2020-07-16T12:24:28Z","publication":"Physical Review X","publication_identifier":{"eissn":["2160-3308"]},"publication_status":"published","date_updated":"2026-05-06T12:44:27Z","year":"2025","type":"journal_article","isi":1,"volume":15,"related_material":{"link":[{"url":"https://github.com/wpodlaski/contextual-memory-nets","relation":"software"}]},"intvolume":" 15","OA_type":"gold","abstract":[{"text":"Biological memory is known to be flexible—memory formation and recall depend on factors such as the behavioral context of the organism. However, this property is often ignored in associative memory models, leaving it unclear how memories can be organized and recalled when subject to contextual control. Because of the lack of a rigorous analytical framework, it is also unknown how contextual control affects memory stability, storage capacity, and information content. Here, we bring the dynamic nature of memory to the fore by introducing a novel model of associative memory, which we refer to as the context-modular memory network. In our model, stored memory patterns are associated to one of several background network states, or contexts. Memories are accessible when their corresponding context is active, and are otherwise inaccessible. Context modulates the effective network connectivity by imposing a specific\r\nconfiguration of neuronal and synaptic gating—gated neurons (synapses) have their activity (weights) momentarily silenced, thereby reducing interference from memories belonging to other contexts. Memory patterns are randomly and independently chosen, while neuronal and synaptic gates may be selected randomly or optimized through a process of contextual synaptic refinement. Through analytic and numerical results, we show that context-modular memory networks can exhibit both improved memory capacity and differential control of memory stability with random gating (especially for neuronal gating). For contextual synaptic refinement, we devise a method in which synapses are gated off for a given context if they destabilize the memory patterns in that context, drastically improving memory capacity and enabling even more precise control over memory stability. Notably, synaptic refinement allows for patterns to be\r\naccessible in multiple contexts, stabilizing memory patterns even for weight matrices that alone do not contain any information about the memory patterns, such as Gaussian random matrices. Overall, our model integrates recent ideas about context-dependent memory organization with classic associative memory models and proposes a rigorous theory which can act as a framework for future work. Furthermore, our work carries important implications for the understanding of biological memory storage and recall in the brain, such as highlighting an intriguing trade-off between memory capacity and accessibility.","lang":"eng"}],"external_id":{"isi":["001451378900002"]},"locked":"1","author":[{"full_name":"Podlaski, William F.","last_name":"Podlaski","first_name":"William F.","orcid":"0000-0001-6619-7502"},{"first_name":"Everton J.","orcid":"0000-0001-7184-7311","last_name":"Agnes","full_name":"Agnes, Everton J."},{"last_name":"Vogels","full_name":"Vogels, Tim P","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","orcid":"0000-0003-3295-6181","first_name":"Tim P"}],"language":[{"iso":"eng"}],"day":"13","file_date_updated":"2025-03-20T12:47:17Z","status":"public","project":[{"_id":"B67AFEDC-15C9-11EA-A837-991A96BB2854","name":"IST Austria Open Access Fund"},{"_id":"c084a126-5a5b-11eb-8a69-d75314a70a87","name":"What’s in a memory? Spatiotemporal dynamics in strongly coupled recurrent neuronal networks.","grant_number":"214316/Z/18/Z"}],"department":[{"_id":"TiVo"}],"_id":"8125","scopus_import":"1","acknowledgement":"We thank Helen Barron, Vezha Boboeva, Adam Packer, João Sacramento, Andrew Saxe, Misha Tsodyks, and Friedemann Zenke for helpful comments at various stages of this work, and Rubem Erichsen, Jr. for carefully reading the manuscript and valuable comments. This work was\r\nsupported by a Sir Henry Dale Fellowship by the Wellcome Trust and the Royal Society [No. WT100000 (W. F. P., E. J. A., and T. P. V.)], a Wellcome Trust Senior Research Fellowship [No. 214316/Z/18/Z (E. J. A. and T. P. V.)], and a Research Project Grant by the Leverhulme Trust\r\n[No. RPG-2016-446 (E. J. A.)]. ","citation":{"ista":"Podlaski WF, Agnes EJ, Vogels TP. 2025. High capacity and dynamic accessibility in associative memory networks with context-dependent neuronal and synaptic gating. Physical Review X. 15, 011057.","chicago":"Podlaski, William F., Everton J. Agnes, and Tim P Vogels. “High Capacity and Dynamic Accessibility in Associative Memory Networks with Context-Dependent Neuronal and Synaptic Gating.” Physical Review X. American Physical Society, 2025. https://doi.org/10.1103/PhysRevX.15.011057.","ama":"Podlaski WF, Agnes EJ, Vogels TP. High capacity and dynamic accessibility in associative memory networks with context-dependent neuronal and synaptic gating. Physical Review X. 2025;15. doi:10.1103/PhysRevX.15.011057","mla":"Podlaski, William F., et al. “High Capacity and Dynamic Accessibility in Associative Memory Networks with Context-Dependent Neuronal and Synaptic Gating.” Physical Review X, vol. 15, 011057, American Physical Society, 2025, doi:10.1103/PhysRevX.15.011057.","ieee":"W. F. Podlaski, E. J. Agnes, and T. P. Vogels, “High capacity and dynamic accessibility in associative memory networks with context-dependent neuronal and synaptic gating,” Physical Review X, vol. 15. American Physical Society, 2025.","apa":"Podlaski, W. F., Agnes, E. J., & Vogels, T. P. (2025). High capacity and dynamic accessibility in associative memory networks with context-dependent neuronal and synaptic gating. Physical Review X. American Physical Society. https://doi.org/10.1103/PhysRevX.15.011057","short":"W.F. Podlaski, E.J. Agnes, T.P. Vogels, Physical Review X 15 (2025)."},"publisher":"American Physical Society","article_number":"011057","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87"},{"year":"2024","type":"journal_article","fulldoi":"https://doi.org/10.1073/pnas.2307776121","file":[{"creator":"dernst","content_type":"application/pdf","relation":"main_file","checksum":"f498c643be81895dd3a69ee90115a782","file_size":3060109,"file_id":"19613","access_level":"open_access","date_created":"2025-04-23T13:51:16Z","file_name":"2024_PNAS_Clatot.pdf","date_updated":"2025-04-23T13:51:16Z","success":1}],"date_created":"2024-01-21T23:00:56Z","publication_identifier":{"eissn":["1091-6490"]},"publication":"Proceedings of the National Academy of Sciences of the United States of America","publication_status":"published","date_updated":"2025-09-04T11:47:47Z","intvolume":" 121","isi":1,"volume":121,"related_material":{"link":[{"relation":"software","url":"https://github.com/ChrisCurrin/pv-kcnc2 "}]},"month":"01","oa":1,"title":"A structurally precise mechanism links an epilepsy-associated KCNC2 potassium channel mutation to interneuron dysfunction","article_processing_charge":"Yes (in subscription journal)","OA_place":"publisher","date_published":"2024-01-16T00:00:00Z","doi":"10.1073/pnas.2307776121","tmp":{"short":"CC BY-NC-ND (4.0)","name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","image":"/images/cc_by_nc_nd.png"},"pmid":1,"ddc":["570"],"quality_controlled":"1","article_type":"original","oa_version":"Published Version","has_accepted_license":"1","publisher":"National Academy of Sciences","_id":"14841","scopus_import":"1","department":[{"_id":"TiVo"}],"acknowledgement":"This work was supported by an ERC Consolidator Grant (SYNAPSEEK) to T.P.V., the NOMIS Foundation through the NOMIS Fellowships program at IST Austria to C.B.C., a Jefferson Synaptic Biology Center Pilot Project Grant to M.C., NIH NINDS U54 NS108874 (PI, Alfred L. George), and NIH NINDS R01 NS122887 to E.M.G. The computations were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) at the PDC Center for High-Performance Computing, KTH Royal Institute of Technology, partially funded by the Swedish Research Council through grant agreement no. 2018-05973. We thank Akshay Sridhar for the fruitful discussion of the project.","citation":{"chicago":"Clatot, Jerome, Christopher Currin, Qiansheng Liang, Tanadet Pipatpolkai, Shavonne L. Massey, Ingo Helbig, Lucie Delemotte, Tim P Vogels, Manuel Covarrubias, and Ethan M. Goldberg. “A Structurally Precise Mechanism Links an Epilepsy-Associated KCNC2 Potassium Channel Mutation to Interneuron Dysfunction.” Proceedings of the National Academy of Sciences of the United States of America. National Academy of Sciences, 2024. https://doi.org/10.1073/pnas.2307776121.","ista":"Clatot J, Currin C, Liang Q, Pipatpolkai T, Massey SL, Helbig I, Delemotte L, Vogels TP, Covarrubias M, Goldberg EM. 2024. A structurally precise mechanism links an epilepsy-associated KCNC2 potassium channel mutation to interneuron dysfunction. Proceedings of the National Academy of Sciences of the United States of America. 121(3), e2307776121.","short":"J. Clatot, C. Currin, Q. Liang, T. Pipatpolkai, S.L. Massey, I. Helbig, L. Delemotte, T.P. Vogels, M. Covarrubias, E.M. Goldberg, Proceedings of the National Academy of Sciences of the United States of America 121 (2024).","apa":"Clatot, J., Currin, C., Liang, Q., Pipatpolkai, T., Massey, S. L., Helbig, I., … Goldberg, E. M. (2024). A structurally precise mechanism links an epilepsy-associated KCNC2 potassium channel mutation to interneuron dysfunction. Proceedings of the National Academy of Sciences of the United States of America. National Academy of Sciences. https://doi.org/10.1073/pnas.2307776121","mla":"Clatot, Jerome, et al. “A Structurally Precise Mechanism Links an Epilepsy-Associated KCNC2 Potassium Channel Mutation to Interneuron Dysfunction.” Proceedings of the National Academy of Sciences of the United States of America, vol. 121, no. 3, e2307776121, National Academy of Sciences, 2024, doi:10.1073/pnas.2307776121.","ieee":"J. Clatot et al., “A structurally precise mechanism links an epilepsy-associated KCNC2 potassium channel mutation to interneuron dysfunction,” Proceedings of the National Academy of Sciences of the United States of America, vol. 121, no. 3. National Academy of Sciences, 2024.","ama":"Clatot J, Currin C, Liang Q, et al. A structurally precise mechanism links an epilepsy-associated KCNC2 potassium channel mutation to interneuron dysfunction. Proceedings of the National Academy of Sciences of the United States of America. 2024;121(3). doi:10.1073/pnas.2307776121"},"ec_funded":1,"article_number":"e2307776121","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","issue":"3","OA_type":"hybrid","external_id":{"isi":["001167401000001"],"pmid":["38194456"]},"abstract":[{"text":"De novo heterozygous variants in KCNC2 encoding the voltage-gated potassium (K+) channel subunit Kv3.2 are a recently described cause of developmental and epileptic encephalopathy (DEE). A de novo variant in KCNC2 c.374G > A (p.Cys125Tyr) was identified via exome sequencing in a patient with DEE. Relative to wild-type Kv3.2, Kv3.2-p.Cys125Tyr induces K+ currents exhibiting a large hyperpolarizing shift in the voltage dependence of activation, accelerated activation, and delayed deactivation consistent with a relative stabilization of the open conformation, along with increased current density. Leveraging the cryogenic electron microscopy (cryo-EM) structure of Kv3.1, molecular dynamic simulations suggest that a strong π-π stacking interaction between the variant Tyr125 and Tyr156 in the α-6 helix of the T1 domain promotes a relative stabilization of the open conformation of the channel, which underlies the observed gain of function. A multicompartment computational model of a Kv3-expressing parvalbumin-positive cerebral cortex fast-spiking γ-aminobutyric acidergic (GABAergic) interneuron (PV-IN) demonstrates how the Kv3.2-Cys125Tyr variant impairs neuronal excitability and dysregulates inhibition in cerebral cortex circuits to explain the resulting epilepsy.","lang":"eng"}],"project":[{"grant_number":"819603","name":"Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning.","_id":"0aacfa84-070f-11eb-9043-d7eb2c709234","call_identifier":"H2020"}],"status":"public","author":[{"last_name":"Clatot","full_name":"Clatot, Jerome","first_name":"Jerome"},{"first_name":"Christopher","orcid":"0000-0002-4809-5059","last_name":"Currin","id":"e8321fc5-3091-11eb-8a53-83f309a11ac9","full_name":"Currin, Christopher"},{"full_name":"Liang, Qiansheng","last_name":"Liang","first_name":"Qiansheng"},{"first_name":"Tanadet","last_name":"Pipatpolkai","full_name":"Pipatpolkai, Tanadet"},{"last_name":"Massey","full_name":"Massey, Shavonne L.","first_name":"Shavonne L."},{"first_name":"Ingo","last_name":"Helbig","full_name":"Helbig, Ingo"},{"last_name":"Delemotte","full_name":"Delemotte, Lucie","first_name":"Lucie"},{"last_name":"Vogels","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","full_name":"Vogels, Tim P","first_name":"Tim P","orcid":"0000-0003-3295-6181"},{"first_name":"Manuel","full_name":"Covarrubias, Manuel","last_name":"Covarrubias"},{"last_name":"Goldberg","full_name":"Goldberg, Ethan M.","first_name":"Ethan M."}],"language":[{"iso":"eng"}],"day":"16","file_date_updated":"2025-04-23T13:51:16Z"},{"date_updated":"2025-09-04T13:06:06Z","publication_identifier":{"eissn":["1546-1726"],"issn":["1097-6256"]},"publication":"Nature Neuroscience","publication_status":"published","fulldoi":"https://doi.org/10.1038/s41593-024-01597-4","date_created":"2024-03-24T23:01:00Z","file":[{"relation":"main_file","content_type":"application/pdf","creator":"dernst","date_created":"2025-06-25T08:45:32Z","file_name":"2025_NatureNeuroscience_Agnes.pdf","success":1,"date_updated":"2025-06-25T08:45:32Z","checksum":"dfca68a24749575b912b3a78a7de4516","file_id":"19902","file_size":10508018,"access_level":"open_access"}],"type":"journal_article","year":"2024","volume":27,"isi":1,"intvolume":" 27","article_processing_charge":"Yes (via OA deal)","title":"Co-dependent excitatory and inhibitory plasticity accounts for quick, stable and long-lasting memories in biological networks","oa":1,"month":"05","has_accepted_license":"1","page":"964-974","article_type":"original","oa_version":"Published Version","pmid":1,"tmp":{"image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"ddc":["570"],"quality_controlled":"1","doi":"10.1038/s41593-024-01597-4","date_published":"2024-05-01T00:00:00Z","OA_place":"publisher","citation":{"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;27:964-974. doi:10.1038/s41593-024-01597-4","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, vol. 27, Springer Nature, 2024, pp. 964–74, 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, vol. 27. Springer Nature, pp. 964–974, 2024.","short":"E.J. Agnes, T.P. Vogels, Nature Neuroscience 27 (2024) 964–974.","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. 27, 964–974.","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."},"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.","scopus_import":"1","_id":"15171","department":[{"_id":"TiVo"}],"publisher":"Springer Nature","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","ec_funded":1,"external_id":{"pmid":["38509348 "],"isi":["001190081400001"]},"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"}],"OA_type":"hybrid","file_date_updated":"2025-06-25T08:45:32Z","day":"01","language":[{"iso":"eng"}],"author":[{"first_name":"Everton J.","last_name":"Agnes","full_name":"Agnes, Everton J."},{"last_name":"Vogels","full_name":"Vogels, Tim P","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","first_name":"Tim P","orcid":"0000-0003-3295-6181"}],"status":"public","project":[{"grant_number":"819603","name":"Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning.","_id":"0aacfa84-070f-11eb-9043-d7eb2c709234","call_identifier":"H2020"}]},{"has_accepted_license":"1","corr_author":"1","tmp":{"image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"pmid":1,"quality_controlled":"1","ddc":["570"],"oa_version":"Published Version","article_type":"original","doi":"10.1073/pnas.2306525120","OA_place":"publisher","date_published":"2023-11-21T00:00:00Z","article_processing_charge":"Yes (in subscription journal)","title":"Metabolically regulated spiking could serve neuronal energy homeostasis and protect from reactive oxygen species","oa":1,"month":"11","related_material":{"link":[{"relation":"software","url":"https://github.com/ccluri/metabolic_spiking"}]},"isi":1,"volume":120,"intvolume":" 120","date_updated":"2026-07-13T12:30:49Z","file":[{"checksum":"bf4ec38602a70dae4338077a5a4d497f","file_id":"14678","file_size":16891602,"access_level":"open_access","date_created":"2023-12-11T12:45:12Z","file_name":"2023_PNAS_Chintaluri.pdf","date_updated":"2023-12-11T12:45:12Z","success":1,"creator":"dernst","content_type":"application/pdf","relation":"main_file"}],"fulldoi":"https://doi.org/10.1073/pnas.2306525120","date_created":"2023-12-10T23:01:00Z","publication_status":"published","publication":"Proceedings of the National Academy of Sciences of the United States of America","publication_identifier":{"eissn":["1091-6490"],"issn":["0027-8424"]},"year":"2023","type":"journal_article","day":"21","file_date_updated":"2023-12-11T12:45:12Z","author":[{"full_name":"Chintaluri, Chaitanya","id":"BA06AFEE-A4BA-11EA-AE5C-14673DDC885E","last_name":"Chintaluri","orcid":"0000-0003-4252-1608","first_name":"Chaitanya"},{"last_name":"Vogels","full_name":"Vogels, Tim P","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","first_name":"Tim P","orcid":"0000-0003-3295-6181"}],"language":[{"iso":"eng"}],"project":[{"_id":"c084a126-5a5b-11eb-8a69-d75314a70a87","name":"What’s in a memory? Spatiotemporal dynamics in strongly coupled recurrent neuronal networks.","grant_number":"214316/Z/18/Z"}],"status":"public","OA_type":"hybrid","abstract":[{"lang":"eng","text":"So-called spontaneous activity is a central hallmark of most nervous systems. Such non-causal firing is contrary to the tenet of spikes as a means of communication, and its purpose remains unclear. We propose that self-initiated firing can serve as a release valve to protect neurons from the toxic conditions arising in mitochondria from lower-than-baseline energy consumption. To demonstrate the viability of our hypothesis, we built a set of models that incorporate recent experimental results indicating homeostatic control of metabolic products—Adenosine triphosphate (ATP), adenosine diphosphate (ADP), and reactive oxygen species (ROS)—by changes in firing. We explore the relationship of metabolic cost of spiking with its effect on the temporal patterning of spikes and reproduce experimentally observed changes in intrinsic firing in the fruitfly dorsal fan-shaped body neuron in a model with ROS-modulated potassium channels. We also show that metabolic spiking homeostasis can produce indefinitely sustained avalanche dynamics in cortical circuits. Our theory can account for key features of neuronal activity observed in many studies ranging from ion channel function all the way to resting state dynamics. We finish with a set of experimental predictions that would confirm an integrated, crucial role for metabolically regulated spiking and firmly link metabolic homeostasis and neuronal function."}],"external_id":{"pmid":["37988463"],"isi":["001157389000005"]},"issue":"48","article_number":"e2306525120","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","acknowledgement":"We thank Prof. C. Nazaret and Prof. J.-P. Mazat for sharing the code of their mitochondrial model. We also thank G. Miesenböck, E. Marder, L. Abbott, A. Kempf, P. Hasenhuetl, W. Podlaski, F. Zenke, E. Agnes, P. Bozelos, J. Watson, B. Confavreux, and G. Christodoulou, and the rest of the Vogels Lab for their feedback. This work was funded by Wellcome Trust and Royal Society Sir Henry Dale Research Fellowship (WT100000), a Wellcome Trust Senior Research Fellowship (214316/Z/18/Z), and a UK Research and Innovation, Biotechnology and Biological Sciences Research Council grant (UKRI-BBSRC BB/N019512/1).","das_tickbox":"1","citation":{"apa":"Chintaluri, C., & Vogels, T. P. (2023). Metabolically regulated spiking could serve neuronal energy homeostasis and protect from reactive oxygen species. Proceedings of the National Academy of Sciences of the United States of America. National Academy of Sciences. https://doi.org/10.1073/pnas.2306525120","short":"C. Chintaluri, T.P. Vogels, Proceedings of the National Academy of Sciences of the United States of America 120 (2023).","ieee":"C. Chintaluri and T. P. Vogels, “Metabolically regulated spiking could serve neuronal energy homeostasis and protect from reactive oxygen species,” Proceedings of the National Academy of Sciences of the United States of America, vol. 120, no. 48. National Academy of Sciences, 2023.","mla":"Chintaluri, Chaitanya, and Tim P. Vogels. “Metabolically Regulated Spiking Could Serve Neuronal Energy Homeostasis and Protect from Reactive Oxygen Species.” Proceedings of the National Academy of Sciences of the United States of America, vol. 120, no. 48, e2306525120, National Academy of Sciences, 2023, doi:10.1073/pnas.2306525120.","ama":"Chintaluri C, Vogels TP. Metabolically regulated spiking could serve neuronal energy homeostasis and protect from reactive oxygen species. Proceedings of the National Academy of Sciences of the United States of America. 2023;120(48). doi:10.1073/pnas.2306525120","chicago":"Chintaluri, Chaitanya, and Tim P Vogels. “Metabolically Regulated Spiking Could Serve Neuronal Energy Homeostasis and Protect from Reactive Oxygen Species.” Proceedings of the National Academy of Sciences of the United States of America. National Academy of Sciences, 2023. https://doi.org/10.1073/pnas.2306525120.","ista":"Chintaluri C, Vogels TP. 2023. Metabolically regulated spiking could serve neuronal energy homeostasis and protect from reactive oxygen species. Proceedings of the National Academy of Sciences of the United States of America. 120(48), e2306525120."},"scopus_import":"1","_id":"14666","department":[{"_id":"TiVo"}],"publisher":"National Academy of Sciences"},{"acknowledgement":"We would like to thank Bernardo Rudy, Joanna Mattis, and Laura Mcgarry for comments on a previous version of the manuscript; Xiaohong Zhang for expert technical support and mouse colony maintenance; Melody Cheng for assistance with generation of the graphical abstract; and Jennifer Kearney for the gift of Scn1a+/− mice. This work was supported by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under F31NS111803 (to K.M.G.) and K08NS097633 and R01NS110869 (to E.M.G.), the Dravet Syndrome Foundation (to A.S.), an ERC Consolidator Grant (SYNAPSEEK) (to T.P.V.), and the NOMIS Foundation through the NOMIS Fellowships program at IST Austria (to C.C.). The graphical abstract was prepared using BioRender software (BioRender.com).","citation":{"ieee":"K. Kaneko et al., “Developmentally regulated impairment of parvalbumin interneuron synaptic transmission in an experimental model of Dravet syndrome,” Cell Reports, vol. 38, no. 13. Elsevier, 2022.","mla":"Kaneko, Keisuke, et al. “Developmentally Regulated Impairment of Parvalbumin Interneuron Synaptic Transmission in an Experimental Model of Dravet Syndrome.” Cell Reports, vol. 38, no. 13, 110580, Elsevier, 2022, doi:10.1016/j.celrep.2022.110580.","ama":"Kaneko K, Currin C, Goff KM, et al. Developmentally regulated impairment of parvalbumin interneuron synaptic transmission in an experimental model of Dravet syndrome. Cell Reports. 2022;38(13). doi:10.1016/j.celrep.2022.110580","short":"K. Kaneko, C. Currin, K.M. Goff, E.R. Wengert, A. Somarowthu, T.P. Vogels, E.M. Goldberg, Cell Reports 38 (2022).","apa":"Kaneko, K., Currin, C., Goff, K. M., Wengert, E. R., Somarowthu, A., Vogels, T. P., & Goldberg, E. M. (2022). Developmentally regulated impairment of parvalbumin interneuron synaptic transmission in an experimental model of Dravet syndrome. Cell Reports. Elsevier. https://doi.org/10.1016/j.celrep.2022.110580","ista":"Kaneko K, Currin C, Goff KM, Wengert ER, Somarowthu A, Vogels TP, Goldberg EM. 2022. Developmentally regulated impairment of parvalbumin interneuron synaptic transmission in an experimental model of Dravet syndrome. Cell Reports. 38(13), 110580.","chicago":"Kaneko, Keisuke, Christopher Currin, Kevin M. Goff, Eric R. Wengert, Ala Somarowthu, Tim P Vogels, and Ethan M. Goldberg. “Developmentally Regulated Impairment of Parvalbumin Interneuron Synaptic Transmission in an Experimental Model of Dravet Syndrome.” Cell Reports. Elsevier, 2022. https://doi.org/10.1016/j.celrep.2022.110580."},"_id":"11143","department":[{"_id":"TiVo"}],"scopus_import":"1","publisher":"Elsevier","issue":"13","article_number":"110580","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","ec_funded":1,"external_id":{"pmid":["35354025"],"isi":["000779794000001"]},"abstract":[{"text":"Dravet syndrome is a neurodevelopmental disorder characterized by epilepsy, intellectual disability, and sudden death due to pathogenic variants in SCN1A with loss of function of the sodium channel subunit Nav1.1. Nav1.1-expressing parvalbumin GABAergic interneurons (PV-INs) from young Scn1a+/− mice show impaired action potential generation. An approach assessing PV-IN function in the same mice at two time points shows impaired spike generation in all Scn1a+/− mice at postnatal days (P) 16–21, whether deceased prior or surviving to P35, with normalization by P35 in surviving mice. However, PV-IN synaptic transmission is dysfunctional in young Scn1a+/− mice that did not survive and in Scn1a+/− mice ≥ P35. Modeling confirms that PV-IN axonal propagation is more sensitive to decreased sodium conductance than spike generation. These results demonstrate dynamic dysfunction in Dravet syndrome: combined abnormalities of PV-IN spike generation and propagation drives early disease severity, while ongoing dysfunction of synaptic transmission contributes to chronic pathology.","lang":"eng"}],"day":"29","file_date_updated":"2022-04-15T11:00:58Z","author":[{"last_name":"Kaneko","full_name":"Kaneko, Keisuke","first_name":"Keisuke"},{"full_name":"Currin, Christopher","id":"e8321fc5-3091-11eb-8a53-83f309a11ac9","last_name":"Currin","orcid":"0000-0002-4809-5059","first_name":"Christopher"},{"first_name":"Kevin M.","last_name":"Goff","full_name":"Goff, Kevin M."},{"last_name":"Wengert","full_name":"Wengert, Eric R.","first_name":"Eric R."},{"last_name":"Somarowthu","full_name":"Somarowthu, Ala","first_name":"Ala"},{"orcid":"0000-0003-3295-6181","first_name":"Tim P","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","full_name":"Vogels, Tim P","last_name":"Vogels"},{"first_name":"Ethan M.","last_name":"Goldberg","full_name":"Goldberg, Ethan M."}],"language":[{"iso":"eng"}],"project":[{"grant_number":"819603","call_identifier":"H2020","name":"Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning.","_id":"0aacfa84-070f-11eb-9043-d7eb2c709234"},{"_id":"9B861AAC-BA93-11EA-9121-9846C619BF3A","name":"NOMIS Fellowship Program"}],"status":"public","date_updated":"2025-06-11T14:00:11Z","fulldoi":"https://doi.org/10.1016/j.celrep.2022.110580","date_created":"2022-04-10T22:01:39Z","file":[{"success":1,"date_updated":"2022-04-15T11:00:58Z","file_name":"2022_CellReports_Kaneko.pdf","date_created":"2022-04-15T11:00:58Z","access_level":"open_access","file_size":4774216,"file_id":"11172","checksum":"49105c6c27c9af0f37f50a8bbb4d380d","relation":"main_file","content_type":"application/pdf","creator":"dernst"}],"publication_identifier":{"eissn":["2211-1247"]},"publication_status":"published","publication":"Cell Reports","year":"2022","type":"journal_article","isi":1,"volume":38,"intvolume":" 38","article_processing_charge":"No","title":"Developmentally regulated impairment of parvalbumin interneuron synaptic transmission in an experimental model of Dravet syndrome","oa":1,"month":"03","has_accepted_license":"1","tmp":{"short":"CC BY-NC-ND (4.0)","name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","image":"/images/cc_by_nc_nd.png"},"ddc":["570"],"pmid":1,"quality_controlled":"1","oa_version":"Published Version","article_type":"original","doi":"10.1016/j.celrep.2022.110580","date_published":"2022-03-29T00:00:00Z"},{"project":[{"grant_number":"214316/Z/18/Z","_id":"c084a126-5a5b-11eb-8a69-d75314a70a87","name":"Whatâs in a memory? Spatiotemporal dynamics in strongly coupled recurrent neuronal networks."},{"_id":"0aacfa84-070f-11eb-9043-d7eb2c709234","name":"Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning.","call_identifier":"H2020","grant_number":"819603"}],"status":"public","day":"25","file_date_updated":"2022-09-05T08:55:11Z","author":[{"first_name":"David W.","last_name":"Jia","full_name":"Jia, David W."},{"orcid":"0000-0003-3295-6181","first_name":"Tim P","last_name":"Vogels","full_name":"Vogels, Tim P","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425"},{"first_name":"Rui Ponte","last_name":"Costa","full_name":"Costa, Rui Ponte"}],"language":[{"iso":"eng"}],"abstract":[{"text":"Changes in the short-term dynamics of excitatory synapses over development have been observed throughout cortex, but their purpose and consequences remain unclear. Here, we propose that developmental changes in synaptic dynamics buffer the effect of slow inhibitory long-term plasticity, allowing for continuously stable neural activity. Using computational modeling we demonstrate that early in development excitatory short-term depression quickly stabilises neural activity, even in the face of strong, unbalanced excitation. We introduce a model of the commonly observed developmental shift from depression to facilitation and show that neural activity remains stable throughout development, while inhibitory synaptic plasticity slowly balances excitation, consistent with experimental observations. Our model predicts changes in the input responses from phasic to phasic-and-tonic and more precise spike timings. We also observe a gradual emergence of short-lasting memory traces governed by short-term plasticity development. We conclude that the developmental depression-to-facilitation shift may control excitation-inhibition balance throughout development with important functional consequences.","lang":"eng"}],"external_id":{"isi":["000844814800007"]},"ec_funded":1,"article_number":"873","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","publisher":"Springer Nature","acknowledgement":"We would like to thank the Vogels Lab for feedback on an earlier version of this manuscript. D.W.J. was supported by a Marshall Scholarship and a Clarendon Scholarship. R.P.C. and T.P.V. were supported by a Wellcome Trust and Royal Society Sir Henry Dale Fellowship (WT 100000), a Wellcome Trust Senior Research Fellowship (214316/Z/18/Z), and an ERC Consolidator Grant (SYNAPSEEK).","citation":{"chicago":"Jia, David W., Tim P Vogels, and Rui Ponte Costa. “Developmental Depression-to-Facilitation Shift Controls Excitation-Inhibition Balance.” Communications Biology. Springer Nature, 2022. https://doi.org/10.1038/s42003-022-03801-2.","ista":"Jia DW, Vogels TP, Costa RP. 2022. Developmental depression-to-facilitation shift controls excitation-inhibition balance. Communications biology. 5, 873.","short":"D.W. Jia, T.P. Vogels, R.P. Costa, Communications Biology 5 (2022).","apa":"Jia, D. W., Vogels, T. P., & Costa, R. P. (2022). Developmental depression-to-facilitation shift controls excitation-inhibition balance. Communications Biology. Springer Nature. https://doi.org/10.1038/s42003-022-03801-2","ama":"Jia DW, Vogels TP, Costa RP. Developmental depression-to-facilitation shift controls excitation-inhibition balance. Communications biology. 2022;5. doi:10.1038/s42003-022-03801-2","mla":"Jia, David W., et al. “Developmental Depression-to-Facilitation Shift Controls Excitation-Inhibition Balance.” Communications Biology, vol. 5, 873, Springer Nature, 2022, doi:10.1038/s42003-022-03801-2.","ieee":"D. W. Jia, T. P. Vogels, and R. P. Costa, “Developmental depression-to-facilitation shift controls excitation-inhibition balance,” Communications biology, vol. 5. Springer Nature, 2022."},"department":[{"_id":"TiVo"}],"_id":"12009","scopus_import":"1","doi":"10.1038/s42003-022-03801-2","date_published":"2022-08-25T00:00:00Z","has_accepted_license":"1","ddc":["570"],"tmp":{"image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"quality_controlled":"1","article_type":"original","oa_version":"Published Version","oa":1,"month":"08","article_processing_charge":"No","title":"Developmental depression-to-facilitation shift controls excitation-inhibition balance","intvolume":" 5","volume":5,"isi":1,"year":"2022","type":"journal_article","date_updated":"2025-04-14T09:44:14Z","fulldoi":"https://doi.org/10.1038/s42003-022-03801-2","date_created":"2022-09-04T22:02:02Z","file":[{"relation":"main_file","creator":"dernst","content_type":"application/pdf","date_created":"2022-09-05T08:55:11Z","file_name":"2022_CommBiology_Jia.pdf","date_updated":"2022-09-05T08:55:11Z","success":1,"checksum":"3ec724c4f6d3440028c217305e32915f","file_size":2491191,"file_id":"12022","access_level":"open_access"}],"publication_identifier":{"eissn":["2399-3642"]},"publication":"Communications biology","publication_status":"published"},{"author":[{"full_name":"Christodoulou, Georgia","last_name":"Christodoulou","first_name":"Georgia"},{"last_name":"Vogels","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","full_name":"Vogels, Tim P","first_name":"Tim P","orcid":"0000-0003-3295-6181"},{"full_name":"Agnes, Everton J.","last_name":"Agnes","first_name":"Everton J."}],"language":[{"iso":"eng"}],"day":"15","file_date_updated":"2022-09-12T07:47:55Z","status":"public","project":[{"name":"Whatâs in a memory? Spatiotemporal dynamics in strongly coupled recurrent neuronal networks.","_id":"c084a126-5a5b-11eb-8a69-d75314a70a87","grant_number":"214316/Z/18/Z"}],"abstract":[{"text":"Neuronal networks encode information through patterns of activity that define the networks’ function. The neurons’ activity relies on specific connectivity structures, yet the link between structure and function is not fully understood. Here, we tackle this structure-function problem with a new conceptual approach. Instead of manipulating the connectivity directly, we focus on upper triangular matrices, which represent the network dynamics in a given orthonormal basis obtained by the Schur decomposition. This abstraction allows us to independently manipulate the eigenspectrum and feedforward structures of a connectivity matrix. Using this method, we describe a diverse repertoire of non-normal transient amplification, and to complement the analysis of the dynamical regimes, we quantify the geometry of output trajectories through the effective rank of both the eigenvector and the dynamics matrices. Counter-intuitively, we find that shrinking the eigenspectrum’s imaginary distribution leads to highly amplifying regimes in linear and long-lasting dynamics in nonlinear networks. We also find a trade-off between amplification and dimensionality of neuronal dynamics, i.e., trajectories in neuronal state-space. Networks that can amplify a large number of orthogonal initial conditions produce neuronal trajectories that lie in the same subspace of the neuronal state-space. Finally, we examine networks of excitatory and inhibitory neurons. We find that the strength of global inhibition is directly linked with the amplitude of amplification, such that weakening inhibitory weights also decreases amplification, and that the eigenspectrum’s imaginary distribution grows with an increase in the ratio between excitatory-to-inhibitory and excitatory-to-excitatory connectivity strengths. Consequently, the strength of global inhibition reveals itself as a strong signature for amplification and a potential control mechanism to switch dynamical regimes. Our results shed a light on how biological networks, i.e., networks constrained by Dale’s law, may be optimised for specific dynamical regimes.","lang":"eng"}],"external_id":{"pmid":["35969604"],"isi":["000937227700001"]},"article_number":"e1010365","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","issue":"8","_id":"12084","scopus_import":"1","department":[{"_id":"TiVo"}],"acknowledgement":"We thank Friedemann Zenke for his comments, especially on the effect of the self loops on the spectrum. We also thank Ken Miller and Bill Podlaski for helpful comments. This research was funded by a Wellcome Trust and Royal Society Henry Dale Research Fellowship (WT100000; TPV), a Wellcome Senior Research Fellowship (214316/Z/18/Z; GC, EJA, and TPV), and a Research Project Grant by the Leverhulme Trust (RPG-2016-446; EJA and TPV). ","citation":{"chicago":"Christodoulou, Georgia, Tim P Vogels, and Everton J. Agnes. “Regimes and Mechanisms of Transient Amplification in Abstract and Biological Neural Networks.” PLoS Computational Biology. Public Library of Science, 2022. https://doi.org/10.1371/journal.pcbi.1010365.","ista":"Christodoulou G, Vogels TP, Agnes EJ. 2022. Regimes and mechanisms of transient amplification in abstract and biological neural networks. PLoS Computational Biology. 18(8), e1010365.","short":"G. Christodoulou, T.P. Vogels, E.J. Agnes, PLoS Computational Biology 18 (2022).","apa":"Christodoulou, G., Vogels, T. P., & Agnes, E. J. (2022). Regimes and mechanisms of transient amplification in abstract and biological neural networks. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1010365","ieee":"G. Christodoulou, T. P. Vogels, and E. J. Agnes, “Regimes and mechanisms of transient amplification in abstract and biological neural networks,” PLoS Computational Biology, vol. 18, no. 8. Public Library of Science, 2022.","mla":"Christodoulou, Georgia, et al. “Regimes and Mechanisms of Transient Amplification in Abstract and Biological Neural Networks.” PLoS Computational Biology, vol. 18, no. 8, e1010365, Public Library of Science, 2022, doi:10.1371/journal.pcbi.1010365.","ama":"Christodoulou G, Vogels TP, Agnes EJ. Regimes and mechanisms of transient amplification in abstract and biological neural networks. PLoS Computational Biology. 2022;18(8). doi:10.1371/journal.pcbi.1010365"},"publisher":"Public Library of Science","tmp":{"image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"quality_controlled":"1","ddc":["570"],"pmid":1,"article_type":"original","oa_version":"Published Version","has_accepted_license":"1","corr_author":"1","date_published":"2022-08-15T00:00:00Z","doi":"10.1371/journal.pcbi.1010365","title":"Regimes and mechanisms of transient amplification in abstract and biological neural networks","article_processing_charge":"No","month":"08","oa":1,"isi":1,"volume":18,"intvolume":" 18","fulldoi":"https://doi.org/10.1371/journal.pcbi.1010365","date_created":"2022-09-11T22:01:56Z","file":[{"checksum":"8a81ab29f837991ee0ea770817c4a50e","file_size":2867337,"file_id":"12090","access_level":"open_access","date_created":"2022-09-12T07:47:55Z","file_name":"2022_PLoSCompBio_Christodoulou.pdf","success":1,"date_updated":"2022-09-12T07:47:55Z","content_type":"application/pdf","creator":"dernst","relation":"main_file"}],"publication":"PLoS Computational Biology","publication_status":"published","publication_identifier":{"eissn":["1553-7358"]},"date_updated":"2025-06-11T13:51:21Z","year":"2022","type":"journal_article"},{"year":"2022","type":"conference","date_created":"2023-07-16T22:01:12Z","file":[{"date_created":"2023-07-18T06:32:38Z","date_updated":"2023-07-18T06:32:38Z","success":1,"file_name":"2022_PMLR_vanderPlas.pdf","file_id":"13243","file_size":585135,"checksum":"7530a93ef42e10b4db1e5e4b69796e93","access_level":"open_access","relation":"main_file","content_type":"application/pdf","creator":"dernst"}],"publication":"Proceedings of Machine Learning Research","publication_status":"published","publication_identifier":{"eissn":["2640-3498"]},"date_updated":"2025-04-14T07:54:31Z","intvolume":" 199","volume":199,"month":"12","oa":1,"title":"Predictive learning enables neural networks to learn complex working memory tasks","article_processing_charge":"No","date_published":"2022-12-01T00:00:00Z","ddc":["000"],"quality_controlled":"1","oa_version":"Published Version","page":"518-531","has_accepted_license":"1","publisher":"ML Research Press","_id":"13239","scopus_import":"1","department":[{"_id":"TiVo"}],"acknowledgement":"The authors would like to thank members of the Vogels lab and Manohar lab, as well as Adam Packer, Andrew Saxe, Stefano Sarao Mannelli and Jacob Bakermans for fruitful discussions and comments on earlier versions of the manuscript.\r\nTLvdP was supported by funding from the Biotechnology and Biological Sciences Research Council (BBSRC) [grant number BB/M011224/1]. TPV was supported by an ERC Consolidator Grant (SYNAPSEEK). SGM was funded by a MRC Clinician Scientist Fellowship MR/P00878X and Leverhulme Grant RPG-2018-310.","citation":{"ama":"Van Der Plas TL, Vogels TP, Manohar SG. Predictive learning enables neural networks to learn complex working memory tasks. In: Proceedings of Machine Learning Research. Vol 199. ML Research Press; 2022:518-531.","mla":"Van Der Plas, Thijs L., et al. “Predictive Learning Enables Neural Networks to Learn Complex Working Memory Tasks.” Proceedings of Machine Learning Research, vol. 199, ML Research Press, 2022, pp. 518–31.","ieee":"T. L. Van Der Plas, T. P. Vogels, and S. G. Manohar, “Predictive learning enables neural networks to learn complex working memory tasks,” in Proceedings of Machine Learning Research, 2022, vol. 199, pp. 518–531.","apa":"Van Der Plas, T. L., Vogels, T. P., & Manohar, S. G. (2022). Predictive learning enables neural networks to learn complex working memory tasks. In Proceedings of Machine Learning Research (Vol. 199, pp. 518–531). ML Research Press.","short":"T.L. Van Der Plas, T.P. Vogels, S.G. Manohar, in:, Proceedings of Machine Learning Research, ML Research Press, 2022, pp. 518–531.","ista":"Van Der Plas TL, Vogels TP, Manohar SG. 2022. Predictive learning enables neural networks to learn complex working memory tasks. Proceedings of Machine Learning Research. vol. 199, 518–531.","chicago":"Van Der Plas, Thijs L., Tim P Vogels, and Sanjay G. Manohar. “Predictive Learning Enables Neural Networks to Learn Complex Working Memory Tasks.” In Proceedings of Machine Learning Research, 199:518–31. ML Research Press, 2022."},"ec_funded":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","abstract":[{"lang":"eng","text":"Brains are thought to engage in predictive learning - learning to predict upcoming stimuli - to construct an internal model of their environment. This is especially notable for spatial navigation, as first described by Tolman’s latent learning tasks. However, predictive learning has also been observed in sensory cortex, in settings unrelated to spatial navigation. Apart from normative frameworks such as active inference or efficient coding, what could be the utility of learning to predict the patterns of occurrence of correlated stimuli? Here we show that prediction, and thereby the construction of an internal model of sequential stimuli, can bootstrap the learning process of a working memory task in a recurrent neural network. We implemented predictive learning alongside working memory match-tasks, and networks emerged to solve the prediction task first by encoding information across time to predict upcoming stimuli, and then eavesdropped on this solution to solve the matching task. Eavesdropping was most beneficial when neural resources were limited. Hence, predictive learning acts as a general neural mechanism to learn to store sensory information that can later be essential for working memory tasks."}],"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."}],"status":"public","author":[{"full_name":"Van Der Plas, Thijs L.","last_name":"Van Der Plas","first_name":"Thijs L."},{"first_name":"Tim P","orcid":"0000-0003-3295-6181","full_name":"Vogels, Tim P","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","last_name":"Vogels"},{"first_name":"Sanjay G.","last_name":"Manohar","full_name":"Manohar, Sanjay G."}],"language":[{"iso":"eng"}],"day":"01","file_date_updated":"2023-07-18T06:32:38Z"},{"intvolume":" 110","isi":1,"volume":110,"year":"2022","type":"journal_article","date_updated":"2026-06-18T08:47:45Z","date_created":"2022-02-13T23:01:34Z","fulldoi":"https://doi.org/10.1016/j.neuron.2022.01.014","publication_status":"published","publication":"Neuron","publication_identifier":{"eissn":["1097-4199"]},"doi":"10.1016/j.neuron.2022.01.014","date_published":"2022-02-02T00:00:00Z","page":"361-362","corr_author":"1","quality_controlled":"1","ddc":["570"],"pmid":1,"main_file_link":[{"open_access":"1","url":"https://doi.org/10.1016/j.neuron.2022.01.014"}],"article_type":"letter_note","oa_version":"Published Version","oa":1,"month":"02","article_processing_charge":"No","title":"A familiar thought: Machines that replace us?","issue":"3","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publisher":"Elsevier","citation":{"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?” Neuron. Elsevier, 2022. https://doi.org/10.1016/j.neuron.2022.01.014.","ama":"Confavreux BJ, Vogels TP. A familiar thought: Machines that replace us? Neuron. 2022;110(3):361-362. doi:10.1016/j.neuron.2022.01.014","ieee":"B. J. Confavreux and T. P. Vogels, “A familiar thought: Machines that replace us?,” Neuron, vol. 110, no. 3. Elsevier, pp. 361–362, 2022.","mla":"Confavreux, Basile J., and Tim P. Vogels. “A Familiar Thought: Machines That Replace Us?” Neuron, vol. 110, no. 3, Elsevier, 2022, pp. 361–62, doi:10.1016/j.neuron.2022.01.014.","short":"B.J. Confavreux, T.P. Vogels, Neuron 110 (2022) 361–362.","apa":"Confavreux, B. J., & Vogels, T. P. (2022). A familiar thought: Machines that replace us? Neuron. Elsevier. https://doi.org/10.1016/j.neuron.2022.01.014"},"department":[{"_id":"TiVo"}],"_id":"10753","scopus_import":"1","status":"public","day":"02","author":[{"first_name":"Basile J","last_name":"Confavreux","full_name":"Confavreux, Basile J","id":"C7610134-B532-11EA-BD9F-F5753DDC885E"},{"orcid":"0000-0003-3295-6181","first_name":"Tim P","last_name":"Vogels","full_name":"Vogels, Tim P","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425"}],"language":[{"iso":"eng"}],"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"}],"external_id":{"pmid":["35114107"],"isi":["000751819100005"]}},{"year":"2021","type":"journal_article","fulldoi":"https://doi.org/10.1162/neco_a_01367","file":[{"relation":"main_file","creator":"dernst","content_type":"application/pdf","file_name":"2021_NeuralComputation_Zenke.pdf","date_updated":"2022-04-08T06:05:39Z","success":1,"date_created":"2022-04-08T06:05:39Z","access_level":"open_access","checksum":"eac5a51c24c8989ae7cf9ae32ec3bc95","file_id":"11131","file_size":1611614}],"date_created":"2020-08-12T12:08:24Z","publication_status":"published","publication_identifier":{"issn":["0899-7667"],"eissn":["1530-888X"]},"publication":"Neural Computation","date_updated":"2025-04-14T09:44:14Z","intvolume":" 33","isi":1,"volume":33,"month":"03","oa":1,"title":"The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks","article_processing_charge":"No","date_published":"2021-03-01T00:00:00Z","doi":"10.1162/neco_a_01367","tmp":{"image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"ddc":["000","570"],"pmid":1,"quality_controlled":"1","oa_version":"Published Version","article_type":"original","has_accepted_license":"1","page":"899-925","corr_author":"1","publisher":"MIT Press","department":[{"_id":"TiVo"}],"_id":"8253","scopus_import":"1","acknowledgement":"F.Z. was supported by the Wellcome Trust (110124/Z/15/Z) and the Novartis Research Foundation. T.P.V. was supported by a Wellcome Trust Sir Henry Dale Research fellowship (WT100000), a Wellcome Trust Senior Research Fellowship (214316/Z/18/Z), and an ERC Consolidator Grant SYNAPSEEK.","citation":{"short":"F. Zenke, T.P. Vogels, Neural Computation 33 (2021) 899–925.","apa":"Zenke, F., & Vogels, T. P. (2021). The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks. Neural Computation. MIT Press. https://doi.org/10.1162/neco_a_01367","mla":"Zenke, Friedemann, and Tim P. Vogels. “The Remarkable Robustness of Surrogate Gradient Learning for Instilling Complex Function in Spiking Neural Networks.” Neural Computation, vol. 33, no. 4, MIT Press, 2021, pp. 899–925, doi:10.1162/neco_a_01367.","ieee":"F. Zenke and T. P. Vogels, “The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks,” Neural Computation, vol. 33, no. 4. MIT Press, pp. 899–925, 2021.","ama":"Zenke F, Vogels TP. The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks. Neural Computation. 2021;33(4):899-925. doi:10.1162/neco_a_01367","chicago":"Zenke, Friedemann, and Tim P Vogels. “The Remarkable Robustness of Surrogate Gradient Learning for Instilling Complex Function in Spiking Neural Networks.” Neural Computation. MIT Press, 2021. https://doi.org/10.1162/neco_a_01367.","ista":"Zenke F, Vogels TP. 2021. The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks. Neural Computation. 33(4), 899–925."},"ec_funded":1,"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","issue":"4","abstract":[{"lang":"eng","text":"Brains process information in spiking neural networks. Their intricate connections shape the diverse functions these networks perform. In comparison, the functional capabilities of models of spiking networks are still rudimentary. This shortcoming is mainly due to the lack of insight and practical algorithms to construct the necessary connectivity. Any such algorithm typically attempts to build networks by iteratively reducing the error compared to a desired output. But assigning credit to hidden units in multi-layered spiking networks has remained challenging due to the non-differentiable nonlinearity of spikes. To avoid this issue, one can employ surrogate gradients to discover the required connectivity in spiking network models. However, the choice of a surrogate is not unique, raising the question of how its implementation influences the effectiveness of the method. Here, we use numerical simulations to systematically study how essential design parameters of surrogate gradients impact learning performance on a range of classification problems. We show that surrogate gradient learning is robust to different shapes of underlying surrogate derivatives, but the choice of the derivative’s scale can substantially affect learning performance. When we combine surrogate gradients with a suitable activity regularization technique, robust information processing can be achieved in spiking networks even at the sparse activity limit. Our study provides a systematic account of the remarkable robustness of surrogate gradient learning and serves as a practical guide to model functional spiking neural networks."}],"external_id":{"isi":["000663433900003"],"pmid":["33513328"]},"status":"public","project":[{"_id":"0aacfa84-070f-11eb-9043-d7eb2c709234","name":"Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning.","call_identifier":"H2020","grant_number":"819603"},{"grant_number":"214316/Z/18/Z","_id":"c084a126-5a5b-11eb-8a69-d75314a70a87","name":"Whatâs in a memory? Spatiotemporal dynamics in strongly coupled recurrent neuronal networks."}],"author":[{"orcid":"0000-0003-1883-644X","first_name":"Friedemann","full_name":"Zenke, Friedemann","last_name":"Zenke"},{"first_name":"Tim P","orcid":"0000-0003-3295-6181","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","full_name":"Vogels, Tim P","last_name":"Vogels"}],"language":[{"iso":"eng"}],"day":"01","file_date_updated":"2022-04-08T06:05:39Z"},{"citation":{"ista":"Bozelos P, Vogels TP. 2021. Talking science, online. Nature Reviews Neuroscience. 22(1), 1–2.","chicago":"Bozelos, Panagiotis, and Tim P Vogels. “Talking Science, Online.” Nature Reviews Neuroscience. Springer Nature, 2021. https://doi.org/10.1038/s41583-020-00408-6.","ieee":"P. Bozelos and T. P. Vogels, “Talking science, online,” Nature Reviews Neuroscience, vol. 22, no. 1. Springer Nature, pp. 1–2, 2021.","mla":"Bozelos, Panagiotis, and Tim P. Vogels. “Talking Science, Online.” Nature Reviews Neuroscience, vol. 22, no. 1, Springer Nature, 2021, pp. 1–2, doi:10.1038/s41583-020-00408-6.","ama":"Bozelos P, Vogels TP. Talking science, online. Nature Reviews Neuroscience. 2021;22(1):1-2. doi:10.1038/s41583-020-00408-6","short":"P. Bozelos, T.P. Vogels, Nature Reviews Neuroscience 22 (2021) 1–2.","apa":"Bozelos, P., & Vogels, T. P. (2021). Talking science, online. Nature Reviews Neuroscience. Springer Nature. https://doi.org/10.1038/s41583-020-00408-6"},"_id":"8757","scopus_import":"1","department":[{"_id":"TiVo"}],"publisher":"Springer Nature","issue":"1","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","external_id":{"isi":["000588256300001"],"pmid":["33173190"]},"abstract":[{"text":"Traditional scientific conferences and seminar events have been hugely disrupted by the COVID-19 pandemic, paving the way for virtual forms of scientific communication to take hold and be put to the test.","lang":"eng"}],"file_date_updated":"2021-02-04T10:34:22Z","day":"01","language":[{"iso":"eng"}],"author":[{"full_name":"Bozelos, Panagiotis","id":"52e9c652-2982-11eb-81d4-b43d94c63700","last_name":"Bozelos","first_name":"Panagiotis"},{"orcid":"0000-0003-3295-6181","first_name":"Tim P","last_name":"Vogels","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","full_name":"Vogels, Tim P"}],"status":"public","date_updated":"2025-07-10T12:01:24Z","publication_status":"published","publication":"Nature Reviews Neuroscience","publication_identifier":{"eissn":["1471-0048"],"issn":["1471-003X"]},"date_created":"2020-11-15T23:01:18Z","fulldoi":"https://doi.org/10.1038/s41583-020-00408-6","file":[{"creator":"dernst","content_type":"application/pdf","relation":"main_file","access_level":"open_access","file_id":"9088","file_size":683634,"checksum":"7985d7dff94c086e35b94a911d78d9ad","date_updated":"2021-02-04T10:34:22Z","success":1,"file_name":"2021_NatureNeuroScience_Bozelos.pdf","date_created":"2021-02-04T10:34:22Z"}],"type":"journal_article","year":"2021","volume":22,"isi":1,"intvolume":" 22","article_processing_charge":"No","title":"Talking science, online","oa":1,"month":"01","page":"1-2","has_accepted_license":"1","article_type":"letter_note","oa_version":"Published Version","ddc":["570"],"pmid":1,"quality_controlled":"1","doi":"10.1038/s41583-020-00408-6","date_published":"2021-01-01T00:00:00Z"},{"title":"Towards democratizing and automating online conferences: Lessons from the Neuromatch Conferences","article_processing_charge":"No","month":"04","oa":1,"ddc":["570"],"pmid":1,"quality_controlled":"1","oa_version":"Submitted Version","article_type":"original","page":"265-268","has_accepted_license":"1","date_published":"2021-04-01T00:00:00Z","doi":"10.1016/j.tics.2021.01.007","date_created":"2021-03-07T23:01:25Z","fulldoi":"https://doi.org/10.1016/j.tics.2021.01.007","file":[{"relation":"main_file","creator":"dernst","content_type":"application/pdf","date_created":"2022-05-27T07:31:24Z","file_name":"2021_TrendsCognitiveSciences_Achakulvisut.pdf","success":1,"date_updated":"2022-05-27T07:31:24Z","checksum":"87e39ea7bd266b976e8631b66979214d","file_size":380720,"file_id":"11415","access_level":"open_access"}],"publication":"Trends in Cognitive Sciences","publication_identifier":{"eissn":["1879-307X"],"issn":["1364-6613"]},"publication_status":"published","date_updated":"2023-08-07T13:59:07Z","year":"2021","type":"journal_article","isi":1,"volume":25,"intvolume":" 25","external_id":{"pmid":["33608214"],"isi":["000627418000001"]},"abstract":[{"lang":"eng","text":"Legacy conferences are costly and time consuming, and exclude scientists lacking various resources or abilities. During the 2020 pandemic, we created an online conference platform, Neuromatch Conferences (NMC), aimed at developing technological and cultural changes to make conferences more democratic, scalable, and accessible. We discuss the lessons we learned."}],"author":[{"first_name":"Titipat","last_name":"Achakulvisut","full_name":"Achakulvisut, Titipat"},{"first_name":"Tulakan","full_name":"Ruangrong, Tulakan","last_name":"Ruangrong"},{"first_name":"Patrick","full_name":"Mineault, Patrick","last_name":"Mineault"},{"last_name":"Vogels","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","full_name":"Vogels, Tim P","first_name":"Tim P","orcid":"0000-0003-3295-6181"},{"last_name":"Peters","full_name":"Peters, Megan A.K.","first_name":"Megan A.K."},{"last_name":"Poirazi","full_name":"Poirazi, Panayiota","first_name":"Panayiota"},{"first_name":"Christopher","full_name":"Rozell, Christopher","last_name":"Rozell"},{"last_name":"Wyble","full_name":"Wyble, Brad","first_name":"Brad"},{"first_name":"Dan F.M.","full_name":"Goodman, Dan F.M.","last_name":"Goodman"},{"first_name":"Konrad Paul","last_name":"Kording","full_name":"Kording, Konrad Paul"}],"language":[{"iso":"eng"}],"day":"01","file_date_updated":"2022-05-27T07:31:24Z","status":"public","department":[{"_id":"TiVo"}],"_id":"9228","scopus_import":"1","acknowledgement":"We thank all of our volunteers from the NMC conferences (list of names in the appendix). We also thank the NSF for support from 1734220 to B.W., and DARPA for support to T.A.","citation":{"apa":"Achakulvisut, T., Ruangrong, T., Mineault, P., Vogels, T. P., Peters, M. A. K., Poirazi, P., … Kording, K. P. (2021). Towards democratizing and automating online conferences: Lessons from the Neuromatch Conferences. Trends in Cognitive Sciences. Elsevier. https://doi.org/10.1016/j.tics.2021.01.007","short":"T. Achakulvisut, T. Ruangrong, P. Mineault, T.P. Vogels, M.A.K. Peters, P. Poirazi, C. Rozell, B. Wyble, D.F.M. Goodman, K.P. Kording, Trends in Cognitive Sciences 25 (2021) 265–268.","mla":"Achakulvisut, Titipat, et al. “Towards Democratizing and Automating Online Conferences: Lessons from the Neuromatch Conferences.” Trends in Cognitive Sciences, vol. 25, no. 4, Elsevier, 2021, pp. 265–68, doi:10.1016/j.tics.2021.01.007.","ieee":"T. Achakulvisut et al., “Towards democratizing and automating online conferences: Lessons from the Neuromatch Conferences,” Trends in Cognitive Sciences, vol. 25, no. 4. Elsevier, pp. 265–268, 2021.","ama":"Achakulvisut T, Ruangrong T, Mineault P, et al. Towards democratizing and automating online conferences: Lessons from the Neuromatch Conferences. Trends in Cognitive Sciences. 2021;25(4):265-268. doi:10.1016/j.tics.2021.01.007","chicago":"Achakulvisut, Titipat, Tulakan Ruangrong, Patrick Mineault, Tim P Vogels, Megan A.K. Peters, Panayiota Poirazi, Christopher Rozell, Brad Wyble, Dan F.M. Goodman, and Konrad Paul Kording. “Towards Democratizing and Automating Online Conferences: Lessons from the Neuromatch Conferences.” Trends in Cognitive Sciences. Elsevier, 2021. https://doi.org/10.1016/j.tics.2021.01.007.","ista":"Achakulvisut T, Ruangrong T, Mineault P, Vogels TP, Peters MAK, Poirazi P, Rozell C, Wyble B, Goodman DFM, Kording KP. 2021. Towards democratizing and automating online conferences: Lessons from the Neuromatch Conferences. Trends in Cognitive Sciences. 25(4), 265–268."},"publisher":"Elsevier","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","issue":"4"},{"citation":{"ista":"Braun L, Vogels TP. 2021. Online learning of neural computations from sparse temporal feedback. 35th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 20, 16437–16450.","chicago":"Braun, Lukas, and Tim P Vogels. “Online Learning of Neural Computations from Sparse Temporal Feedback.” In 35th Conference on Neural Information Processing Systems, 20:16437–50. Neural Information Processing Systems Foundation, 2021.","ieee":"L. Braun and T. P. Vogels, “Online learning of neural computations from sparse temporal feedback,” in 35th Conference on Neural Information Processing Systems, Virtual, Online, 2021, vol. 20, pp. 16437–16450.","mla":"Braun, Lukas, and Tim P. Vogels. “Online Learning of Neural Computations from Sparse Temporal Feedback.” 35th Conference on Neural Information Processing Systems, vol. 20, Neural Information Processing Systems Foundation, 2021, pp. 16437–50.","ama":"Braun L, Vogels TP. Online learning of neural computations from sparse temporal feedback. In: 35th Conference on Neural Information Processing Systems. Vol 20. Neural Information Processing Systems Foundation; 2021:16437-16450.","apa":"Braun, L., & Vogels, T. P. (2021). Online learning of neural computations from sparse temporal feedback. In 35th Conference on Neural Information Processing Systems (Vol. 20, pp. 16437–16450). Virtual, Online: Neural Information Processing Systems Foundation.","short":"L. Braun, T.P. Vogels, in:, 35th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2021, pp. 16437–16450."},"das_tickbox":"1","acknowledgement":"We would like to thank Professor Dr. Henning Sprekeler for his valuable suggestions and Dr. Andrew Saxe, Milan Klöwer and Anna Wallis for their constructive feedback on the manuscript. Lukas Braun was supported by the Network of European Neuroscience Schools through their NENS Exchange Grant program, by the European Union through their European Community Action Scheme for the Mobility of University Students, the Woodward Scholarship awarded by Wadham College, Oxford and the Medical Research Council [MR/N013468/1]. Tim P. Vogels was supported by a Wellcome Trust Senior Research Fellowship [214316/Z/18/Z].","_id":"11453","scopus_import":"1","department":[{"_id":"TiVo"}],"publisher":"Neural Information Processing Systems Foundation","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","alternative_title":["Advances in Neural Information Processing Systems"],"abstract":[{"lang":"eng","text":"Neuronal computations depend on synaptic connectivity and intrinsic electrophysiological properties. Synaptic connectivity determines which inputs from presynaptic neurons are integrated, while cellular properties determine how inputs are filtered over time. Unlike their biological counterparts, most computational approaches to learning in simulated neural networks are limited to changes in synaptic connectivity. However, if intrinsic parameters change, neural computations are altered drastically. Here, we include the parameters that determine the intrinsic properties,\r\ne.g., time constants and reset potential, into the learning paradigm. Using sparse feedback signals that indicate target spike times, and gradient-based parameter updates, we show that the intrinsic parameters can be learned along with the synaptic weights to produce specific input-output functions. Specifically, we use a teacher-student paradigm in which a randomly initialised leaky integrate-and-fire or resonate-and-fire neuron must recover the parameters of a teacher neuron. We show that complex temporal functions can be learned online and without backpropagation through time, relying on event-based updates only. Our results are a step towards online learning of neural computations from ungraded and unsigned sparse feedback signals with a biologically inspired learning mechanism."}],"day":"01","language":[{"iso":"eng"}],"author":[{"first_name":"Lukas","full_name":"Braun, Lukas","last_name":"Braun"},{"last_name":"Vogels","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","full_name":"Vogels, Tim P","orcid":"0000-0003-3295-6181","first_name":"Tim P"}],"status":"public","project":[{"_id":"c084a126-5a5b-11eb-8a69-d75314a70a87","name":"Whatâs in a memory? Spatiotemporal dynamics in strongly coupled recurrent neuronal networks.","grant_number":"214316/Z/18/Z"}],"date_updated":"2026-07-08T05:45:00Z","publication":"35th Conference on Neural Information Processing Systems","publication_identifier":{"issn":["1049-5258"],"isbn":["9781713845393"]},"publication_status":"published","date_created":"2022-06-19T22:01:59Z","type":"conference","year":"2021","volume":20,"conference":{"end_date":"2021-12-14","location":"Virtual, Online","name":"NeurIPS: Neural Information Processing Systems","start_date":"2021-12-06"},"intvolume":" 20","article_processing_charge":"No","title":"Online learning of neural computations from sparse temporal feedback","oa":1,"month":"12","corr_author":"1","page":"16437-16450","oa_version":"Published Version","main_file_link":[{"open_access":"1","url":"https://proceedings.neurips.cc/paper/2021/file/88e1ce84f9feef5a08d0df0334c53468-Paper.pdf"}],"ddc":["000","570"],"quality_controlled":"1","date_published":"2021-12-01T00:00:00Z"},{"oa_version":"Published Version","article_type":"original","ddc":["570"],"tmp":{"image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"pmid":1,"quality_controlled":"1","has_accepted_license":"1","page":"9634-9649","date_published":"2020-12-09T00:00:00Z","doi":"10.1523/JNEUROSCI.0276-20.2020","title":"Complementary inhibitory weight profiles emerge from plasticity and allow attentional switching of receptive fields","article_processing_charge":"No","month":"12","oa":1,"isi":1,"volume":40,"intvolume":" 40","publication":"The Journal of Neuroscience","publication_identifier":{"eissn":["1529-2401"]},"publication_status":"published","fulldoi":"https://doi.org/10.1523/JNEUROSCI.0276-20.2020","date_created":"2020-07-16T12:25:04Z","file":[{"date_created":"2020-12-28T08:31:47Z","file_name":"2020_JourNeuroscience_Agnes.pdf","success":1,"date_updated":"2020-12-28T08:31:47Z","checksum":"7977e4dd6b89357d1a5cc88babac56da","file_id":"8977","file_size":2750920,"access_level":"open_access","relation":"main_file","creator":"dernst","content_type":"application/pdf"}],"date_updated":"2023-08-22T07:54:26Z","type":"journal_article","year":"2020","language":[{"iso":"eng"}],"author":[{"full_name":"Agnes, Everton J.","last_name":"Agnes","first_name":"Everton J.","orcid":"0000-0001-7184-7311"},{"last_name":"Luppi","full_name":"Luppi, Andrea I.","first_name":"Andrea I."},{"full_name":"Vogels, Tim P","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","last_name":"Vogels","orcid":"0000-0003-3295-6181","first_name":"Tim P"}],"file_date_updated":"2020-12-28T08:31:47Z","day":"09","status":"public","abstract":[{"text":"Cortical areas comprise multiple types of inhibitory interneurons with stereotypical connectivity motifs, but their combined effect on postsynaptic dynamics has been largely unexplored. Here, we analyse the response of a single postsynaptic model neuron receiving tuned excitatory connections alongside inhibition from two plastic populations. Depending on the inhibitory plasticity rule, synapses remain unspecific (flat), become anti-correlated to, or mirror excitatory synapses. Crucially, the neuron’s receptive field, i.e., its response to presynaptic stimuli, depends on the modulatory state of inhibition. When both inhibitory populations are active, inhibition balances excitation, resulting in uncorrelated postsynaptic responses regardless of the inhibitory tuning profiles. Modulating the activity of a given inhibitory population produces strong correlations to either preferred or non-preferred inputs, in line with recent experimental findings showing dramatic context-dependent changes of neurons’ receptive fields. We thus confirm that a neuron’s receptive field doesn’t follow directly from the weight profiles of its presynaptic afferents.","lang":"eng"}],"external_id":{"pmid":["33168622"],"isi":["000606706400009"]},"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","issue":"50","_id":"8126","department":[{"_id":"TiVo"}],"scopus_import":"1","citation":{"ista":"Agnes EJ, Luppi AI, Vogels TP. 2020. Complementary inhibitory weight profiles emerge from plasticity and allow attentional switching of receptive fields. The Journal of Neuroscience. 40(50), 9634–9649.","chicago":"Agnes, Everton J., Andrea I. Luppi, and Tim P Vogels. “Complementary Inhibitory Weight Profiles Emerge from Plasticity and Allow Attentional Switching of Receptive Fields.” The Journal of Neuroscience. Society for Neuroscience, 2020. https://doi.org/10.1523/JNEUROSCI.0276-20.2020.","ama":"Agnes EJ, Luppi AI, Vogels TP. Complementary inhibitory weight profiles emerge from plasticity and allow attentional switching of receptive fields. The Journal of Neuroscience. 2020;40(50):9634-9649. doi:10.1523/JNEUROSCI.0276-20.2020","ieee":"E. J. Agnes, A. I. Luppi, and T. P. Vogels, “Complementary inhibitory weight profiles emerge from plasticity and allow attentional switching of receptive fields,” The Journal of Neuroscience, vol. 40, no. 50. Society for Neuroscience, pp. 9634–9649, 2020.","mla":"Agnes, Everton J., et al. “Complementary Inhibitory Weight Profiles Emerge from Plasticity and Allow Attentional Switching of Receptive Fields.” The Journal of Neuroscience, vol. 40, no. 50, Society for Neuroscience, 2020, pp. 9634–49, doi:10.1523/JNEUROSCI.0276-20.2020.","apa":"Agnes, E. J., Luppi, A. I., & Vogels, T. P. (2020). Complementary inhibitory weight profiles emerge from plasticity and allow attentional switching of receptive fields. The Journal of Neuroscience. Society for Neuroscience. https://doi.org/10.1523/JNEUROSCI.0276-20.2020","short":"E.J. Agnes, A.I. Luppi, T.P. Vogels, The Journal of Neuroscience 40 (2020) 9634–9649."},"publisher":"Society for Neuroscience"},{"language":[{"iso":"eng"}],"author":[{"first_name":"Pedro J.","orcid":"0000-0002-6987-4836","full_name":"Gonçalves, Pedro J.","last_name":"Gonçalves"},{"first_name":"Jan-Matthis","orcid":"0000-0003-4320-4663","last_name":"Lueckmann","full_name":"Lueckmann, Jan-Matthis"},{"first_name":"Michael","orcid":"0000-0002-3573-0404","full_name":"Deistler, Michael","last_name":"Deistler"},{"first_name":"Marcel","orcid":"0000-0001-6044-6627","last_name":"Nonnenmacher","full_name":"Nonnenmacher, Marcel"},{"last_name":"Öcal","full_name":"Öcal, Kaan","first_name":"Kaan","orcid":"0000-0002-8528-6858"},{"full_name":"Bassetto, Giacomo","last_name":"Bassetto","first_name":"Giacomo"},{"id":"BA06AFEE-A4BA-11EA-AE5C-14673DDC885E","full_name":"Chintaluri, Chaitanya","last_name":"Chintaluri","orcid":"0000-0003-4252-1608","first_name":"Chaitanya"},{"orcid":"0000-0001-6619-7502","first_name":"William F.","full_name":"Podlaski, William F.","last_name":"Podlaski"},{"last_name":"Haddad","full_name":"Haddad, Sara A.","orcid":"0000-0003-0807-0823","first_name":"Sara A."},{"first_name":"Tim P","orcid":"0000-0003-3295-6181","full_name":"Vogels, Tim P","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","last_name":"Vogels"},{"last_name":"Greenberg","full_name":"Greenberg, David S.","first_name":"David S."},{"full_name":"Macke, Jakob H.","last_name":"Macke","orcid":"0000-0001-5154-8912","first_name":"Jakob H."}],"file_date_updated":"2020-10-27T11:37:32Z","day":"17","status":"public","project":[{"_id":"0aacfa84-070f-11eb-9043-d7eb2c709234","name":"Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning.","call_identifier":"H2020","grant_number":"819603"}],"abstract":[{"lang":"eng","text":"Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators—trained using model simulations—to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin–Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics."}],"external_id":{"pmid":["32940606"],"isi":["000584989400001"]},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_number":"e56261","ec_funded":1,"department":[{"_id":"TiVo"}],"_id":"8127","scopus_import":"1","citation":{"ista":"Gonçalves PJ, Lueckmann J-M, Deistler M, Nonnenmacher M, Öcal K, Bassetto G, Chintaluri C, Podlaski WF, Haddad SA, Vogels TP, Greenberg DS, Macke JH. 2020. Training deep neural density estimators to identify mechanistic models of neural dynamics. eLife. 9, e56261.","chicago":"Gonçalves, Pedro J., Jan-Matthis Lueckmann, Michael Deistler, Marcel Nonnenmacher, Kaan Öcal, Giacomo Bassetto, Chaitanya Chintaluri, et al. “Training Deep Neural Density Estimators to Identify Mechanistic Models of Neural Dynamics.” ELife. eLife Sciences Publications, 2020. https://doi.org/10.7554/eLife.56261.","ieee":"P. J. Gonçalves et al., “Training deep neural density estimators to identify mechanistic models of neural dynamics,” eLife, vol. 9. eLife Sciences Publications, 2020.","mla":"Gonçalves, Pedro J., et al. “Training Deep Neural Density Estimators to Identify Mechanistic Models of Neural Dynamics.” ELife, vol. 9, e56261, eLife Sciences Publications, 2020, doi:10.7554/eLife.56261.","ama":"Gonçalves PJ, Lueckmann J-M, Deistler M, et al. Training deep neural density estimators to identify mechanistic models of neural dynamics. eLife. 2020;9. doi:10.7554/eLife.56261","short":"P.J. Gonçalves, J.-M. Lueckmann, M. Deistler, M. Nonnenmacher, K. Öcal, G. Bassetto, C. Chintaluri, W.F. Podlaski, S.A. Haddad, T.P. Vogels, D.S. Greenberg, J.H. Macke, ELife 9 (2020).","apa":"Gonçalves, P. J., Lueckmann, J.-M., Deistler, M., Nonnenmacher, M., Öcal, K., Bassetto, G., … Macke, J. H. (2020). Training deep neural density estimators to identify mechanistic models of neural dynamics. ELife. eLife Sciences Publications. https://doi.org/10.7554/eLife.56261"},"das_tickbox":"1","acknowledgement":"We thank Mahmood S Hoseini and Michael Stryker for sharing their data for Figure 2, and Philipp Berens, Sean Bittner, Jan Boelts, John Cunningham, Richard Gao, Scott Linderman, Eve Marder, Iain Murray, George Papamakarios, Astrid Prinz, Auguste Schulz and Srinivas Turaga for discussions and/or comments on the manuscript. This work was supported by the German Research Foundation (DFG) through SFB 1233 ‘Robust Vision’, (276693517), SFB 1089 ‘Synaptic Microcircuits’, SPP 2041 ‘Computational Connectomics’ and Germany's Excellence Strategy – EXC-Number 2064/1 – Project number 390727645 and the German Federal Ministry of Education and Research (BMBF, project ‘ADIMEM’, FKZ 01IS18052 A-D) to JHM, a Sir Henry Dale Fellowship by the Wellcome Trust and the Royal Society (WT100000; WFP and TPV), a Wellcome Trust Senior Research Fellowship (214316/Z/18/Z; TPV), a ERC Consolidator Grant (SYNAPSEEK; WPF and CC), and a UK Research and Innovation, Biotechnology and Biological Sciences Research Council (CC, UKRI-BBSRC BB/N019512/1). We gratefully acknowledge the Leibniz Supercomputing Centre for funding this project by providing computing time on its Linux-Cluster.","publisher":"eLife Sciences Publications","oa_version":"Published Version","article_type":"original","tmp":{"image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"pmid":1,"ddc":["570"],"quality_controlled":"1","has_accepted_license":"1","date_published":"2020-09-17T00:00:00Z","doi":"10.7554/eLife.56261","title":"Training deep neural density estimators to identify mechanistic models of neural dynamics","article_processing_charge":"No","month":"09","oa":1,"volume":9,"isi":1,"intvolume":" 9","publication":"eLife","publication_status":"published","publication_identifier":{"eissn":["2050-084X"]},"fulldoi":"https://doi.org/10.7554/eLife.56261","date_created":"2020-07-16T12:26:04Z","file":[{"date_created":"2020-10-27T11:37:32Z","file_name":"2020_eLife_Gonçalves.pdf","date_updated":"2020-10-27T11:37:32Z","success":1,"checksum":"c4300ddcd93ed03fc9c6cdf1f77890be","file_size":17355867,"file_id":"8709","access_level":"open_access","relation":"main_file","creator":"cziletti","content_type":"application/pdf"}],"date_updated":"2026-07-13T12:31:21Z","type":"journal_article","year":"2020"}]