[{"oa_version":"Published Version","month":"02","publication_status":"published","quality_controlled":"1","type":"journal_article","has_accepted_license":"1","_id":"21378","date_published":"2026-02-11T00:00:00Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_processing_charge":"Yes (in subscription journal)","publication_identifier":{"eissn":["2522-5812"]},"oa":1,"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.","year":"2026","issue":"2","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"}],"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"author":[{"last_name":"Amrapali Vishwanath","first_name":"Anjali","full_name":"Amrapali Vishwanath, Anjali"},{"first_name":"Typhaine","last_name":"Comyn","full_name":"Comyn, Typhaine"},{"last_name":"Mira","first_name":"Rodrigo G.","full_name":"Mira, Rodrigo G."},{"full_name":"Brossier, Claire","first_name":"Claire","last_name":"Brossier"},{"full_name":"Pascual-Caro, Carlos","last_name":"Pascual-Caro","first_name":"Carlos"},{"first_name":"Maya","last_name":"Faour","full_name":"Faour, Maya"},{"first_name":"Kahina","last_name":"Boumendil","full_name":"Boumendil, Kahina"},{"first_name":"Chaitanya","last_name":"Chintaluri","id":"BA06AFEE-A4BA-11EA-AE5C-14673DDC885E","orcid":"0000-0003-4252-1608","full_name":"Chintaluri, Chaitanya"},{"full_name":"Ramon-Duaso, Carla","last_name":"Ramon-Duaso","first_name":"Carla"},{"last_name":"Fan","first_name":"Ruolin","full_name":"Fan, Ruolin"},{"first_name":"Kishalay","last_name":"Ghosh","full_name":"Ghosh, Kishalay"},{"full_name":"Farrants, Helen","first_name":"Helen","last_name":"Farrants"},{"full_name":"Berwick, Jean-Paul","last_name":"Berwick","first_name":"Jean-Paul"},{"last_name":"Sivakumar","first_name":"Riya","full_name":"Sivakumar, Riya"},{"full_name":"Lopez-Manzaneda, Mario","last_name":"Lopez-Manzaneda","first_name":"Mario"},{"last_name":"Schreiter","first_name":"Eric R.","full_name":"Schreiter, Eric R."},{"last_name":"Preat","first_name":"Thomas","full_name":"Preat, Thomas"},{"id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","first_name":"Tim P","last_name":"Vogels","full_name":"Vogels, Tim P","orcid":"0000-0003-3295-6181"},{"last_name":"Rangaraju","first_name":"Vidhya","full_name":"Rangaraju, Vidhya"},{"full_name":"Busquets-Garcia, Arnau","first_name":"Arnau","last_name":"Busquets-Garcia"},{"last_name":"Plaçais","first_name":"Pierre-Yves","full_name":"Plaçais, Pierre-Yves"},{"last_name":"Pavlowsky","first_name":"Alice","full_name":"Pavlowsky, Alice"},{"full_name":"de Juan-Sanz, Jaime","first_name":"Jaime","last_name":"de Juan-Sanz"}],"page":"467-488","date_created":"2026-03-02T10:04:49Z","PlanS_conform":"1","OA_type":"hybrid","status":"public","volume":8,"publisher":"Springer Nature","language":[{"iso":"eng"}],"day":"11","ddc":["570"],"article_type":"original","pmid":1,"scopus_import":"1","OA_place":"publisher","file":[{"file_size":5326608,"file_id":"21392","file_name":"2026_NatureMetab_AmrapaliVishwanath.pdf","checksum":"365932a599d05bc9ce8a57204e7a1465","relation":"main_file","date_created":"2026-03-02T15:21:27Z","date_updated":"2026-03-02T15:21:27Z","success":1,"access_level":"open_access","creator":"dernst","content_type":"application/pdf"}],"citation":{"mla":"Amrapali Vishwanath, Anjali, et al. “Mitochondrial Ca2+ Efflux Controls Neuronal Metabolism and Long-Term Memory across Species.” <i>Nature Metabolism</i>, vol. 8, no. 2, Springer Nature, 2026, pp. 467–88, doi:<a href=\"https://doi.org/10.1038/s42255-026-01451-w\">10.1038/s42255-026-01451-w</a>.","ama":"Amrapali Vishwanath A, Comyn T, Mira RG, et al. Mitochondrial Ca2+ efflux controls neuronal metabolism and long-term memory across species. <i>Nature Metabolism</i>. 2026;8(2):467-488. doi:<a href=\"https://doi.org/10.1038/s42255-026-01451-w\">10.1038/s42255-026-01451-w</a>","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. <i>Nature Metabolism</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s42255-026-01451-w\">https://doi.org/10.1038/s42255-026-01451-w</a>","ieee":"A. Amrapali Vishwanath <i>et al.</i>, “Mitochondrial Ca2+ efflux controls neuronal metabolism and long-term memory across species,” <i>Nature Metabolism</i>, 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.","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.” <i>Nature Metabolism</i>. Springer Nature, 2026. <a href=\"https://doi.org/10.1038/s42255-026-01451-w\">https://doi.org/10.1038/s42255-026-01451-w</a>.","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."},"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"}],"department":[{"_id":"TiVo"}],"file_date_updated":"2026-03-02T15:21:27Z","publication":"Nature Metabolism","intvolume":"         8","external_id":{"pmid":["41673453"]},"das_tickbox":"1","doi":"10.1038/s42255-026-01451-w","date_updated":"2026-07-13T12:30:14Z","title":"Mitochondrial Ca2+ efflux controls neuronal metabolism and long-term memory across species"},{"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."}],"author":[{"first_name":"Lena A","last_name":"Schwarz","id":"29A8453C-F248-11E8-B48F-1D18A9856A87","full_name":"Schwarz, Lena A"},{"first_name":"Christoph","last_name":"Dotter","id":"4C66542E-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-9033-9096","full_name":"Dotter, Christoph"},{"full_name":"Isaev, Sergey","last_name":"Isaev","first_name":"Sergey"},{"id":"39383c1b-d3eb-11ef-8d6c-c8cdf4e10c8c","last_name":"Lisi","first_name":"Michela","full_name":"Lisi, Michela"},{"first_name":"Daniel","last_name":"Malzl","full_name":"Malzl, Daniel"},{"full_name":"Büschl, Christoph","last_name":"Büschl","first_name":"Christoph","id":"2a8c054c-0913-11ee-9159-f8ef515809ed"},{"first_name":"Sabrina","last_name":"Ladstätter","full_name":"Ladstätter, Sabrina"},{"full_name":"Oliveira, Bárbara","id":"3B03AA1A-F248-11E8-B48F-1D18A9856A87","first_name":"Bárbara","last_name":"Oliveira"},{"full_name":"Barel, Matteo","id":"8959927b-2236-11ed-bd6e-ea83d94ade0e","last_name":"Barel","first_name":"Matteo"},{"first_name":"Bernadette","last_name":"Basilico","id":"36035796-5ACA-11E9-A75E-7AF2E5697425","orcid":"0000-0003-1843-3173","full_name":"Basilico, Bernadette"},{"full_name":"Chintaluri, Chaitanya","orcid":"0000-0003-4252-1608","last_name":"Chintaluri","first_name":"Chaitanya","id":"BA06AFEE-A4BA-11EA-AE5C-14673DDC885E"},{"full_name":"Gorkiewicz, Sarah","first_name":"Sarah","last_name":"Gorkiewicz","id":"f141a35d-15a9-11ec-9fb2-fef6becc7b6f"},{"full_name":"Goudarzi, Mohammad","id":"3384113A-F248-11E8-B48F-1D18A9856A87","last_name":"Goudarzi","first_name":"Mohammad"},{"full_name":"Belinova, Tereza","id":"0bf89b6a-d28b-11eb-8bd6-f43768e4d368","first_name":"Tereza","last_name":"Belinova"},{"full_name":"Reichl, Stephan","last_name":"Reichl","first_name":"Stephan"},{"full_name":"Sendžikaitė, Gintarė","last_name":"Sendžikaitė","first_name":"Gintarė","id":"dd6d52f2-c50d-11eb-9548-bcf0ff82b344"},{"first_name":"Satish","last_name":"Arcot Jayaram","id":"b0bbee33-09f7-11eb-909c-8b358058d28a","orcid":"0000-0002-2479-2669","full_name":"Arcot Jayaram, Satish"},{"orcid":"0000-0002-3509-1948","full_name":"Koppensteiner, Peter","id":"3B8B25A8-F248-11E8-B48F-1D18A9856A87","first_name":"Peter","last_name":"Koppensteiner"},{"orcid":"0000-0003-1216-9105","full_name":"Sommer, Christoph M","last_name":"Sommer","first_name":"Christoph M","id":"4DF26D8C-F248-11E8-B48F-1D18A9856A87"},{"id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","first_name":"Tim P","last_name":"Vogels","full_name":"Vogels, Tim P","orcid":"0000-0003-3295-6181"},{"first_name":"Jörg","last_name":"Menche","full_name":"Menche, Jörg"},{"full_name":"Adameyko, Igor","first_name":"Igor","last_name":"Adameyko"},{"full_name":"Kharchenko, Peter Vasili","last_name":"Kharchenko","first_name":"Peter Vasili","id":"0095641e-7eb7-11f1-8665-aec51a2ab5e0"},{"full_name":"Bock, Christoph","last_name":"Bock","first_name":"Christoph"},{"orcid":"0000-0002-7673-7178","full_name":"Novarino, Gaia","last_name":"Novarino","first_name":"Gaia","id":"3E57A680-F248-11E8-B48F-1D18A9856A87"}],"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"oa":1,"year":"2026","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).","publication_identifier":{"eissn":["1476-4687"],"issn":["0028-0836"]},"article_processing_charge":"Yes (via OA deal)","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1038/s41586-026-10679-1"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_published":"2026-06-17T00:00:00Z","_id":"22295","has_accepted_license":"1","quality_controlled":"1","type":"journal_article","month":"06","publication_status":"epub_ahead","oa_version":"Published Version","date_updated":"2026-07-13T12:58:19Z","title":"Cortical development dynamics across autism spectrum disorder mouse models","doi":"10.1038/s41586-026-10679-1","publication":"Nature","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).","external_id":{"pmid":["42310454"]},"citation":{"ieee":"L. A. Schwarz <i>et al.</i>, “Cortical development dynamics across autism spectrum disorder mouse models,” <i>Nature</i>. Springer Nature, 2026.","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.” <i>Nature</i>. Springer Nature, 2026. <a href=\"https://doi.org/10.1038/s41586-026-10679-1\">https://doi.org/10.1038/s41586-026-10679-1</a>.","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).","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.","mla":"Schwarz, Lena A., et al. “Cortical Development Dynamics across Autism Spectrum Disorder Mouse Models.” <i>Nature</i>, Springer Nature, 2026, doi:<a href=\"https://doi.org/10.1038/s41586-026-10679-1\">10.1038/s41586-026-10679-1</a>.","ama":"Schwarz LA, Dotter C, Isaev S, et al. Cortical development dynamics across autism spectrum disorder mouse models. <i>Nature</i>. 2026. doi:<a href=\"https://doi.org/10.1038/s41586-026-10679-1\">10.1038/s41586-026-10679-1</a>","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. <i>Nature</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41586-026-10679-1\">https://doi.org/10.1038/s41586-026-10679-1</a>"},"department":[{"_id":"AnKi"},{"_id":"GaNo"},{"_id":"TiVo"},{"_id":"ScienComp"},{"_id":"GradSch"},{"_id":"Bio"},{"_id":"PreCl"}],"project":[{"grant_number":"101044865","name":"Toward an understanding of the brain interstitial system and the extracellular proteome in health and autism spectrum disorders","_id":"34ba8964-11ca-11ed-8bc3-e15864e7e9a6"},{"_id":"9B91375C-BA93-11EA-9121-9846C619BF3A","name":"Critical windows and reversibility of ASD associated with mutations in chromatin remodelers","grant_number":"707964"},{"_id":"2548AE96-B435-11E9-9278-68D0E5697425","name":"Molecular Drug Targets","call_identifier":"FWF","grant_number":"W1232"},{"_id":"ebb38b5d-77a9-11ec-83b8-a42e08120a88","grant_number":"FG1803 49015","name":"Neurobiology of anxiety in autism spectrum disorders"}],"OA_place":"publisher","researchdata_availability":"yes","corr_author":"1","pmid":1,"scopus_import":"1","ddc":["570"],"language":[{"iso":"eng"}],"day":"17","article_type":"original","publisher":"Springer Nature","supplementarymaterial":"yes","PlanS_conform":"1","date_created":"2026-07-13T09:47:21Z","acknowledged_ssus":[{"_id":"Bio"},{"_id":"LifeSc"}],"status":"public","OA_type":"hybrid"},{"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","has_accepted_license":"1","_id":"15169","date_published":"2024-03-14T00:00:00Z","type":"journal_article","quality_controlled":"1","oa_version":"Published Version","publication_status":"published","month":"03","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"author":[{"orcid":"0000-0003-4252-1608","full_name":"Chintaluri, Chaitanya","first_name":"Chaitanya","last_name":"Chintaluri","id":"BA06AFEE-A4BA-11EA-AE5C-14673DDC885E"},{"full_name":"Bejtka, Marta","last_name":"Bejtka","first_name":"Marta"},{"full_name":"Sredniawa, Wladyslaw","last_name":"Sredniawa","first_name":"Wladyslaw"},{"full_name":"Czerwinski, Michal","first_name":"Michal","last_name":"Czerwinski"},{"full_name":"Dzik, Jakub M.","first_name":"Jakub M.","last_name":"Dzik"},{"full_name":"Jedrzejewska-Szmek, Joanna","first_name":"Joanna","last_name":"Jedrzejewska-Szmek"},{"first_name":"Daniel K.","last_name":"Wojciki","full_name":"Wojciki, Daniel K."}],"abstract":[{"text":"Interpretation of extracellular recordings can be challenging due to the long range of electric field. This challenge can be mitigated by estimating the current source density (CSD). Here we introduce kCSD-python, an open Python package implementing Kernel Current Source Density (kCSD) method and related tools to facilitate CSD analysis of experimental data and the interpretation of results. We show how to counter the limitations imposed by noise and assumptions in the method itself. kCSD-python allows CSD estimation for an arbitrary distribution of electrodes in 1D, 2D, and 3D, assuming distributions of sources in tissue, a slice, or in a single cell, and includes a range of diagnostic aids. We demonstrate its features in a Jupyter Notebook tutorial which illustrates a typical analytical workflow and main functionalities useful in validating analysis results.","lang":"eng"}],"issue":"3","acknowledgement":"The Python implementation of kCSD was started by Grzegorz Parka during Google Summer of Code project through the International Neuroinformatics Coordinating Facility. Jan Mąka implemented the first Python version of skCSD class. This work was supported by the Polish National Science Centre (2013/08/W/NZ4/00691 to DKW; 2015/17/B/ST7/04123 to DKW). ","year":"2024","oa":1,"isi":1,"publication_identifier":{"eissn":["1553-7358"],"issn":["1553-734X"]},"article_processing_charge":"Yes","scopus_import":"1","pmid":1,"article_type":"original","related_material":{"link":[{"relation":"software","url":"https://github.com/Neuroinflab/kCSD-python"}]},"day":"14","article_number":"e1011941","language":[{"iso":"eng"}],"ddc":["000","570"],"publisher":"Public Library of Science","volume":20,"OA_type":"gold","status":"public","date_created":"2024-03-24T23:00:59Z","doi":"10.1371/journal.pcbi.1011941","title":"kCSD-python, reliable current source density estimation with quality control","date_updated":"2026-07-13T12:30:33Z","intvolume":"        20","das_tickbox":"1","external_id":{"isi":["001190689800001"],"pmid":["38484020"]},"file_date_updated":"2025-06-25T05:47:36Z","DOAJ_listed":"1","publication":"PLoS Computational Biology","department":[{"_id":"TiVo"}],"citation":{"ama":"Chintaluri C, Bejtka M, Sredniawa W, et al. kCSD-python, reliable current source density estimation with quality control. <i>PLoS Computational Biology</i>. 2024;20(3). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1011941\">10.1371/journal.pcbi.1011941</a>","mla":"Chintaluri, Chaitanya, et al. “KCSD-Python, Reliable Current Source Density Estimation with Quality Control.” <i>PLoS Computational Biology</i>, vol. 20, no. 3, e1011941, Public Library of Science, 2024, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1011941\">10.1371/journal.pcbi.1011941</a>.","apa":"Chintaluri, C., Bejtka, M., Sredniawa, W., Czerwinski, M., Dzik, J. M., Jedrzejewska-Szmek, J., &#38; Wojciki, D. K. (2024). kCSD-python, reliable current source density estimation with quality control. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1011941\">https://doi.org/10.1371/journal.pcbi.1011941</a>","short":"C. Chintaluri, M. Bejtka, W. Sredniawa, M. Czerwinski, J.M. Dzik, J. Jedrzejewska-Szmek, D.K. Wojciki, PLoS Computational Biology 20 (2024).","chicago":"Chintaluri, Chaitanya, Marta Bejtka, Wladyslaw Sredniawa, Michal Czerwinski, Jakub M. Dzik, Joanna Jedrzejewska-Szmek, and Daniel K. Wojciki. “KCSD-Python, Reliable Current Source Density Estimation with Quality Control.” <i>PLoS Computational Biology</i>. Public Library of Science, 2024. <a href=\"https://doi.org/10.1371/journal.pcbi.1011941\">https://doi.org/10.1371/journal.pcbi.1011941</a>.","ista":"Chintaluri C, Bejtka M, Sredniawa W, Czerwinski M, Dzik JM, Jedrzejewska-Szmek J, Wojciki DK. 2024. kCSD-python, reliable current source density estimation with quality control. PLoS Computational Biology. 20(3), e1011941.","ieee":"C. Chintaluri <i>et al.</i>, “kCSD-python, reliable current source density estimation with quality control,” <i>PLoS Computational Biology</i>, vol. 20, no. 3. Public Library of Science, 2024."},"corr_author":"1","OA_place":"publisher","file":[{"creator":"dernst","success":1,"access_level":"open_access","content_type":"application/pdf","date_updated":"2025-06-25T05:47:36Z","checksum":"c09718d0d09614642d877d0716ce32e8","relation":"main_file","date_created":"2025-06-25T05:47:36Z","file_id":"19897","file_size":2540277,"file_name":"2024_PLoSCompBio_Chintaluri.pdf"}]},{"volume":120,"publisher":"National Academy of Sciences","date_created":"2023-12-10T23:01:00Z","OA_type":"hybrid","status":"public","pmid":1,"scopus_import":"1","related_material":{"link":[{"relation":"software","url":"https://github.com/ccluri/metabolic_spiking"}]},"day":"21","article_number":"e2306525120","language":[{"iso":"eng"}],"ddc":["570"],"article_type":"original","citation":{"apa":"Chintaluri, C., &#38; Vogels, T. P. (2023). Metabolically regulated spiking could serve neuronal energy homeostasis and protect from reactive oxygen species. <i>Proceedings of the National Academy of Sciences of the United States of America</i>. National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.2306525120\">https://doi.org/10.1073/pnas.2306525120</a>","mla":"Chintaluri, Chaitanya, and Tim P. Vogels. “Metabolically Regulated Spiking Could Serve Neuronal Energy Homeostasis and Protect from Reactive Oxygen Species.” <i>Proceedings of the National Academy of Sciences of the United States of America</i>, vol. 120, no. 48, e2306525120, National Academy of Sciences, 2023, doi:<a href=\"https://doi.org/10.1073/pnas.2306525120\">10.1073/pnas.2306525120</a>.","ama":"Chintaluri C, Vogels TP. Metabolically regulated spiking could serve neuronal energy homeostasis and protect from reactive oxygen species. <i>Proceedings of the National Academy of Sciences of the United States of America</i>. 2023;120(48). doi:<a href=\"https://doi.org/10.1073/pnas.2306525120\">10.1073/pnas.2306525120</a>","ieee":"C. Chintaluri and T. P. Vogels, “Metabolically regulated spiking could serve neuronal energy homeostasis and protect from reactive oxygen species,” <i>Proceedings of the National Academy of Sciences of the United States of America</i>, vol. 120, no. 48. National Academy of Sciences, 2023.","short":"C. Chintaluri, T.P. Vogels, Proceedings of the National Academy of Sciences of the United States of America 120 (2023).","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.","chicago":"Chintaluri, Chaitanya, and Tim P Vogels. “Metabolically Regulated Spiking Could Serve Neuronal Energy Homeostasis and Protect from Reactive Oxygen Species.” <i>Proceedings of the National Academy of Sciences of the United States of America</i>. National Academy of Sciences, 2023. <a href=\"https://doi.org/10.1073/pnas.2306525120\">https://doi.org/10.1073/pnas.2306525120</a>."},"project":[{"_id":"c084a126-5a5b-11eb-8a69-d75314a70a87","grant_number":"214316/Z/18/Z","name":"What’s in a memory? Spatiotemporal dynamics in strongly coupled recurrent neuronal networks."}],"department":[{"_id":"TiVo"}],"OA_place":"publisher","file":[{"date_updated":"2023-12-11T12:45:12Z","content_type":"application/pdf","creator":"dernst","success":1,"access_level":"open_access","file_name":"2023_PNAS_Chintaluri.pdf","file_size":16891602,"file_id":"14678","checksum":"bf4ec38602a70dae4338077a5a4d497f","date_created":"2023-12-11T12:45:12Z","relation":"main_file"}],"corr_author":"1","doi":"10.1073/pnas.2306525120","date_updated":"2026-07-13T12:30:49Z","title":"Metabolically regulated spiking could serve neuronal energy homeostasis and protect from reactive oxygen species","file_date_updated":"2023-12-11T12:45:12Z","publication":"Proceedings of the National Academy of Sciences of the United States of America","intvolume":"       120","das_tickbox":"1","external_id":{"isi":["001157389000005"],"pmid":["37988463"]},"quality_controlled":"1","type":"journal_article","oa_version":"Published Version","publication_status":"published","month":"11","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","_id":"14666","has_accepted_license":"1","date_published":"2023-11-21T00:00:00Z","isi":1,"publication_identifier":{"issn":["0027-8424"],"eissn":["1091-6490"]},"article_processing_charge":"Yes (in subscription journal)","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."}],"issue":"48","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"author":[{"orcid":"0000-0003-4252-1608","full_name":"Chintaluri, Chaitanya","id":"BA06AFEE-A4BA-11EA-AE5C-14673DDC885E","last_name":"Chintaluri","first_name":"Chaitanya"},{"orcid":"0000-0003-3295-6181","full_name":"Vogels, Tim P","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","last_name":"Vogels","first_name":"Tim P"}],"oa":1,"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).","year":"2023"},{"citation":{"ieee":"C. Chintaluri <i>et al.</i>, “What we can and what we cannot see with extracellular multielectrodes,” <i>PLOS Computational Biology</i>, vol. 17, no. 5. Public Library of Science, 2021.","chicago":"Chintaluri, Chaitanya, Marta Bejtka, Władysław Średniawa, Michał Czerwiński, Jakub M. Dzik, Joanna Jędrzejewska-Szmek, Kacper Kondrakiewicz, Ewa Kublik, and Daniel K. Wójcik. “What We Can and What We Cannot See with Extracellular Multielectrodes.” <i>PLOS Computational Biology</i>. Public Library of Science, 2021. <a href=\"https://doi.org/10.1371/journal.pcbi.1008615\">https://doi.org/10.1371/journal.pcbi.1008615</a>.","short":"C. Chintaluri, M. Bejtka, W. Średniawa, M. Czerwiński, J.M. Dzik, J. Jędrzejewska-Szmek, K. Kondrakiewicz, E. Kublik, D.K. Wójcik, PLOS Computational Biology 17 (2021).","ista":"Chintaluri C, Bejtka M, Średniawa W, Czerwiński M, Dzik JM, Jędrzejewska-Szmek J, Kondrakiewicz K, Kublik E, Wójcik DK. 2021. What we can and what we cannot see with extracellular multielectrodes. PLOS Computational Biology. 17(5), e1008615.","mla":"Chintaluri, Chaitanya, et al. “What We Can and What We Cannot See with Extracellular Multielectrodes.” <i>PLOS Computational Biology</i>, vol. 17, no. 5, e1008615, Public Library of Science, 2021, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1008615\">10.1371/journal.pcbi.1008615</a>.","ama":"Chintaluri C, Bejtka M, Średniawa W, et al. What we can and what we cannot see with extracellular multielectrodes. <i>PLOS Computational Biology</i>. 2021;17(5). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1008615\">10.1371/journal.pcbi.1008615</a>","apa":"Chintaluri, C., Bejtka, M., Średniawa, W., Czerwiński, M., Dzik, J. M., Jędrzejewska-Szmek, J., … Wójcik, D. K. (2021). What we can and what we cannot see with extracellular multielectrodes. <i>PLOS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1008615\">https://doi.org/10.1371/journal.pcbi.1008615</a>"},"intvolume":"        17","das_tickbox":"1","publication":"PLOS Computational Biology","doi":"10.1371/journal.pcbi.1008615","title":"What we can and what we cannot see with extracellular multielectrodes","date_updated":"2026-07-13T12:31:04Z","status":"public","date_created":"2024-06-11T14:43:37Z","extern":"1","publisher":"Public Library of Science","volume":17,"article_type":"original","language":[{"iso":"eng"}],"article_number":"e1008615","day":"14","main_file_link":[{"url":"https://doi.org/10.1371/journal.pcbi.1008615","open_access":"1"}],"article_processing_charge":"No","publication_identifier":{"issn":["1553-7358"]},"year":"2021","oa":1,"author":[{"id":"BA06AFEE-A4BA-11EA-AE5C-14673DDC885E","last_name":"Chintaluri","first_name":"Chaitanya","orcid":"0000-0003-4252-1608","full_name":"Chintaluri, Chaitanya"},{"full_name":"Bejtka, Marta","last_name":"Bejtka","first_name":"Marta"},{"last_name":"Średniawa","first_name":"Władysław","full_name":"Średniawa, Władysław"},{"last_name":"Czerwiński","first_name":"Michał","full_name":"Czerwiński, Michał"},{"last_name":"Dzik","first_name":"Jakub M.","full_name":"Dzik, Jakub M."},{"first_name":"Joanna","last_name":"Jędrzejewska-Szmek","full_name":"Jędrzejewska-Szmek, Joanna"},{"first_name":"Kacper","last_name":"Kondrakiewicz","full_name":"Kondrakiewicz, Kacper"},{"first_name":"Ewa","last_name":"Kublik","full_name":"Kublik, Ewa"},{"full_name":"Wójcik, Daniel K.","last_name":"Wójcik","first_name":"Daniel K."}],"abstract":[{"lang":"eng","text":"<jats:p>Extracellular recording is an accessible technique used in animals and humans to study the brain physiology and pathology. As the number of recording channels and their density grows it is natural to ask how much improvement the additional channels bring in and how we can optimally use the new capabilities for monitoring the brain. Here we show that for any given distribution of electrodes we can establish exactly what information about current sources in the brain can be recovered and what information is strictly unobservable. We demonstrate this in the general setting of previously proposed kernel Current Source Density method and illustrate it with simplified examples as well as using evoked potentials from the barrel cortex obtained with a Neuropixels probe and with compatible model data. We show that with conceptual separation of the estimation space from experimental setup one can recover sources not accessible to standard methods.</jats:p>"}],"issue":"5","oa_version":"Published Version","publication_status":"published","month":"05","type":"journal_article","quality_controlled":"1","has_accepted_license":"1","_id":"17132","date_published":"2021-05-14T00:00:00Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87"},{"year":"2020","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.","oa":1,"author":[{"first_name":"Pedro J.","last_name":"Gonçalves","orcid":"0000-0002-6987-4836","full_name":"Gonçalves, Pedro J."},{"first_name":"Jan-Matthis","last_name":"Lueckmann","full_name":"Lueckmann, Jan-Matthis","orcid":"0000-0003-4320-4663"},{"full_name":"Deistler, Michael","orcid":"0000-0002-3573-0404","last_name":"Deistler","first_name":"Michael"},{"full_name":"Nonnenmacher, Marcel","orcid":"0000-0001-6044-6627","last_name":"Nonnenmacher","first_name":"Marcel"},{"first_name":"Kaan","last_name":"Öcal","orcid":"0000-0002-8528-6858","full_name":"Öcal, Kaan"},{"full_name":"Bassetto, Giacomo","first_name":"Giacomo","last_name":"Bassetto"},{"id":"BA06AFEE-A4BA-11EA-AE5C-14673DDC885E","first_name":"Chaitanya","last_name":"Chintaluri","orcid":"0000-0003-4252-1608","full_name":"Chintaluri, Chaitanya"},{"full_name":"Podlaski, William F.","orcid":"0000-0001-6619-7502","last_name":"Podlaski","first_name":"William F."},{"last_name":"Haddad","first_name":"Sara A.","full_name":"Haddad, Sara A.","orcid":"0000-0003-0807-0823"},{"id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","first_name":"Tim P","last_name":"Vogels","full_name":"Vogels, Tim P","orcid":"0000-0003-3295-6181"},{"full_name":"Greenberg, David S.","first_name":"David S.","last_name":"Greenberg"},{"orcid":"0000-0001-5154-8912","full_name":"Macke, Jakob H.","last_name":"Macke","first_name":"Jakob H."}],"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"abstract":[{"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.","lang":"eng"}],"article_processing_charge":"No","publication_identifier":{"eissn":["2050-084X"]},"isi":1,"date_published":"2020-09-17T00:00:00Z","has_accepted_license":"1","_id":"8127","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication_status":"published","month":"09","oa_version":"Published Version","type":"journal_article","quality_controlled":"1","das_tickbox":"1","external_id":{"pmid":["32940606"],"isi":["000584989400001"]},"intvolume":"         9","publication":"eLife","file_date_updated":"2020-10-27T11:37:32Z","title":"Training deep neural density estimators to identify mechanistic models of neural dynamics","date_updated":"2026-07-13T12:31:21Z","doi":"10.7554/eLife.56261","file":[{"content_type":"application/pdf","creator":"cziletti","access_level":"open_access","success":1,"date_updated":"2020-10-27T11:37:32Z","date_created":"2020-10-27T11:37:32Z","relation":"main_file","checksum":"c4300ddcd93ed03fc9c6cdf1f77890be","file_name":"2020_eLife_Gonçalves.pdf","file_id":"8709","file_size":17355867}],"department":[{"_id":"TiVo"}],"ec_funded":1,"project":[{"name":"Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning.","call_identifier":"H2020","grant_number":"819603","_id":"0aacfa84-070f-11eb-9043-d7eb2c709234"}],"citation":{"ieee":"P. J. Gonçalves <i>et al.</i>, “Training deep neural density estimators to identify mechanistic models of neural dynamics,” <i>eLife</i>, vol. 9. eLife Sciences Publications, 2020.","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.","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).","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.” <i>ELife</i>. eLife Sciences Publications, 2020. <a href=\"https://doi.org/10.7554/eLife.56261\">https://doi.org/10.7554/eLife.56261</a>.","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. <i>ELife</i>. eLife Sciences Publications. <a href=\"https://doi.org/10.7554/eLife.56261\">https://doi.org/10.7554/eLife.56261</a>","mla":"Gonçalves, Pedro J., et al. “Training Deep Neural Density Estimators to Identify Mechanistic Models of Neural Dynamics.” <i>ELife</i>, vol. 9, e56261, eLife Sciences Publications, 2020, doi:<a href=\"https://doi.org/10.7554/eLife.56261\">10.7554/eLife.56261</a>.","ama":"Gonçalves PJ, Lueckmann J-M, Deistler M, et al. Training deep neural density estimators to identify mechanistic models of neural dynamics. <i>eLife</i>. 2020;9. doi:<a href=\"https://doi.org/10.7554/eLife.56261\">10.7554/eLife.56261</a>"},"article_type":"original","ddc":["570"],"article_number":"e56261","day":"17","language":[{"iso":"eng"}],"scopus_import":"1","pmid":1,"status":"public","date_created":"2020-07-16T12:26:04Z","publisher":"eLife Sciences Publications","volume":9}]