[{"project":[{"grant_number":"819603","name":"Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning.","call_identifier":"H2020","_id":"0aacfa84-070f-11eb-9043-d7eb2c709234"}],"article_number":"e2307776121","author":[{"last_name":"Clatot","full_name":"Clatot, Jerome","first_name":"Jerome"},{"id":"e8321fc5-3091-11eb-8a53-83f309a11ac9","first_name":"Christopher","last_name":"Currin","full_name":"Currin, Christopher","orcid":"0000-0002-4809-5059"},{"full_name":"Liang, Qiansheng","last_name":"Liang","first_name":"Qiansheng"},{"first_name":"Tanadet","full_name":"Pipatpolkai, Tanadet","last_name":"Pipatpolkai"},{"last_name":"Massey","full_name":"Massey, Shavonne L.","first_name":"Shavonne L."},{"last_name":"Helbig","full_name":"Helbig, Ingo","first_name":"Ingo"},{"last_name":"Delemotte","full_name":"Delemotte, Lucie","first_name":"Lucie"},{"last_name":"Vogels","full_name":"Vogels, Tim P","orcid":"0000-0003-3295-6181","first_name":"Tim P","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425"},{"first_name":"Manuel","full_name":"Covarrubias, Manuel","last_name":"Covarrubias"},{"last_name":"Goldberg","full_name":"Goldberg, Ethan M.","first_name":"Ethan M."}],"external_id":{"pmid":["38194456"]},"article_processing_charge":"No","title":"A structurally precise mechanism links an epilepsy-associated KCNC2 potassium channel mutation to interneuron dysfunction","citation":{"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.","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. Proceedings of the National Academy of Sciences, 2024. https://doi.org/10.1073/pnas.2307776121.","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","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. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.2307776121","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).","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. Proceedings of the National Academy of Sciences, 2024.","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, Proceedings of the National Academy of Sciences, 2024, doi:10.1073/pnas.2307776121."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","quality_controlled":"1","publisher":"Proceedings of the National Academy of Sciences","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.","doi":"10.1073/pnas.2307776121","date_published":"2024-01-16T00:00:00Z","date_created":"2024-01-21T23:00:56Z","year":"2024","day":"16","publication":"Proceedings of the National Academy of Sciences of the United States of America","type":"journal_article","article_type":"original","status":"public","_id":"14841","department":[{"_id":"TiVo"}],"date_updated":"2024-01-23T10:20:40Z","scopus_import":"1","month":"01","intvolume":" 121","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"}],"pmid":1,"oa_version":"None","volume":121,"issue":"3","related_material":{"link":[{"url":"https://github.com/ChrisCurrin/pv-kcnc2 ","relation":"software"}]},"ec_funded":1,"publication_identifier":{"eissn":["1091-6490"]},"publication_status":"published","language":[{"iso":"eng"}]},{"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.","quality_controlled":"1","publisher":"Springer Nature","oa":1,"year":"2024","day":"20","publication":"Nature Neuroscience","doi":"10.1038/s41593-024-01597-4","date_published":"2024-03-20T00:00:00Z","date_created":"2024-03-24T23:01:00Z","project":[{"call_identifier":"H2020","_id":"0aacfa84-070f-11eb-9043-d7eb2c709234","grant_number":"819603","name":"Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning."}],"citation":{"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","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. doi:10.1038/s41593-024-01597-4","short":"E.J. Agnes, T.P. Vogels, Nature Neuroscience (2024).","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. Springer Nature, 2024.","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, Springer Nature, 2024, doi: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.","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."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"last_name":"Agnes","full_name":"Agnes, Everton J.","first_name":"Everton J."},{"last_name":"Vogels","full_name":"Vogels, Tim P","orcid":"0000-0003-3295-6181","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","first_name":"Tim P"}],"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","abstract":[{"lang":"eng","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."}],"oa_version":"Published Version","scopus_import":"1","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1038/s41593-024-01597-4"}],"month":"03","publication_identifier":{"issn":["1097-6256"],"eissn":["1546-1726"]},"publication_status":"epub_ahead","language":[{"iso":"eng"}],"ec_funded":1,"_id":"15171","article_type":"original","type":"journal_article","status":"public","date_updated":"2024-03-25T07:04:05Z","department":[{"_id":"TiVo"}]},{"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"type":"journal_article","article_type":"original","status":"public","_id":"14666","file_date_updated":"2023-12-11T12:45:12Z","department":[{"_id":"TiVo"}],"date_updated":"2023-12-11T12:47:41Z","ddc":["570"],"scopus_import":"1","intvolume":" 120","month":"11","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."}],"pmid":1,"oa_version":"None","related_material":{"link":[{"url":"https://github.com/ccluri/metabolic_spiking","relation":"software"}]},"volume":120,"issue":"48","publication_status":"published","publication_identifier":{"issn":["0027-8424"],"eissn":["1091-6490"]},"language":[{"iso":"eng"}],"file":[{"file_name":"2023_PNAS_Chintaluri.pdf","date_created":"2023-12-11T12:45:12Z","creator":"dernst","file_size":16891602,"date_updated":"2023-12-11T12:45:12Z","success":1,"file_id":"14678","checksum":"bf4ec38602a70dae4338077a5a4d497f","relation":"main_file","access_level":"open_access","content_type":"application/pdf"}],"project":[{"grant_number":"214316/Z/18/Z","name":"What’s in a memory? Spatiotemporal dynamics in strongly coupled recurrent neuronal networks.","_id":"c084a126-5a5b-11eb-8a69-d75314a70a87"}],"article_number":"e2306525120","external_id":{"pmid":["37988463"]},"article_processing_charge":"Yes (in subscription journal)","author":[{"first_name":"Chaitanya","id":"E4EDB536-3485-11EA-98D2-20AF3DDC885E","full_name":"Chintaluri, Chaitanya","last_name":"Chintaluri"},{"orcid":"0000-0003-3295-6181","full_name":"Vogels, Tim P","last_name":"Vogels","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","first_name":"Tim P"}],"title":"Metabolically regulated spiking could serve neuronal energy homeostasis and protect from reactive oxygen species","citation":{"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","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.","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."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"quality_controlled":"1","publisher":"National Academy of Sciences","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).","date_created":"2023-12-10T23:01:00Z","doi":"10.1073/pnas.2306525120","date_published":"2023-11-21T00:00:00Z","year":"2023","has_accepted_license":"1","publication":"Proceedings of the National Academy of Sciences of the United States of America","day":"21"},{"status":"public","type":"conference","_id":"13239","department":[{"_id":"TiVo"}],"file_date_updated":"2023-07-18T06:32:38Z","ddc":["000"],"date_updated":"2023-07-18T06:36:28Z","intvolume":" 199","month":"12","scopus_import":"1","oa_version":"Published Version","abstract":[{"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.","lang":"eng"}],"ec_funded":1,"volume":199,"language":[{"iso":"eng"}],"file":[{"file_size":585135,"date_updated":"2023-07-18T06:32:38Z","creator":"dernst","file_name":"2022_PMLR_vanderPlas.pdf","date_created":"2023-07-18T06:32:38Z","content_type":"application/pdf","relation":"main_file","access_level":"open_access","success":1,"checksum":"7530a93ef42e10b4db1e5e4b69796e93","file_id":"13243"}],"publication_status":"published","publication_identifier":{"eissn":["2640-3498"]},"project":[{"_id":"0aacfa84-070f-11eb-9043-d7eb2c709234","call_identifier":"H2020","grant_number":"819603","name":"Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning."}],"title":"Predictive learning enables neural networks to learn complex working memory tasks","article_processing_charge":"No","author":[{"full_name":"Van Der Plas, Thijs L.","last_name":"Van Der Plas","first_name":"Thijs L."},{"full_name":"Vogels, Tim P","orcid":"0000-0003-3295-6181","last_name":"Vogels","first_name":"Tim P","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425"},{"first_name":"Sanjay G.","full_name":"Manohar, Sanjay G.","last_name":"Manohar"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"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.","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.","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.","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.","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.","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.","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."},"oa":1,"quality_controlled":"1","publisher":"ML Research Press","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.","date_created":"2023-07-16T22:01:12Z","date_published":"2022-12-01T00:00:00Z","page":"518-531","publication":"Proceedings of Machine Learning Research","day":"01","year":"2022","has_accepted_license":"1"},{"publication":"Cell Reports","day":"29","year":"2022","isi":1,"has_accepted_license":"1","date_created":"2022-04-10T22:01:39Z","date_published":"2022-03-29T00:00:00Z","doi":"10.1016/j.celrep.2022.110580","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).","oa":1,"publisher":"Elsevier","quality_controlled":"1","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","citation":{"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.","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.","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","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","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."},"title":"Developmentally regulated impairment of parvalbumin interneuron synaptic transmission in an experimental model of Dravet syndrome","external_id":{"isi":["000779794000001"]},"article_processing_charge":"No","author":[{"last_name":"Kaneko","full_name":"Kaneko, Keisuke","first_name":"Keisuke"},{"id":"e8321fc5-3091-11eb-8a53-83f309a11ac9","first_name":"Christopher","full_name":"Currin, Christopher","orcid":"0000-0002-4809-5059","last_name":"Currin"},{"full_name":"Goff, Kevin M.","last_name":"Goff","first_name":"Kevin M."},{"first_name":"Eric R.","last_name":"Wengert","full_name":"Wengert, Eric R."},{"first_name":"Ala","full_name":"Somarowthu, Ala","last_name":"Somarowthu"},{"first_name":"Tim P","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","orcid":"0000-0003-3295-6181","full_name":"Vogels, Tim P","last_name":"Vogels"},{"first_name":"Ethan M.","last_name":"Goldberg","full_name":"Goldberg, Ethan M."}],"article_number":"110580","project":[{"name":"Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning.","grant_number":"819603","_id":"0aacfa84-070f-11eb-9043-d7eb2c709234","call_identifier":"H2020"},{"_id":"9B861AAC-BA93-11EA-9121-9846C619BF3A","name":"NOMIS Fellowship Program"}],"language":[{"iso":"eng"}],"file":[{"content_type":"application/pdf","access_level":"open_access","relation":"main_file","checksum":"49105c6c27c9af0f37f50a8bbb4d380d","file_id":"11172","success":1,"date_updated":"2022-04-15T11:00:58Z","file_size":4774216,"creator":"dernst","date_created":"2022-04-15T11:00:58Z","file_name":"2022_CellReports_Kaneko.pdf"}],"publication_status":"published","publication_identifier":{"eissn":["2211-1247"]},"ec_funded":1,"volume":38,"issue":"13","oa_version":"Published Version","abstract":[{"lang":"eng","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."}],"intvolume":" 38","month":"03","scopus_import":"1","ddc":["570"],"date_updated":"2023-08-03T06:32:55Z","file_date_updated":"2022-04-15T11:00:58Z","department":[{"_id":"TiVo"}],"_id":"11143","status":"public","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"},"type":"journal_article","article_type":"original"},{"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).","oa":1,"quality_controlled":"1","publisher":"Springer Nature","year":"2022","isi":1,"has_accepted_license":"1","publication":"Communications biology","day":"25","date_created":"2022-09-04T22:02:02Z","doi":"10.1038/s42003-022-03801-2","date_published":"2022-08-25T00:00:00Z","article_number":"873","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."},{"call_identifier":"H2020","_id":"0aacfa84-070f-11eb-9043-d7eb2c709234","grant_number":"819603","name":"Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning."}],"citation":{"ista":"Jia DW, Vogels TP, Costa RP. 2022. Developmental depression-to-facilitation shift controls excitation-inhibition balance. Communications biology. 5, 873.","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.","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","short":"D.W. Jia, T.P. Vogels, R.P. Costa, Communications Biology 5 (2022).","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.","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."},"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","article_processing_charge":"No","external_id":{"isi":["000844814800007"]},"author":[{"full_name":"Jia, David W.","last_name":"Jia","first_name":"David W."},{"first_name":"Tim P","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","last_name":"Vogels","full_name":"Vogels, Tim P","orcid":"0000-0003-3295-6181"},{"full_name":"Costa, Rui Ponte","last_name":"Costa","first_name":"Rui Ponte"}],"title":"Developmental depression-to-facilitation shift controls excitation-inhibition balance","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"}],"oa_version":"Published Version","scopus_import":"1","intvolume":" 5","month":"08","publication_status":"published","publication_identifier":{"eissn":["2399-3642"]},"language":[{"iso":"eng"}],"file":[{"success":1,"file_id":"12022","checksum":"3ec724c4f6d3440028c217305e32915f","relation":"main_file","access_level":"open_access","content_type":"application/pdf","file_name":"2022_CommBiology_Jia.pdf","date_created":"2022-09-05T08:55:11Z","creator":"dernst","file_size":2491191,"date_updated":"2022-09-05T08:55:11Z"}],"ec_funded":1,"volume":5,"_id":"12009","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"type":"journal_article","article_type":"original","status":"public","date_updated":"2023-08-03T13:22:42Z","ddc":["570"],"department":[{"_id":"TiVo"}],"file_date_updated":"2022-09-05T08:55:11Z"},{"file_date_updated":"2022-09-12T07:47:55Z","department":[{"_id":"TiVo"}],"ddc":["570"],"date_updated":"2023-08-03T14:06:29Z","status":"public","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"article_type":"original","type":"journal_article","_id":"12084","volume":18,"issue":"8","language":[{"iso":"eng"}],"file":[{"file_id":"12090","checksum":"8a81ab29f837991ee0ea770817c4a50e","success":1,"access_level":"open_access","relation":"main_file","content_type":"application/pdf","date_created":"2022-09-12T07:47:55Z","file_name":"2022_PLoSCompBio_Christodoulou.pdf","creator":"dernst","date_updated":"2022-09-12T07:47:55Z","file_size":2867337}],"publication_status":"published","publication_identifier":{"eissn":["1553-7358"]},"intvolume":" 18","month":"08","scopus_import":"1","oa_version":"Published Version","abstract":[{"lang":"eng","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."}],"title":"Regimes and mechanisms of transient amplification in abstract and biological neural networks","article_processing_charge":"No","external_id":{"isi":["000937227700001"]},"author":[{"first_name":"Georgia","last_name":"Christodoulou","full_name":"Christodoulou, Georgia"},{"orcid":"0000-0003-3295-6181","full_name":"Vogels, Tim P","last_name":"Vogels","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","first_name":"Tim P"},{"full_name":"Agnes, Everton J.","last_name":"Agnes","first_name":"Everton J."}],"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","citation":{"short":"G. Christodoulou, T.P. Vogels, E.J. Agnes, PLoS Computational Biology 18 (2022).","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.","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","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","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.","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.","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."},"project":[{"name":"What’s in a memory? Spatiotemporal dynamics in strongly coupled recurrent neuronal networks.","grant_number":"214316/Z/18/Z","_id":"c084a126-5a5b-11eb-8a69-d75314a70a87"}],"article_number":"e1010365","date_created":"2022-09-11T22:01:56Z","date_published":"2022-08-15T00:00:00Z","doi":"10.1371/journal.pcbi.1010365","publication":"PLoS Computational Biology","day":"15","year":"2022","has_accepted_license":"1","isi":1,"oa":1,"publisher":"Public Library of Science","quality_controlled":"1","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). "},{"date_updated":"2023-10-03T10:53:17Z","department":[{"_id":"TiVo"}],"_id":"10753","status":"public","article_type":"letter_note","type":"journal_article","language":[{"iso":"eng"}],"publication_identifier":{"eissn":["1097-4199"]},"publication_status":"published","volume":110,"issue":"3","pmid":1,"oa_version":"Published Version","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"}],"month":"02","intvolume":" 110","scopus_import":"1","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1016/j.neuron.2022.01.014"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"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.","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","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.","short":"B.J. Confavreux, T.P. Vogels, Neuron 110 (2022) 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.","ista":"Confavreux BJ, Vogels TP. 2022. A familiar thought: Machines that replace us? Neuron. 110(3), 361–362."},"title":"A familiar thought: Machines that replace us?","author":[{"id":"C7610134-B532-11EA-BD9F-F5753DDC885E","first_name":"Basile J","full_name":"Confavreux, Basile J","last_name":"Confavreux"},{"last_name":"Vogels","full_name":"Vogels, Tim P","orcid":"0000-0003-3295-6181","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","first_name":"Tim P"}],"article_processing_charge":"No","external_id":{"pmid":["35114107"],"isi":["000751819100005"]},"day":"02","publication":"Neuron","isi":1,"year":"2022","date_published":"2022-02-02T00:00:00Z","doi":"10.1016/j.neuron.2022.01.014","date_created":"2022-02-13T23:01:34Z","page":"361-362","quality_controlled":"1","publisher":"Elsevier","oa":1},{"year":"2022","publication_status":"published","day":"21","language":[{"iso":"eng"}],"publication":"bioRxiv","date_published":"2022-12-21T00:00:00Z","doi":"10.1101/2020.01.08.898528","date_created":"2020-07-16T12:24:28Z","locked":"1","abstract":[{"lang":"eng","text":"Context, such as behavioral state, is known to modulate memory formation and retrieval, but is usually ignored in associative memory models. Here, we propose several types of contextual modulation for associative memory networks that greatly increase their performance. In these networks, context inactivates specific neurons and connections, which modulates the effective connectivity of the network. Memories are stored only by the active components, thereby reducing interference from memories acquired in other contexts. Such networks exhibit several beneficial characteristics, including enhanced memory capacity, high robustness to noise, increased robustness to memory overloading, and better memory retention during continual learning. Furthermore, memories can be biased to have different relative strengths, or even gated on or off, according to contextual cues, providing a candidate model for cognitive control of memory and efficient memory search. An external context-encoding network can dynamically switch the memory network to a desired state, which we liken to experimentally observed contextual signals in prefrontal cortex and hippocampus. Overall, our work illustrates the benefits of organizing memory around context, and provides an important link between behavioral studies of memory and mechanistic details of neural circuits.SIGNIFICANCEMemory is context dependent — both encoding and recall vary in effectiveness and speed depending on factors like location and brain state during a task. We apply this idea to a simple computational model of associative memory through contextual gating of neurons and synaptic connections. Intriguingly, this results in several advantages, including vastly enhanced memory capacity, better robustness, and flexible memory gating. Our model helps to explain (i) how gating and inhibition contribute to memory processes, (ii) how memory access dynamically changes over time, and (iii) how context representations, such as those observed in hippocampus and prefrontal cortex, may interact with and control memory processes."}],"oa_version":"Preprint","publisher":"Cold Spring Harbor Laboratory","oa":1,"main_file_link":[{"url":"https://doi.org/10.1101/2020.01.08.898528 ","open_access":"1"}],"month":"12","date_updated":"2024-03-06T12:03:59Z","citation":{"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.” BioRxiv. Cold Spring Harbor Laboratory, 2022. https://doi.org/10.1101/2020.01.08.898528.","ista":"Podlaski WF, Agnes EJ, Vogels TP. 2022. High capacity and dynamic accessibility in associative memory networks with context-dependent neuronal and synaptic gating. bioRxiv, 10.1101/2020.01.08.898528.","mla":"Podlaski, William F., et al. “High Capacity and Dynamic Accessibility in Associative Memory Networks with Context-Dependent Neuronal and Synaptic Gating.” BioRxiv, Cold Spring Harbor Laboratory, 2022, doi:10.1101/2020.01.08.898528.","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,” bioRxiv. Cold Spring Harbor Laboratory, 2022.","short":"W.F. Podlaski, E.J. Agnes, T.P. Vogels, BioRxiv (2022).","ama":"Podlaski WF, Agnes EJ, Vogels TP. High capacity and dynamic accessibility in associative memory networks with context-dependent neuronal and synaptic gating. bioRxiv. 2022. doi:10.1101/2020.01.08.898528","apa":"Podlaski, W. F., Agnes, E. J., & Vogels, T. P. (2022). High capacity and dynamic accessibility in associative memory networks with context-dependent neuronal and synaptic gating. bioRxiv. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2020.01.08.898528"},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"full_name":"Podlaski, William F.","orcid":"0000-0001-6619-7502","last_name":"Podlaski","first_name":"William F."},{"first_name":"Everton J.","orcid":"0000-0001-7184-7311","full_name":"Agnes, Everton J.","last_name":"Agnes"},{"first_name":"Tim P","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","last_name":"Vogels","orcid":"0000-0003-3295-6181","full_name":"Vogels, Tim P"}],"article_processing_charge":"No","department":[{"_id":"TiVo"}],"title":"High capacity and dynamic accessibility in associative memory networks with context-dependent neuronal and synaptic gating","_id":"8125","type":"preprint","status":"public"},{"volume":20,"language":[{"iso":"eng"}],"publication_identifier":{"issn":["1049-5258"],"isbn":["9781713845393"]},"publication_status":"published","month":"12","intvolume":" 20","scopus_import":"1","main_file_link":[{"url":"https://proceedings.neurips.cc/paper/2021/file/88e1ce84f9feef5a08d0df0334c53468-Paper.pdf","open_access":"1"}],"oa_version":"Published Version","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."}],"department":[{"_id":"TiVo"}],"date_updated":"2022-06-20T07:12:58Z","status":"public","type":"conference","conference":{"name":"NeurIPS: Neural Information Processing Systems","start_date":"2021-12-06","end_date":"2021-12-14","location":"Virtual, Online"},"_id":"11453","date_published":"2021-12-01T00:00:00Z","date_created":"2022-06-19T22:01:59Z","page":"16437-16450","day":"01","publication":"Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems","year":"2021","quality_controlled":"1","publisher":"Neural Information Processing Systems Foundation","oa":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].","title":"Online learning of neural computations from sparse temporal feedback","author":[{"first_name":"Lukas","full_name":"Braun, Lukas","last_name":"Braun"},{"id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","first_name":"Tim P","orcid":"0000-0003-3295-6181","full_name":"Vogels, Tim P","last_name":"Vogels"}],"article_processing_charge":"No","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"apa":"Braun, L., & Vogels, T. P. (2021). Online learning of neural computations from sparse temporal feedback. In Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems (Vol. 20, pp. 16437–16450). Virtual, Online: Neural Information Processing Systems Foundation.","ama":"Braun L, Vogels TP. Online learning of neural computations from sparse temporal feedback. In: Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems. Vol 20. Neural Information Processing Systems Foundation; 2021:16437-16450.","short":"L. Braun, T.P. Vogels, in:, Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2021, pp. 16437–16450.","ieee":"L. Braun and T. P. Vogels, “Online learning of neural computations from sparse temporal feedback,” in Advances in Neural Information Processing Systems - 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.” Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems, vol. 20, Neural Information Processing Systems Foundation, 2021, pp. 16437–50.","ista":"Braun L, Vogels TP. 2021. Online learning of neural computations from sparse temporal feedback. Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems. NeurIPS: 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 Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems, 20:16437–50. Neural Information Processing Systems Foundation, 2021."},"project":[{"name":"What’s in a memory? Spatiotemporal dynamics in strongly coupled recurrent neuronal networks.","grant_number":"214316/Z/18/Z","_id":"c084a126-5a5b-11eb-8a69-d75314a70a87"}]},{"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.","publisher":"MIT Press","quality_controlled":"1","oa":1,"day":"01","publication":"Neural Computation","isi":1,"has_accepted_license":"1","year":"2021","doi":"10.1162/neco_a_01367","date_published":"2021-03-01T00:00:00Z","date_created":"2020-08-12T12:08:24Z","page":"899-925","project":[{"_id":"0aacfa84-070f-11eb-9043-d7eb2c709234","call_identifier":"H2020","name":"Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning.","grant_number":"819603"},{"name":"What’s in a memory? Spatiotemporal dynamics in strongly coupled recurrent neuronal networks.","grant_number":"214316/Z/18/Z","_id":"c084a126-5a5b-11eb-8a69-d75314a70a87"}],"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","citation":{"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.","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.","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","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","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.","short":"F. Zenke, T.P. Vogels, Neural Computation 33 (2021) 899–925.","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."},"title":"The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks","author":[{"first_name":"Friedemann","orcid":"0000-0003-1883-644X","full_name":"Zenke, Friedemann","last_name":"Zenke"},{"last_name":"Vogels","orcid":"0000-0003-3295-6181","full_name":"Vogels, Tim P","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","first_name":"Tim P"}],"article_processing_charge":"No","external_id":{"pmid":["33513328"],"isi":["000663433900003"]},"oa_version":"Published Version","pmid":1,"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."}],"month":"03","intvolume":" 33","scopus_import":"1","file":[{"success":1,"file_id":"11131","checksum":"eac5a51c24c8989ae7cf9ae32ec3bc95","content_type":"application/pdf","relation":"main_file","access_level":"open_access","file_name":"2021_NeuralComputation_Zenke.pdf","date_created":"2022-04-08T06:05:39Z","file_size":1611614,"date_updated":"2022-04-08T06:05:39Z","creator":"dernst"}],"language":[{"iso":"eng"}],"publication_identifier":{"eissn":["1530-888X"],"issn":["0899-7667"]},"publication_status":"published","issue":"4","volume":33,"ec_funded":1,"_id":"8253","status":"public","type":"journal_article","article_type":"original","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"ddc":["000","570"],"date_updated":"2023-08-04T10:53:14Z","file_date_updated":"2022-04-08T06:05:39Z","department":[{"_id":"TiVo"}]},{"citation":{"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.","ista":"Bozelos P, Vogels TP. 2021. Talking science, online. Nature Reviews Neuroscience. 22(1), 1–2.","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.","apa":"Bozelos, P., & Vogels, T. P. (2021). Talking science, online. Nature Reviews Neuroscience. Springer Nature. https://doi.org/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","ieee":"P. Bozelos and T. P. Vogels, “Talking science, online,” Nature Reviews Neuroscience, vol. 22, no. 1. Springer Nature, pp. 1–2, 2021.","short":"P. Bozelos, T.P. Vogels, Nature Reviews Neuroscience 22 (2021) 1–2."},"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","article_processing_charge":"No","external_id":{"pmid":["33173190"],"isi":["000588256300001"]},"author":[{"full_name":"Bozelos, Panagiotis","last_name":"Bozelos","first_name":"Panagiotis","id":"52e9c652-2982-11eb-81d4-b43d94c63700"},{"first_name":"Tim P","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","last_name":"Vogels","orcid":"0000-0003-3295-6181","full_name":"Vogels, Tim P"}],"title":"Talking science, online","oa":1,"quality_controlled":"1","publisher":"Springer Nature","year":"2021","has_accepted_license":"1","isi":1,"publication":"Nature Reviews Neuroscience","day":"01","page":"1-2","date_created":"2020-11-15T23:01:18Z","date_published":"2021-01-01T00:00:00Z","doi":"10.1038/s41583-020-00408-6","_id":"8757","type":"journal_article","article_type":"letter_note","status":"public","date_updated":"2023-08-04T11:10:20Z","ddc":["570"],"department":[{"_id":"TiVo"}],"file_date_updated":"2021-02-04T10:34:22Z","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"}],"oa_version":"Published Version","pmid":1,"scopus_import":"1","intvolume":" 22","month":"01","publication_status":"published","publication_identifier":{"issn":["1471003X"],"eissn":["14710048"]},"language":[{"iso":"eng"}],"file":[{"access_level":"open_access","relation":"main_file","content_type":"application/pdf","file_id":"9088","checksum":"7985d7dff94c086e35b94a911d78d9ad","success":1,"creator":"dernst","date_updated":"2021-02-04T10:34:22Z","file_size":683634,"date_created":"2021-02-04T10:34:22Z","file_name":"2021_NatureNeuroScience_Bozelos.pdf"}],"volume":22,"issue":"1"},{"doi":"10.1016/j.tics.2021.01.007","date_published":"2021-04-01T00:00:00Z","date_created":"2021-03-07T23:01:25Z","page":"265-268","day":"01","publication":"Trends in Cognitive Sciences","isi":1,"has_accepted_license":"1","year":"2021","publisher":"Elsevier","quality_controlled":"1","oa":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.","title":"Towards democratizing and automating online conferences: Lessons from the Neuromatch Conferences","author":[{"full_name":"Achakulvisut, Titipat","last_name":"Achakulvisut","first_name":"Titipat"},{"full_name":"Ruangrong, Tulakan","last_name":"Ruangrong","first_name":"Tulakan"},{"first_name":"Patrick","full_name":"Mineault, Patrick","last_name":"Mineault"},{"orcid":"0000-0003-3295-6181","full_name":"Vogels, Tim P","last_name":"Vogels","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","first_name":"Tim P"},{"first_name":"Megan A.K.","full_name":"Peters, Megan A.K.","last_name":"Peters"},{"full_name":"Poirazi, Panayiota","last_name":"Poirazi","first_name":"Panayiota"},{"last_name":"Rozell","full_name":"Rozell, Christopher","first_name":"Christopher"},{"first_name":"Brad","last_name":"Wyble","full_name":"Wyble, Brad"},{"last_name":"Goodman","full_name":"Goodman, Dan F.M.","first_name":"Dan F.M."},{"last_name":"Kording","full_name":"Kording, Konrad Paul","first_name":"Konrad Paul"}],"external_id":{"pmid":["33608214"],"isi":["000627418000001"]},"article_processing_charge":"No","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","citation":{"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.","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.","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.","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.","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","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"},"issue":"4","volume":25,"file":[{"creator":"dernst","date_updated":"2022-05-27T07:31:24Z","file_size":380720,"date_created":"2022-05-27T07:31:24Z","file_name":"2021_TrendsCognitiveSciences_Achakulvisut.pdf","access_level":"open_access","relation":"main_file","content_type":"application/pdf","checksum":"87e39ea7bd266b976e8631b66979214d","file_id":"11415","success":1}],"language":[{"iso":"eng"}],"publication_identifier":{"eissn":["1879-307X"],"issn":["1364-6613"]},"publication_status":"published","month":"04","intvolume":" 25","scopus_import":"1","oa_version":"Submitted Version","pmid":1,"abstract":[{"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.","lang":"eng"}],"file_date_updated":"2022-05-27T07:31:24Z","department":[{"_id":"TiVo"}],"ddc":["570"],"date_updated":"2023-08-07T13:59:07Z","status":"public","article_type":"original","type":"journal_article","_id":"9228"},{"project":[{"name":"Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning.","grant_number":"819603","call_identifier":"H2020","_id":"0aacfa84-070f-11eb-9043-d7eb2c709234"}],"article_number":"e56261","article_processing_charge":"No","external_id":{"pmid":["32940606"],"isi":["000584989400001"]},"author":[{"first_name":"Pedro J.","orcid":"0000-0002-6987-4836","full_name":"Gonçalves, Pedro J.","last_name":"Gonçalves"},{"first_name":"Jan-Matthis","last_name":"Lueckmann","full_name":"Lueckmann, Jan-Matthis","orcid":"0000-0003-4320-4663"},{"last_name":"Deistler","full_name":"Deistler, Michael","orcid":"0000-0002-3573-0404","first_name":"Michael"},{"last_name":"Nonnenmacher","orcid":"0000-0001-6044-6627","full_name":"Nonnenmacher, Marcel","first_name":"Marcel"},{"first_name":"Kaan","orcid":"0000-0002-8528-6858","full_name":"Öcal, Kaan","last_name":"Öcal"},{"full_name":"Bassetto, Giacomo","last_name":"Bassetto","first_name":"Giacomo"},{"orcid":"0000-0003-4252-1608","full_name":"Chintaluri, Chaitanya","last_name":"Chintaluri","id":"BA06AFEE-A4BA-11EA-AE5C-14673DDC885E","first_name":"Chaitanya"},{"last_name":"Podlaski","full_name":"Podlaski, William F.","orcid":"0000-0001-6619-7502","first_name":"William F."},{"first_name":"Sara A.","last_name":"Haddad","orcid":"0000-0003-0807-0823","full_name":"Haddad, Sara A."},{"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.","last_name":"Greenberg","first_name":"David S."},{"orcid":"0000-0001-5154-8912","full_name":"Macke, Jakob H.","last_name":"Macke","first_name":"Jakob H."}],"title":"Training deep neural density estimators to identify mechanistic models of neural dynamics","citation":{"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.","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).","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.","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","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","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.","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."},"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","oa":1,"quality_controlled":"1","publisher":"eLife Sciences Publications","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.","date_created":"2020-07-16T12:26:04Z","doi":"10.7554/eLife.56261","date_published":"2020-09-17T00:00:00Z","year":"2020","has_accepted_license":"1","isi":1,"publication":"eLife","day":"17","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"type":"journal_article","article_type":"original","status":"public","_id":"8127","department":[{"_id":"TiVo"}],"file_date_updated":"2020-10-27T11:37:32Z","date_updated":"2023-08-22T07:54:52Z","ddc":["570"],"scopus_import":"1","intvolume":" 9","month":"09","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"}],"oa_version":"Published Version","pmid":1,"ec_funded":1,"volume":9,"publication_status":"published","publication_identifier":{"eissn":["2050-084X"]},"language":[{"iso":"eng"}],"file":[{"success":1,"file_id":"8709","checksum":"c4300ddcd93ed03fc9c6cdf1f77890be","content_type":"application/pdf","relation":"main_file","access_level":"open_access","file_name":"2020_eLife_Gonçalves.pdf","date_created":"2020-10-27T11:37:32Z","file_size":17355867,"date_updated":"2020-10-27T11:37:32Z","creator":"cziletti"}]},{"date_updated":"2023-08-22T07:54:26Z","ddc":["570"],"file_date_updated":"2020-12-28T08:31:47Z","department":[{"_id":"TiVo"}],"_id":"8126","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"article_type":"original","type":"journal_article","status":"public","publication_status":"published","publication_identifier":{"eissn":["1529-2401"]},"language":[{"iso":"eng"}],"file":[{"date_created":"2020-12-28T08:31:47Z","file_name":"2020_JourNeuroscience_Agnes.pdf","date_updated":"2020-12-28T08:31:47Z","file_size":2750920,"creator":"dernst","checksum":"7977e4dd6b89357d1a5cc88babac56da","file_id":"8977","success":1,"content_type":"application/pdf","access_level":"open_access","relation":"main_file"}],"issue":"50","volume":40,"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"}],"pmid":1,"oa_version":"Published Version","scopus_import":"1","intvolume":" 40","month":"12","citation":{"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.","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.","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.","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","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","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.","short":"E.J. Agnes, A.I. Luppi, T.P. Vogels, The Journal of Neuroscience 40 (2020) 9634–9649."},"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","article_processing_charge":"No","external_id":{"pmid":["33168622"],"isi":["000606706400009"]},"author":[{"last_name":"Agnes","full_name":"Agnes, Everton J.","orcid":"0000-0001-7184-7311","first_name":"Everton J."},{"first_name":"Andrea I.","last_name":"Luppi","full_name":"Luppi, Andrea I."},{"full_name":"Vogels, Tim P","orcid":"0000-0003-3295-6181","last_name":"Vogels","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","first_name":"Tim P"}],"title":"Complementary inhibitory weight profiles emerge from plasticity and allow attentional switching of receptive fields","year":"2020","has_accepted_license":"1","isi":1,"publication":"The Journal of Neuroscience","day":"09","page":"9634-9649","date_created":"2020-07-16T12:25:04Z","date_published":"2020-12-09T00:00:00Z","doi":"10.1523/JNEUROSCI.0276-20.2020","oa":1,"quality_controlled":"1","publisher":"Society for Neuroscience"},{"day":"06","publication":"Advances in Neural Information Processing Systems","year":"2020","date_published":"2020-12-06T00:00:00Z","date_created":"2021-07-04T22:01:27Z","page":"16398-16408","acknowledgement":"We would like to thank Chaitanya Chintaluri, Georgia Christodoulou, Bill Podlaski and Merima Šabanovic for useful discussions and comments. This work was supported by a Wellcome Trust ´ Senior Research Fellowship (214316/Z/18/Z), a BBSRC grant (BB/N019512/1), an ERC consolidator Grant (SYNAPSEEK), a Leverhulme Trust Project Grant (RPG-2016-446), and funding from École Polytechnique, Paris.","quality_controlled":"1","oa":1,"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","citation":{"ista":"Confavreux BJ, Zenke F, Agnes EJ, Lillicrap T, Vogels TP. 2020. A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network. Advances in Neural Information Processing Systems. NeurIPS: Conference on Neural Information Processing Systems vol. 33, 16398–16408.","chicago":"Confavreux, Basile J, Friedemann Zenke, Everton J. Agnes, Timothy Lillicrap, and Tim P Vogels. “A Meta-Learning Approach to (Re)Discover Plasticity Rules That Carve a Desired Function into a Neural Network.” In Advances in Neural Information Processing Systems, 33:16398–408, 2020.","short":"B.J. Confavreux, F. Zenke, E.J. Agnes, T. Lillicrap, T.P. Vogels, in:, Advances in Neural Information Processing Systems, 2020, pp. 16398–16408.","ieee":"B. J. Confavreux, F. Zenke, E. J. Agnes, T. Lillicrap, and T. P. Vogels, “A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network,” in Advances in Neural Information Processing Systems, Vancouver, Canada, 2020, vol. 33, pp. 16398–16408.","apa":"Confavreux, B. J., Zenke, F., Agnes, E. J., Lillicrap, T., & Vogels, T. P. (2020). A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network. In Advances in Neural Information Processing Systems (Vol. 33, pp. 16398–16408). Vancouver, Canada.","ama":"Confavreux BJ, Zenke F, Agnes EJ, Lillicrap T, Vogels TP. A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network. In: Advances in Neural Information Processing Systems. Vol 33. ; 2020:16398-16408.","mla":"Confavreux, Basile J., et al. “A Meta-Learning Approach to (Re)Discover Plasticity Rules That Carve a Desired Function into a Neural Network.” Advances in Neural Information Processing Systems, vol. 33, 2020, pp. 16398–408."},"title":"A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network","author":[{"full_name":"Confavreux, Basile J","last_name":"Confavreux","id":"C7610134-B532-11EA-BD9F-F5753DDC885E","first_name":"Basile J"},{"last_name":"Zenke","full_name":"Zenke, Friedemann","first_name":"Friedemann"},{"full_name":"Agnes, Everton J.","last_name":"Agnes","first_name":"Everton J."},{"first_name":"Timothy","full_name":"Lillicrap, Timothy","last_name":"Lillicrap"},{"first_name":"Tim P","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","last_name":"Vogels","orcid":"0000-0003-3295-6181","full_name":"Vogels, Tim P"}],"article_processing_charge":"No","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"},{"name":"Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning.","grant_number":"819603","call_identifier":"H2020","_id":"0aacfa84-070f-11eb-9043-d7eb2c709234"}],"language":[{"iso":"eng"}],"publication_identifier":{"issn":["1049-5258"]},"publication_status":"published","volume":33,"related_material":{"record":[{"relation":"dissertation_contains","id":"14422","status":"public"}],"link":[{"url":"https://doi.org/10.1101/2020.10.24.353409","relation":"is_continued_by"}]},"ec_funded":1,"oa_version":"Published Version","abstract":[{"lang":"eng","text":"The search for biologically faithful synaptic plasticity rules has resulted in a large body of models. They are usually inspired by – and fitted to – experimental data, but they rarely produce neural dynamics that serve complex functions. These failures suggest that current plasticity models are still under-constrained by existing data. Here, we present an alternative approach that uses meta-learning to discover plausible synaptic plasticity rules. Instead of experimental data, the rules are constrained by the functions they implement and the structure they are meant to produce. Briefly, we parameterize synaptic plasticity rules by a Volterra expansion and then use supervised learning methods (gradient descent or evolutionary strategies) to minimize a problem-dependent loss function that quantifies how effectively a candidate plasticity rule transforms an initially random network into one with the desired function. We first validate our approach by re-discovering previously described plasticity rules, starting at the single-neuron level and “Oja’s rule”, a simple Hebbian plasticity rule that captures the direction of most variability of inputs to a neuron (i.e., the first principal component). We expand the problem to the network level and ask the framework to find Oja’s rule together with an anti-Hebbian rule such that an initially random two-layer firing-rate network will recover several principal components of the input space after learning. Next, we move to networks of integrate-and-fire neurons with plastic inhibitory afferents. We train for rules that achieve a target firing rate by countering tuned excitation. Our algorithm discovers a specific subset of the manifold of rules that can solve this task. Our work is a proof of principle of an automated and unbiased approach to unveil synaptic plasticity rules that obey biological constraints and can solve complex functions."}],"month":"12","intvolume":" 33","scopus_import":"1","main_file_link":[{"url":"https://proceedings.neurips.cc/paper/2020/hash/bdbd5ebfde4934142c8a88e7a3796cd5-Abstract.html","open_access":"1"}],"date_updated":"2023-10-18T09:20:55Z","department":[{"_id":"TiVo"}],"_id":"9633","status":"public","type":"conference","conference":{"name":"NeurIPS: Conference on Neural Information Processing Systems","start_date":"2020-12-06","end_date":"2020-12-12","location":"Vancouver, Canada"}},{"intvolume":" 15","month":"07","pmid":1,"oa_version":"Published Version","volume":15,"issue":"7","language":[{"iso":"eng"}],"file":[{"creator":"cziletti","date_updated":"2020-07-14T12:48:08Z","file_size":773969,"date_created":"2020-07-02T12:22:57Z","file_name":"2019_PlosCompBio_Currin.pdf","access_level":"open_access","relation":"main_file","content_type":"application/pdf","checksum":"723bdfb6ee5c747cbbb32baf01d17fad","file_id":"8079"}],"publication_status":"published","publication_identifier":{"issn":["1553-7358"]},"status":"public","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"article_type":"original","type":"journal_article","_id":"8013","file_date_updated":"2020-07-14T12:48:08Z","ddc":["570"],"extern":"1","date_updated":"2021-01-12T08:16:31Z","oa":1,"quality_controlled":"1","publisher":"Public Library of Science","date_created":"2020-06-25T12:50:39Z","date_published":"2019-07-11T00:00:00Z","doi":"10.1371/journal.pcbi.1007049","publication":"PLOS Computational Biology","day":"11","year":"2019","has_accepted_license":"1","article_number":"e1007049","title":"Think: Theory for Africa","article_processing_charge":"No","external_id":{"pmid":["31295253"]},"author":[{"first_name":"Christopher B.","last_name":"Currin","full_name":"Currin, Christopher B."},{"last_name":"Khoza","full_name":"Khoza, Phumlani N.","first_name":"Phumlani N."},{"first_name":"Alexander D.","full_name":"Antrobus, Alexander D.","last_name":"Antrobus"},{"first_name":"Peter E.","full_name":"Latham, Peter E.","last_name":"Latham"},{"first_name":"Tim P","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","orcid":"0000-0003-3295-6181","full_name":"Vogels, Tim P","last_name":"Vogels"},{"first_name":"Joseph V.","full_name":"Raimondo, Joseph V.","last_name":"Raimondo"}],"user_id":"D865714E-FA4E-11E9-B85B-F5C5E5697425","citation":{"ista":"Currin CB, Khoza PN, Antrobus AD, Latham PE, Vogels TP, Raimondo JV. 2019. Think: Theory for Africa. PLOS Computational Biology. 15(7), e1007049.","chicago":"Currin, Christopher B., Phumlani N. Khoza, Alexander D. Antrobus, Peter E. Latham, Tim P Vogels, and Joseph V. Raimondo. “Think: Theory for Africa.” PLOS Computational Biology. Public Library of Science, 2019. https://doi.org/10.1371/journal.pcbi.1007049.","apa":"Currin, C. B., Khoza, P. N., Antrobus, A. D., Latham, P. E., Vogels, T. P., & Raimondo, J. V. (2019). Think: Theory for Africa. PLOS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1007049","ama":"Currin CB, Khoza PN, Antrobus AD, Latham PE, Vogels TP, Raimondo JV. Think: Theory for Africa. PLOS Computational Biology. 2019;15(7). doi:10.1371/journal.pcbi.1007049","short":"C.B. Currin, P.N. Khoza, A.D. Antrobus, P.E. Latham, T.P. Vogels, J.V. Raimondo, PLOS Computational Biology 15 (2019).","ieee":"C. B. Currin, P. N. Khoza, A. D. Antrobus, P. E. Latham, T. P. Vogels, and J. V. Raimondo, “Think: Theory for Africa,” PLOS Computational Biology, vol. 15, no. 7. Public Library of Science, 2019.","mla":"Currin, Christopher B., et al. “Think: Theory for Africa.” PLOS Computational Biology, vol. 15, no. 7, e1007049, Public Library of Science, 2019, doi:10.1371/journal.pcbi.1007049."}},{"_id":"8014","status":"public","type":"journal_article","article_type":"original","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"extern":"1","ddc":["570"],"date_updated":"2021-01-12T08:16:31Z","file_date_updated":"2020-07-14T12:48:08Z","pmid":1,"oa_version":"Published Version","abstract":[{"lang":"eng","text":"Working memory, the ability to keep recently accessed information available for immediate manipulation, has been proposed to rely on two mechanisms that appear difficult to reconcile: self-sustained neural firing, or the opposite—activity-silent synaptic traces. Here we review and contrast models of these two mechanisms, and then show that both phenomena can co-exist within a unified system in which neurons hold information in both activity and synapses. Rapid plasticity in flexibly-coding neurons allows features to be bound together into objects, with an important emergent property being the focus of attention. One memory item is held by persistent activity in an attended or “focused” state, and is thus remembered better than other items. Other, previously attended items can remain in memory but in the background, encoded in activity-silent synaptic traces. This dual functional architecture provides a unified common mechanism accounting for a diversity of perplexing attention and memory effects that have been hitherto difficult to explain in a single theoretical framework."}],"month":"06","intvolume":" 101","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1101/233007 "}],"file":[{"file_name":"2019_NeurosBiobehavRev_Manohar.pdf","date_created":"2020-07-02T13:17:52Z","file_size":1754418,"date_updated":"2020-07-14T12:48:08Z","creator":"cziletti","checksum":"7b972e3d6f7bb3122c8c5648f44e60ca","file_id":"8080","content_type":"application/pdf","relation":"main_file","access_level":"open_access"}],"language":[{"iso":"eng"}],"publication_identifier":{"issn":["0149-7634"]},"publication_status":"published","volume":101,"user_id":"D865714E-FA4E-11E9-B85B-F5C5E5697425","citation":{"mla":"Manohar, Sanjay G., et al. “Neural Mechanisms of Attending to Items in Working Memory.” Neuroscience and Biobehavioral Reviews, vol. 101, Elsevier , 2019, pp. 1–12, doi:10.1016/j.neubiorev.2019.03.017.","ieee":"S. G. Manohar, N. Zokaei, S. J. Fallon, T. P. Vogels, and M. Husain, “Neural mechanisms of attending to items in working memory,” Neuroscience and Biobehavioral Reviews, vol. 101. Elsevier , pp. 1–12, 2019.","short":"S.G. Manohar, N. Zokaei, S.J. Fallon, T.P. Vogels, M. Husain, Neuroscience and Biobehavioral Reviews 101 (2019) 1–12.","ama":"Manohar SG, Zokaei N, Fallon SJ, Vogels TP, Husain M. Neural mechanisms of attending to items in working memory. Neuroscience and Biobehavioral Reviews. 2019;101:1-12. doi:10.1016/j.neubiorev.2019.03.017","apa":"Manohar, S. G., Zokaei, N., Fallon, S. J., Vogels, T. P., & Husain, M. (2019). Neural mechanisms of attending to items in working memory. Neuroscience and Biobehavioral Reviews. Elsevier . https://doi.org/10.1016/j.neubiorev.2019.03.017","chicago":"Manohar, Sanjay G., Nahid Zokaei, Sean J. Fallon, Tim P Vogels, and Masud Husain. “Neural Mechanisms of Attending to Items in Working Memory.” Neuroscience and Biobehavioral Reviews. Elsevier , 2019. https://doi.org/10.1016/j.neubiorev.2019.03.017.","ista":"Manohar SG, Zokaei N, Fallon SJ, Vogels TP, Husain M. 2019. Neural mechanisms of attending to items in working memory. Neuroscience and Biobehavioral Reviews. 101, 1–12."},"title":"Neural mechanisms of attending to items in working memory","author":[{"full_name":"Manohar, Sanjay G.","last_name":"Manohar","first_name":"Sanjay G."},{"full_name":"Zokaei, Nahid","last_name":"Zokaei","first_name":"Nahid"},{"first_name":"Sean J.","last_name":"Fallon","full_name":"Fallon, Sean J."},{"full_name":"Vogels, Tim P","orcid":"0000-0003-3295-6181","last_name":"Vogels","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","first_name":"Tim P"},{"last_name":"Husain","full_name":"Husain, Masud","first_name":"Masud"}],"external_id":{"pmid":["30922977"]},"article_processing_charge":"No","quality_controlled":"1","publisher":"Elsevier ","oa":1,"day":"01","publication":"Neuroscience and Biobehavioral Reviews","has_accepted_license":"1","year":"2019","doi":"10.1016/j.neubiorev.2019.03.017","date_published":"2019-06-01T00:00:00Z","date_created":"2020-06-25T12:52:13Z","page":"1-12"},{"citation":{"mla":"Stroud, Jake P., and Tim P. Vogels. “Cortical Signal Propagation: Balance, Amplify, Transmit.” Neuron, vol. 98, no. 1, Elsevier, 2018, pp. 8–9, doi:10.1016/j.neuron.2018.03.028.","short":"J.P. Stroud, T.P. Vogels, Neuron 98 (2018) 8–9.","ieee":"J. P. Stroud and T. P. Vogels, “Cortical signal propagation: Balance, amplify, transmit,” Neuron, vol. 98, no. 1. Elsevier, pp. 8–9, 2018.","ama":"Stroud JP, Vogels TP. Cortical signal propagation: Balance, amplify, transmit. Neuron. 2018;98(1):8-9. doi:10.1016/j.neuron.2018.03.028","apa":"Stroud, J. P., & Vogels, T. P. (2018). Cortical signal propagation: Balance, amplify, transmit. Neuron. Elsevier. https://doi.org/10.1016/j.neuron.2018.03.028","chicago":"Stroud, Jake P., and Tim P Vogels. “Cortical Signal Propagation: Balance, Amplify, Transmit.” Neuron. Elsevier, 2018. https://doi.org/10.1016/j.neuron.2018.03.028.","ista":"Stroud JP, Vogels TP. 2018. Cortical signal propagation: Balance, amplify, transmit. Neuron. 98(1), 8–9."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"full_name":"Stroud, Jake P.","last_name":"Stroud","first_name":"Jake P."},{"id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","first_name":"Tim P","full_name":"Vogels, Tim P","orcid":"0000-0003-3295-6181","last_name":"Vogels"}],"external_id":{"pmid":["29621492"]},"article_processing_charge":"No","title":"Cortical signal propagation: Balance, amplify, transmit","year":"2018","day":"04","publication":"Neuron","page":"8-9","date_published":"2018-04-04T00:00:00Z","doi":"10.1016/j.neuron.2018.03.028","date_created":"2020-06-25T12:53:39Z","publisher":"Elsevier","quality_controlled":"1","oa":1,"date_updated":"2021-01-12T08:16:31Z","extern":"1","_id":"8015","article_type":"original","type":"journal_article","status":"public","publication_identifier":{"issn":["0896-6273"]},"publication_status":"published","language":[{"iso":"eng"}],"volume":98,"issue":"1","abstract":[{"text":"The neural code of cortical processing remains uncracked; however, it must necessarily rely on faithful signal propagation between cortical areas. In this issue of Neuron, Joglekar et al. (2018) show that strong inter-areal excitation balanced by local inhibition can enable reliable signal propagation in data-constrained network models of macaque cortex. ","lang":"eng"}],"oa_version":"Published Version","pmid":1,"main_file_link":[{"url":"https://doi.org/10.1016/j.neuron.2018.03.028","open_access":"1"}],"month":"04","intvolume":" 98"},{"quality_controlled":"1","publisher":"Springer Nature","oa":1,"page":"1774-1783","date_published":"2018-12-01T00:00:00Z","doi":"10.1038/s41593-018-0276-0","date_created":"2020-06-30T13:18:02Z","year":"2018","day":"01","publication":"Nature Neuroscience","author":[{"first_name":"Jake P.","last_name":"Stroud","full_name":"Stroud, Jake P."},{"first_name":"Mason A.","full_name":"Porter, Mason A.","last_name":"Porter"},{"first_name":"Guillaume","full_name":"Hennequin, Guillaume","last_name":"Hennequin"},{"id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","first_name":"Tim P","orcid":"0000-0003-3295-6181","full_name":"Vogels, Tim P","last_name":"Vogels"}],"external_id":{"pmid":["30482949"]},"article_processing_charge":"No","title":"Motor primitives in space and time via targeted gain modulation in cortical networks","citation":{"chicago":"Stroud, Jake P., Mason A. Porter, Guillaume Hennequin, and Tim P Vogels. “Motor Primitives in Space and Time via Targeted Gain Modulation in Cortical Networks.” Nature Neuroscience. Springer Nature, 2018. https://doi.org/10.1038/s41593-018-0276-0.","ista":"Stroud JP, Porter MA, Hennequin G, Vogels TP. 2018. Motor primitives in space and time via targeted gain modulation in cortical networks. Nature Neuroscience. 21(12), 1774–1783.","mla":"Stroud, Jake P., et al. “Motor Primitives in Space and Time via Targeted Gain Modulation in Cortical Networks.” Nature Neuroscience, vol. 21, no. 12, Springer Nature, 2018, pp. 1774–83, doi:10.1038/s41593-018-0276-0.","ama":"Stroud JP, Porter MA, Hennequin G, Vogels TP. Motor primitives in space and time via targeted gain modulation in cortical networks. Nature Neuroscience. 2018;21(12):1774-1783. doi:10.1038/s41593-018-0276-0","apa":"Stroud, J. P., Porter, M. A., Hennequin, G., & Vogels, T. P. (2018). Motor primitives in space and time via targeted gain modulation in cortical networks. Nature Neuroscience. Springer Nature. https://doi.org/10.1038/s41593-018-0276-0","short":"J.P. Stroud, M.A. Porter, G. Hennequin, T.P. Vogels, Nature Neuroscience 21 (2018) 1774–1783.","ieee":"J. P. Stroud, M. A. Porter, G. Hennequin, and T. P. Vogels, “Motor primitives in space and time via targeted gain modulation in cortical networks,” Nature Neuroscience, vol. 21, no. 12. Springer Nature, pp. 1774–1783, 2018."},"user_id":"D865714E-FA4E-11E9-B85B-F5C5E5697425","main_file_link":[{"open_access":"1","url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6276991/"}],"month":"12","intvolume":" 21","abstract":[{"text":"Motor cortex (M1) exhibits a rich repertoire of neuronal activities to support the generation of complex movements. Although recent neuronal-network models capture many qualitative aspects of M1 dynamics, they can generate only a few distinct movements. Additionally, it is unclear how M1 efficiently controls movements over a wide range of shapes and speeds. We demonstrate that modulation of neuronal input–output gains in recurrent neuronal-network models with a fixed architecture can dramatically reorganize neuronal activity and thus downstream muscle outputs. Consistent with the observation of diffuse neuromodulatory projections to M1, a relatively small number of modulatory control units provide sufficient flexibility to adjust high-dimensional network activity using a simple reward-based learning rule. Furthermore, it is possible to assemble novel movements from previously learned primitives, and one can separately change movement speed while preserving movement shape. Our results provide a new perspective on the role of modulatory systems in controlling recurrent cortical activity.","lang":"eng"}],"pmid":1,"oa_version":"Submitted Version","issue":"12","volume":21,"related_material":{"link":[{"relation":"erratum","url":"https://doi.org/10.1038/s41593-018-0307-x"}]},"publication_identifier":{"issn":["1097-6256","1546-1726"]},"publication_status":"published","language":[{"iso":"eng"}],"type":"journal_article","article_type":"original","status":"public","_id":"8073","date_updated":"2021-01-12T08:16:46Z","extern":"1"}]