[{"scopus_import":"1","has_accepted_license":"1","article_processing_charge":"Yes (in subscription journal)","day":"29","citation":{"ama":"Nardin M, Csicsvari JL, Tkačik G, Savin C. The structure of hippocampal CA1 interactions optimizes spatial coding across experience. The Journal of Neuroscience. 2023;43(48):8140-8156. doi:10.1523/JNEUROSCI.0194-23.2023","ieee":"M. Nardin, J. L. Csicsvari, G. Tkačik, and C. Savin, “The structure of hippocampal CA1 interactions optimizes spatial coding across experience,” The Journal of Neuroscience, vol. 43, no. 48. Society of Neuroscience, pp. 8140–8156, 2023.","apa":"Nardin, M., Csicsvari, J. L., Tkačik, G., & Savin, C. (2023). The structure of hippocampal CA1 interactions optimizes spatial coding across experience. The Journal of Neuroscience. Society of Neuroscience. https://doi.org/10.1523/JNEUROSCI.0194-23.2023","ista":"Nardin M, Csicsvari JL, Tkačik G, Savin C. 2023. The structure of hippocampal CA1 interactions optimizes spatial coding across experience. The Journal of Neuroscience. 43(48), 8140–8156.","short":"M. Nardin, J.L. Csicsvari, G. Tkačik, C. Savin, The Journal of Neuroscience 43 (2023) 8140–8156.","mla":"Nardin, Michele, et al. “The Structure of Hippocampal CA1 Interactions Optimizes Spatial Coding across Experience.” The Journal of Neuroscience, vol. 43, no. 48, Society of Neuroscience, 2023, pp. 8140–56, doi:10.1523/JNEUROSCI.0194-23.2023.","chicago":"Nardin, Michele, Jozsef L Csicsvari, Gašper Tkačik, and Cristina Savin. “The Structure of Hippocampal CA1 Interactions Optimizes Spatial Coding across Experience.” The Journal of Neuroscience. Society of Neuroscience, 2023. https://doi.org/10.1523/JNEUROSCI.0194-23.2023."},"publication":"The Journal of Neuroscience","page":"8140-8156","article_type":"original","date_published":"2023-11-29T00:00:00Z","type":"journal_article","issue":"48","abstract":[{"lang":"eng","text":"Although much is known about how single neurons in the hippocampus represent an animal's position, how circuit interactions contribute to spatial coding is less well understood. Using a novel statistical estimator and theoretical modeling, both developed in the framework of maximum entropy models, we reveal highly structured CA1 cell-cell interactions in male rats during open field exploration. The statistics of these interactions depend on whether the animal is in a familiar or novel environment. In both conditions the circuit interactions optimize the encoding of spatial information, but for regimes that differ in the informativeness of their spatial inputs. This structure facilitates linear decodability, making the information easy to read out by downstream circuits. Overall, our findings suggest that the efficient coding hypothesis is not only applicable to individual neuron properties in the sensory periphery, but also to neural interactions in the central brain."}],"_id":"14656","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","intvolume":" 43","ddc":["570"],"title":"The structure of hippocampal CA1 interactions optimizes spatial coding across experience","status":"public","oa_version":"Published Version","file":[{"file_size":2280632,"content_type":"application/pdf","creator":"dernst","access_level":"closed","embargo_to":"open_access","file_name":"2023_JourNeuroscience_Nardin.pdf","checksum":"e2503c8f84be1050e28f64320f1d5bd2","date_updated":"2023-12-11T11:30:37Z","date_created":"2023-12-11T11:30:37Z","relation":"main_file","embargo":"2024-06-01","file_id":"14674"}],"publication_identifier":{"eissn":["1529-2401"]},"month":"11","oa":1,"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"main_file_link":[{"url":"https://doi.org/10.1523/JNEUROSCI.0194-23.2023","open_access":"1"}],"external_id":{"pmid":["37758476"]},"project":[{"_id":"257A4776-B435-11E9-9278-68D0E5697425","grant_number":"281511","call_identifier":"FP7","name":"Memory-related information processing in neuronal circuits of the hippocampus and entorhinal cortex"},{"name":"Efficient coding with biophysical realism","_id":"626c45b5-2b32-11ec-9570-e509828c1ba6","grant_number":"P34015"},{"grant_number":"665385","_id":"2564DBCA-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","name":"International IST Doctoral Program"}],"quality_controlled":"1","doi":"10.1523/JNEUROSCI.0194-23.2023","language":[{"iso":"eng"}],"ec_funded":1,"file_date_updated":"2023-12-11T11:30:37Z","license":"https://creativecommons.org/licenses/by/4.0/","pmid":1,"year":"2023","acknowledgement":"M.N. was supported by the European Union Horizon 2020 Grant 665385. J.C. was supported by the European Research Council Consolidator Grant 281511. G.T. was supported by the Austrian Science Fund (FWF) Grant P34015. C.S. was supported by an Institute of Science and Technology fellow award and by the National Science Foundation (NSF) Award No. 1922658. We thank Peter Baracskay, Karola Kaefer, and Hugo Malagon-Vina for the acquisition of the data. We also thank Federico Stella, Wiktor Młynarski, Dori Derdikman, Colin Bredenberg, Roman Huszar, Heloisa Chiossi, Lorenzo Posani, and Mohamady El-Gaby for comments on an earlier version of the manuscript.","publisher":"Society of Neuroscience","department":[{"_id":"JoCs"},{"_id":"GaTk"}],"publication_status":"published","author":[{"full_name":"Nardin, Michele","orcid":"0000-0001-8849-6570","id":"30BD0376-F248-11E8-B48F-1D18A9856A87","last_name":"Nardin","first_name":"Michele"},{"orcid":"0000-0002-5193-4036","id":"3FA14672-F248-11E8-B48F-1D18A9856A87","last_name":"Csicsvari","first_name":"Jozsef L","full_name":"Csicsvari, Jozsef L"},{"full_name":"Tkačik, Gašper","orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkačik","first_name":"Gašper"},{"full_name":"Savin, Cristina","id":"3933349E-F248-11E8-B48F-1D18A9856A87","first_name":"Cristina","last_name":"Savin"}],"volume":43,"date_updated":"2023-12-11T11:37:20Z","date_created":"2023-12-10T23:00:58Z"},{"type":"preprint","license":"https://creativecommons.org/licenses/by-nc-nd/4.0/","abstract":[{"text":"Although much is known about how single neurons in the hippocampus represent an animal’s position, how cell-cell interactions contribute to spatial coding remains poorly understood. Using a novel statistical estimator and theoretical modeling, both developed in the framework of maximum entropy models, we reveal highly structured cell-to-cell interactions whose statistics depend on familiar vs. novel environment. In both conditions the circuit interactions optimize the encoding of spatial information, but for regimes that differ in the signal-to-noise ratio of their spatial inputs. Moreover, the topology of the interactions facilitates linear decodability, making the information easy to read out by downstream circuits. These findings suggest that the efficient coding hypothesis is not applicable only to individual neuron properties in the sensory periphery, but also to neural interactions in the central brain.","lang":"eng"}],"ec_funded":1,"title":"The structure of hippocampal CA1 interactions optimizes spatial coding across experience","publication_status":"submitted","status":"public","department":[{"_id":"GradSch"},{"_id":"JoCs"},{"_id":"GaTk"}],"publisher":"Cold Spring Harbor Laboratory","year":"2021","_id":"10077","acknowledgement":"We thank Peter Baracskay, Karola Kaefer and Hugo Malagon-Vina for the acquisition of the data. We thank Federico Stella for comments on an earlier version of the manuscript. MN was supported by European Union Horizon 2020 grant 665385, JC was supported by European Research Council consolidator grant 281511, GT was supported by the Austrian Science Fund (FWF) grant P34015, CS was supported by an IST fellow grant, National Institute of Mental Health Award 1R01MH125571-01, by the National Science Foundation under NSF Award No. 1922658 and a Google faculty award.","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","date_created":"2021-10-04T06:23:34Z","date_updated":"2024-03-28T23:30:16Z","oa_version":"Preprint","author":[{"id":"30BD0376-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-8849-6570","first_name":"Michele","last_name":"Nardin","full_name":"Nardin, Michele"},{"full_name":"Csicsvari, Jozsef L","id":"3FA14672-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-5193-4036","first_name":"Jozsef L","last_name":"Csicsvari"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6699-1455","first_name":"Gašper","last_name":"Tkačik","full_name":"Tkačik, Gašper"},{"last_name":"Savin","first_name":"Cristina","id":"3933349E-F248-11E8-B48F-1D18A9856A87","full_name":"Savin, Cristina"}],"related_material":{"record":[{"relation":"dissertation_contains","status":"public","id":"11932"}]},"month":"09","day":"29","article_processing_charge":"No","project":[{"name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734"},{"_id":"2564DBCA-B435-11E9-9278-68D0E5697425","grant_number":"665385","name":"International IST Doctoral Program","call_identifier":"H2020"},{"_id":"257A4776-B435-11E9-9278-68D0E5697425","grant_number":"281511","call_identifier":"FP7","name":"Memory-related information processing in neuronal circuits of the hippocampus and entorhinal cortex"},{"_id":"626c45b5-2b32-11ec-9570-e509828c1ba6","grant_number":"P34015","name":"Efficient coding with biophysical realism"}],"publication":"bioRxiv","oa":1,"tmp":{"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","short":"CC BY-NC-ND (4.0)","image":"/images/cc_by_nc_nd.png"},"main_file_link":[{"open_access":"1","url":"https://www.biorxiv.org/content/10.1101/2021.09.28.460602"}],"citation":{"ama":"Nardin M, Csicsvari JL, Tkačik G, Savin C. The structure of hippocampal CA1 interactions optimizes spatial coding across experience. bioRxiv. doi:10.1101/2021.09.28.460602","ista":"Nardin M, Csicsvari JL, Tkačik G, Savin C. The structure of hippocampal CA1 interactions optimizes spatial coding across experience. bioRxiv, 10.1101/2021.09.28.460602.","ieee":"M. Nardin, J. L. Csicsvari, G. Tkačik, and C. Savin, “The structure of hippocampal CA1 interactions optimizes spatial coding across experience,” bioRxiv. Cold Spring Harbor Laboratory.","apa":"Nardin, M., Csicsvari, J. L., Tkačik, G., & Savin, C. (n.d.). The structure of hippocampal CA1 interactions optimizes spatial coding across experience. bioRxiv. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2021.09.28.460602","mla":"Nardin, Michele, et al. “The Structure of Hippocampal CA1 Interactions Optimizes Spatial Coding across Experience.” BioRxiv, Cold Spring Harbor Laboratory, doi:10.1101/2021.09.28.460602.","short":"M. Nardin, J.L. Csicsvari, G. Tkačik, C. Savin, BioRxiv (n.d.).","chicago":"Nardin, Michele, Jozsef L Csicsvari, Gašper Tkačik, and Cristina Savin. “The Structure of Hippocampal CA1 Interactions Optimizes Spatial Coding across Experience.” BioRxiv. Cold Spring Harbor Laboratory, n.d. https://doi.org/10.1101/2021.09.28.460602."},"language":[{"iso":"eng"}],"doi":"10.1101/2021.09.28.460602","date_published":"2021-09-29T00:00:00Z"},{"publication_identifier":{"issn":["09594388"]},"month":"10","language":[{"iso":"eng"}],"doi":"10.1016/j.conb.2017.08.001","project":[{"grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme"}],"quality_controlled":"1","isi":1,"external_id":{"isi":["000416196400016"]},"publist_id":"6943","ec_funded":1,"volume":46,"date_created":"2018-12-11T11:48:11Z","date_updated":"2023-09-28T11:32:22Z","author":[{"full_name":"Savin, Cristina","id":"3933349E-F248-11E8-B48F-1D18A9856A87","first_name":"Cristina","last_name":"Savin"},{"full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkacik","first_name":"Gasper"}],"publisher":"Elsevier","department":[{"_id":"GaTk"}],"publication_status":"published","year":"2017","article_processing_charge":"No","day":"01","scopus_import":"1","date_published":"2017-10-01T00:00:00Z","page":"120 - 126","citation":{"apa":"Savin, C., & Tkačik, G. (2017). Maximum entropy models as a tool for building precise neural controls. Current Opinion in Neurobiology. Elsevier. https://doi.org/10.1016/j.conb.2017.08.001","ieee":"C. Savin and G. Tkačik, “Maximum entropy models as a tool for building precise neural controls,” Current Opinion in Neurobiology, vol. 46. Elsevier, pp. 120–126, 2017.","ista":"Savin C, Tkačik G. 2017. Maximum entropy models as a tool for building precise neural controls. Current Opinion in Neurobiology. 46, 120–126.","ama":"Savin C, Tkačik G. Maximum entropy models as a tool for building precise neural controls. Current Opinion in Neurobiology. 2017;46:120-126. doi:10.1016/j.conb.2017.08.001","chicago":"Savin, Cristina, and Gašper Tkačik. “Maximum Entropy Models as a Tool for Building Precise Neural Controls.” Current Opinion in Neurobiology. Elsevier, 2017. https://doi.org/10.1016/j.conb.2017.08.001.","short":"C. Savin, G. Tkačik, Current Opinion in Neurobiology 46 (2017) 120–126.","mla":"Savin, Cristina, and Gašper Tkačik. “Maximum Entropy Models as a Tool for Building Precise Neural Controls.” Current Opinion in Neurobiology, vol. 46, Elsevier, 2017, pp. 120–26, doi:10.1016/j.conb.2017.08.001."},"publication":"Current Opinion in Neurobiology","abstract":[{"text":"Neural responses are highly structured, with population activity restricted to a small subset of the astronomical range of possible activity patterns. Characterizing these statistical regularities is important for understanding circuit computation, but challenging in practice. Here we review recent approaches based on the maximum entropy principle used for quantifying collective behavior in neural activity. We highlight recent models that capture population-level statistics of neural data, yielding insights into the organization of the neural code and its biological substrate. Furthermore, the MaxEnt framework provides a general recipe for constructing surrogate ensembles that preserve aspects of the data, but are otherwise maximally unstructured. This idea can be used to generate a hierarchy of controls against which rigorous statistical tests are possible.","lang":"eng"}],"type":"journal_article","oa_version":"None","intvolume":" 46","title":"Maximum entropy models as a tool for building precise neural controls","status":"public","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","_id":"730"},{"abstract":[{"text":"Jointly characterizing neural responses in terms of several external variables promises novel insights into circuit function, but remains computationally prohibitive in practice. Here we use gaussian process (GP) priors and exploit recent advances in fast GP inference and learning based on Kronecker methods, to efficiently estimate multidimensional nonlinear tuning functions. Our estimator require considerably less data than traditional methods and further provides principled uncertainty estimates. We apply these tools to hippocampal recordings during open field exploration and use them to characterize the joint dependence of CA1 responses on the position of the animal and several other variables, including the animal\\'s speed, direction of motion, and network oscillations.Our results provide an unprecedentedly detailed quantification of the tuning of hippocampal neurons. The model\\'s generality suggests that our approach can be used to estimate neural response properties in other brain regions.","lang":"eng"}],"alternative_title":["Advances in Neural Information Processing Systems"],"type":"conference","oa_version":"None","intvolume":" 29","status":"public","title":"Estimating nonlinear neural response functions using GP priors and Kronecker methods","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","_id":"1105","day":"01","scopus_import":1,"date_published":"2016-12-01T00:00:00Z","page":"3610-3618","citation":{"chicago":"Savin, Cristina, and Gašper Tkačik. “Estimating Nonlinear Neural Response Functions Using GP Priors and Kronecker Methods,” 29:3610–18. Neural Information Processing Systems, 2016.","short":"C. Savin, G. Tkačik, in:, Neural Information Processing Systems, 2016, pp. 3610–3618.","mla":"Savin, Cristina, and Gašper Tkačik. Estimating Nonlinear Neural Response Functions Using GP Priors and Kronecker Methods. Vol. 29, Neural Information Processing Systems, 2016, pp. 3610–18.","ieee":"C. Savin and G. Tkačik, “Estimating nonlinear neural response functions using GP priors and Kronecker methods,” presented at the NIPS: Neural Information Processing Systems, Barcelona; Spain, 2016, vol. 29, pp. 3610–3618.","apa":"Savin, C., & Tkačik, G. (2016). Estimating nonlinear neural response functions using GP priors and Kronecker methods (Vol. 29, pp. 3610–3618). Presented at the NIPS: Neural Information Processing Systems, Barcelona; Spain: Neural Information Processing Systems.","ista":"Savin C, Tkačik G. 2016. Estimating nonlinear neural response functions using GP priors and Kronecker methods. NIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 29, 3610–3618.","ama":"Savin C, Tkačik G. Estimating nonlinear neural response functions using GP priors and Kronecker methods. In: Vol 29. Neural Information Processing Systems; 2016:3610-3618."},"ec_funded":1,"publist_id":"6265","volume":29,"date_updated":"2021-01-12T06:48:19Z","date_created":"2018-12-11T11:50:10Z","author":[{"last_name":"Savin","first_name":"Cristina","id":"3933349E-F248-11E8-B48F-1D18A9856A87","full_name":"Savin, Cristina"},{"first_name":"Gasper","last_name":"Tkacik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper"}],"department":[{"_id":"GaTk"}],"publisher":"Neural Information Processing Systems","publication_status":"published","acknowledgement":"We thank Jozsef Csicsvari for kindly sharing the CA1 data.\r\nThis work was supported by the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme(FP7/2007-2013) under REA grant agreement no. 291734.","year":"2016","month":"12","language":[{"iso":"eng"}],"conference":{"name":"NIPS: Neural Information Processing Systems","end_date":"2016-12-10","start_date":"2016-12-05","location":"Barcelona; Spain"},"project":[{"_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734","call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme"}],"quality_controlled":"1","main_file_link":[{"url":"http://papers.nips.cc/paper/6153-estimating-nonlinear-neural-response-functions-using-gp-priors-and-kronecker-methods"}]},{"conference":{"name":"NIPS: Neural Information Processing Systems","end_date":"2016-12-10","start_date":"2016-12-05","location":"Barcelona, Spaine"},"language":[{"iso":"eng"}],"main_file_link":[{"url":"https://papers.nips.cc/paper/6582-neurons-equipped-with-intrinsic-plasticity-learn-stimulus-intensity-statistics"}],"quality_controlled":"1","project":[{"call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425"}],"month":"01","author":[{"first_name":"Travis","last_name":"Monk","full_name":"Monk, Travis"},{"id":"3933349E-F248-11E8-B48F-1D18A9856A87","first_name":"Cristina","last_name":"Savin","full_name":"Savin, Cristina"},{"last_name":"Lücke","first_name":"Jörg","full_name":"Lücke, Jörg"}],"date_updated":"2021-01-12T08:22:08Z","date_created":"2018-12-11T11:49:21Z","volume":29,"year":"2016","acknowledgement":"DFG Cluster of Excellence EXC 1077/1 (Hearing4all) and LU 1196/5-1 (JL and TM), People Programme (Marie Curie Actions) FP7/2007-2013 grant agreement no. 291734 (CS)","publication_status":"published","department":[{"_id":"GaTk"}],"publisher":"Neural Information Processing Systems","publist_id":"6469","ec_funded":1,"date_published":"2016-01-01T00:00:00Z","citation":{"ista":"Monk T, Savin C, Lücke J. 2016. Neurons equipped with intrinsic plasticity learn stimulus intensity statistics. NIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 29, 4285–4293.","apa":"Monk, T., Savin, C., & Lücke, J. (2016). Neurons equipped with intrinsic plasticity learn stimulus intensity statistics (Vol. 29, pp. 4285–4293). Presented at the NIPS: Neural Information Processing Systems, Barcelona, Spaine: Neural Information Processing Systems.","ieee":"T. Monk, C. Savin, and J. Lücke, “Neurons equipped with intrinsic plasticity learn stimulus intensity statistics,” presented at the NIPS: Neural Information Processing Systems, Barcelona, Spaine, 2016, vol. 29, pp. 4285–4293.","ama":"Monk T, Savin C, Lücke J. Neurons equipped with intrinsic plasticity learn stimulus intensity statistics. In: Vol 29. Neural Information Processing Systems; 2016:4285-4293.","chicago":"Monk, Travis, Cristina Savin, and Jörg Lücke. “Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics,” 29:4285–93. Neural Information Processing Systems, 2016.","mla":"Monk, Travis, et al. Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics. Vol. 29, Neural Information Processing Systems, 2016, pp. 4285–93.","short":"T. Monk, C. Savin, J. Lücke, in:, Neural Information Processing Systems, 2016, pp. 4285–4293."},"page":"4285 - 4293","day":"01","scopus_import":1,"oa_version":"None","_id":"948","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","status":"public","title":"Neurons equipped with intrinsic plasticity learn stimulus intensity statistics","intvolume":" 29","abstract":[{"text":"Experience constantly shapes neural circuits through a variety of plasticity mechanisms. While the functional roles of some plasticity mechanisms are well-understood, it remains unclear how changes in neural excitability contribute to learning. Here, we develop a normative interpretation of intrinsic plasticity (IP) as a key component of unsupervised learning. We introduce a novel generative mixture model that accounts for the class-specific statistics of stimulus intensities, and we derive a neural circuit that learns the input classes and their intensities. We will analytically show that inference and learning for our generative model can be achieved by a neural circuit with intensity-sensitive neurons equipped with a specific form of IP. Numerical experiments verify our analytical derivations and show robust behavior for artificial and natural stimuli. Our results link IP to non-trivial input statistics, in particular the statistics of stimulus intensities for classes to which a neuron is sensitive. More generally, our work paves the way toward new classification algorithms that are robust to intensity variations.","lang":"eng"}],"type":"conference","alternative_title":["Advances in Neural Information Processing Systems"]},{"article_number":"145","file_date_updated":"2020-07-14T12:45:02Z","publist_id":"5607","ec_funded":1,"year":"2015","publication_status":"published","publisher":"Frontiers Research Foundation","department":[{"_id":"GaTk"}],"author":[{"full_name":"Gilson, Matthieu","last_name":"Gilson","first_name":"Matthieu"},{"full_name":"Savin, Cristina","id":"3933349E-F248-11E8-B48F-1D18A9856A87","last_name":"Savin","first_name":"Cristina"},{"last_name":"Zenke","first_name":"Friedemann","full_name":"Zenke, Friedemann"}],"date_created":"2018-12-11T11:52:45Z","date_updated":"2021-01-12T06:51:37Z","volume":9,"month":"11","oa":1,"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"quality_controlled":"1","project":[{"call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425"}],"doi":"10.3389/fncom.2015.00145","language":[{"iso":"eng"}],"type":"journal_article","issue":"11","_id":"1564","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","ddc":["570"],"title":"Editorial: Emergent neural computation from the interaction of different forms of plasticity","status":"public","intvolume":" 9","pubrep_id":"479","oa_version":"Published Version","file":[{"file_size":187038,"content_type":"application/pdf","creator":"system","access_level":"open_access","file_name":"IST-2016-479-v1+1_fncom-09-00145.pdf","checksum":"cea73b6d3ef1579f32da10b82f4de4fd","date_created":"2018-12-12T10:12:09Z","date_updated":"2020-07-14T12:45:02Z","relation":"main_file","file_id":"4927"}],"scopus_import":1,"day":"30","has_accepted_license":"1","publication":"Frontiers in Computational Neuroscience","citation":{"ista":"Gilson M, Savin C, Zenke F. 2015. Editorial: Emergent neural computation from the interaction of different forms of plasticity. Frontiers in Computational Neuroscience. 9(11), 145.","ieee":"M. Gilson, C. Savin, and F. Zenke, “Editorial: Emergent neural computation from the interaction of different forms of plasticity,” Frontiers in Computational Neuroscience, vol. 9, no. 11. Frontiers Research Foundation, 2015.","apa":"Gilson, M., Savin, C., & Zenke, F. (2015). Editorial: Emergent neural computation from the interaction of different forms of plasticity. Frontiers in Computational Neuroscience. Frontiers Research Foundation. https://doi.org/10.3389/fncom.2015.00145","ama":"Gilson M, Savin C, Zenke F. Editorial: Emergent neural computation from the interaction of different forms of plasticity. Frontiers in Computational Neuroscience. 2015;9(11). doi:10.3389/fncom.2015.00145","chicago":"Gilson, Matthieu, Cristina Savin, and Friedemann Zenke. “Editorial: Emergent Neural Computation from the Interaction of Different Forms of Plasticity.” Frontiers in Computational Neuroscience. Frontiers Research Foundation, 2015. https://doi.org/10.3389/fncom.2015.00145.","mla":"Gilson, Matthieu, et al. “Editorial: Emergent Neural Computation from the Interaction of Different Forms of Plasticity.” Frontiers in Computational Neuroscience, vol. 9, no. 11, 145, Frontiers Research Foundation, 2015, doi:10.3389/fncom.2015.00145.","short":"M. Gilson, C. Savin, F. Zenke, Frontiers in Computational Neuroscience 9 (2015)."},"date_published":"2015-11-30T00:00:00Z"},{"scopus_import":1,"month":"01","day":"01","quality_controlled":"1","page":"2024 - 2032","main_file_link":[{"url":"http://papers.nips.cc/paper/5343-spatio-temporal-representations-of-uncertainty-in-spiking-neural-networks.pdf"}],"citation":{"ista":"Savin C, Denève S. 2014. Spatio-temporal representations of uncertainty in spiking neural networks. NIPS: Neural Information Processing Systems vol. 3, 2024–2032.","ieee":"C. Savin and S. Denève, “Spatio-temporal representations of uncertainty in spiking neural networks,” presented at the NIPS: Neural Information Processing Systems, Montreal, Canada, 2014, vol. 3, no. January, pp. 2024–2032.","apa":"Savin, C., & Denève, S. (2014). Spatio-temporal representations of uncertainty in spiking neural networks (Vol. 3, pp. 2024–2032). Presented at the NIPS: Neural Information Processing Systems, Montreal, Canada: Neural Information Processing Systems.","ama":"Savin C, Denève S. Spatio-temporal representations of uncertainty in spiking neural networks. In: Vol 3. Neural Information Processing Systems; 2014:2024-2032.","chicago":"Savin, Cristina, and Sophie Denève. “Spatio-Temporal Representations of Uncertainty in Spiking Neural Networks,” 3:2024–32. Neural Information Processing Systems, 2014.","mla":"Savin, Cristina, and Sophie Denève. Spatio-Temporal Representations of Uncertainty in Spiking Neural Networks. Vol. 3, no. January, Neural Information Processing Systems, 2014, pp. 2024–32.","short":"C. Savin, S. Denève, in:, Neural Information Processing Systems, 2014, pp. 2024–2032."},"language":[{"iso":"eng"}],"conference":{"start_date":"2014-12-08","location":"Montreal, Canada","end_date":"2014-12-13","name":"NIPS: Neural Information Processing Systems"},"date_published":"2014-01-01T00:00:00Z","type":"conference","abstract":[{"text":"It has been long argued that, because of inherent ambiguity and noise, the brain needs to represent uncertainty in the form of probability distributions. The neural encoding of such distributions remains however highly controversial. Here we present a novel circuit model for representing multidimensional real-valued distributions using a spike based spatio-temporal code. Our model combines the computational advantages of the currently competing models for probabilistic codes and exhibits realistic neural responses along a variety of classic measures. Furthermore, the model highlights the challenges associated with interpreting neural activity in relation to behavioral uncertainty and points to alternative population-level approaches for the experimental validation of distributed representations.","lang":"eng"}],"publist_id":"5427","issue":"January","title":"Spatio-temporal representations of uncertainty in spiking neural networks","publication_status":"published","status":"public","publisher":"Neural Information Processing Systems","department":[{"_id":"GaTk"}],"intvolume":" 3","year":"2014","_id":"1708","user_id":"4435EBFC-F248-11E8-B48F-1D18A9856A87","date_updated":"2021-01-12T06:52:40Z","date_created":"2018-12-11T11:53:35Z","oa_version":"None","volume":3,"author":[{"full_name":"Savin, Cristina","id":"3933349E-F248-11E8-B48F-1D18A9856A87","first_name":"Cristina","last_name":"Savin"},{"last_name":"Denève","first_name":"Sophie","full_name":"Denève, Sophie"}]},{"date_published":"2014-05-28T00:00:00Z","citation":{"mla":"Savin, Cristina, and Jochen Triesch. “Emergence of Task-Dependent Representations in Working Memory Circuits.” Frontiers in Computational Neuroscience, vol. 8, no. MAY, 57, Frontiers Research Foundation, 2014, doi:10.3389/fncom.2014.00057.","short":"C. Savin, J. Triesch, Frontiers in Computational Neuroscience 8 (2014).","chicago":"Savin, Cristina, and Jochen Triesch. “Emergence of Task-Dependent Representations in Working Memory Circuits.” Frontiers in Computational Neuroscience. Frontiers Research Foundation, 2014. https://doi.org/10.3389/fncom.2014.00057.","ama":"Savin C, Triesch J. Emergence of task-dependent representations in working memory circuits. Frontiers in Computational Neuroscience. 2014;8(MAY). doi:10.3389/fncom.2014.00057","ista":"Savin C, Triesch J. 2014. Emergence of task-dependent representations in working memory circuits. Frontiers in Computational Neuroscience. 8(MAY), 57.","apa":"Savin, C., & Triesch, J. (2014). Emergence of task-dependent representations in working memory circuits. Frontiers in Computational Neuroscience. Frontiers Research Foundation. https://doi.org/10.3389/fncom.2014.00057","ieee":"C. Savin and J. Triesch, “Emergence of task-dependent representations in working memory circuits,” Frontiers in Computational Neuroscience, vol. 8, no. MAY. Frontiers Research Foundation, 2014."},"publication":"Frontiers in Computational Neuroscience","day":"28","scopus_import":1,"oa_version":"Submitted Version","user_id":"4435EBFC-F248-11E8-B48F-1D18A9856A87","_id":"1931","intvolume":" 8","title":"Emergence of task-dependent representations in working memory circuits","status":"public","issue":"MAY","abstract":[{"text":"A wealth of experimental evidence suggests that working memory circuits preferentially represent information that is behaviorally relevant. Still, we are missing a mechanistic account of how these representations come about. Here we provide a simple explanation for a range of experimental findings, in light of prefrontal circuits adapting to task constraints by reward-dependent learning. In particular, we model a neural network shaped by reward-modulated spike-timing dependent plasticity (r-STDP) and homeostatic plasticity (intrinsic excitability and synaptic scaling). We show that the experimentally-observed neural representations naturally emerge in an initially unstructured circuit as it learns to solve several working memory tasks. These results point to a critical, and previously unappreciated, role for reward-dependent learning in shaping prefrontal cortex activity.","lang":"eng"}],"type":"journal_article","doi":"10.3389/fncom.2014.00057","language":[{"iso":"eng"}],"main_file_link":[{"open_access":"1","url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4035833/"}],"oa":1,"quality_controlled":"1","month":"05","author":[{"id":"3933349E-F248-11E8-B48F-1D18A9856A87","first_name":"Cristina","last_name":"Savin","full_name":"Savin, Cristina"},{"full_name":"Triesch, Jochen","first_name":"Jochen","last_name":"Triesch"}],"volume":8,"date_created":"2018-12-11T11:54:46Z","date_updated":"2021-01-12T06:54:09Z","year":"2014","acknowledgement":"Supported in part by EC MEXT project PLICON and the LOEWE-Program “Neuronal Coordination Research Focus Frankfurt” (NeFF). Jochen Triesch was supported by the Quandt foundation.","department":[{"_id":"GaTk"}],"publisher":"Frontiers Research Foundation","publication_status":"published","publist_id":"5163","article_number":"57"}]