{"database":"BioModels","file_versions":[{"headers":{"Content-Type":["application/json"]},"body":{"files":{"Pdf":["https://www.ebi.ac.uk/biomodels/model/download/MODEL1006230028?filename=MODEL1006230028.pdf"],"Svg":["https://www.ebi.ac.uk/biomodels/model/download/MODEL1006230028?filename=MODEL1006230028.svg"],"Owl":["https://www.ebi.ac.uk/biomodels/model/download/MODEL1006230028?filename=MODEL1006230028-biopax2.owl","https://www.ebi.ac.uk/biomodels/model/download/MODEL1006230028?filename=MODEL1006230028-biopax3.owl"],"Xml":["https://www.ebi.ac.uk/biomodels/model/download/MODEL1006230028?filename=MODEL1006230028_url.xml","https://www.ebi.ac.uk/biomodels/model/download/MODEL1006230028?filename=MODEL1006230028_urn.xml"],"Other":["https://www.ebi.ac.uk/biomodels/model/download/MODEL1006230028?filename=MODEL1006230028.xpp","https://www.ebi.ac.uk/biomodels/model/download/MODEL1006230028?filename=MODEL1006230028.sci","https://www.ebi.ac.uk/biomodels/model/download/MODEL1006230028?filename=MODEL1006230028.png","https://www.ebi.ac.uk/biomodels/model/download/MODEL1006230028?filename=MODEL1006230028.vcml","https://www.ebi.ac.uk/biomodels/model/download/MODEL1006230028?filename=MODEL1006230028.m"]},"type":"primary"},"statusCode":"OK","statusCodeValue":200}],"scores":null,"additional":{"submitter":["Camille Laibe"],"curationStatus":["Non-curated"],"modellingApproach":["ordinary differential equation model"],"levelVersion":["L2V4"],"full_dataset_link":["https://www.ebi.ac.uk/biomodels/MODEL1006230028"],"publication_pubmed":["20164396"],"isPrivate":["false"],"repository":["BioModels"],"modelFormat":["SBML"],"omics_type":["Models"],"tokenised_name":["Tabak2010 NeuronalNetworks"],"publication_year":["2010"],"submissionId":["MODEL1006230028"],"publication_authors":["Joël Tabak, Michael Mascagni, R Bertram"],"first_author":["Joël Tabak"],"publication":["20164396,\n                            Spontaneous episodic activity is a fundamental mode of operation of developing networks. Surprisingly, the duration of an episode of activity correlates with the length of the silent interval that precedes it, but not with the interval that follows. Here we use a modeling approach to explain this characteristic, but thus far unexplained, feature of developing networks. Because the correlation pattern is observed in networks with different structures and components, a satisfactory model needs to generate the right pattern of activity regardless of the details of network architecture or individual cell properties. We thus developed simple models incorporating excitatory coupling between heterogeneous neurons and activity-dependent synaptic depression. These models robustly generated episodic activity with the correct correlation pattern. The correlation pattern resulted from episodes being triggered at random levels of recovery from depression while they terminated around the same level of depression. To explain this fundamental difference between episode onset and termination, we used a mean field model, where only average activity and average level of recovery from synaptic depression are considered. In this model, episode onset is highly sensitive to inputs. Thus noise resulting from random coincidences in the spike times of individual neurons led to the high variability at episode onset and to the observed correlation pattern. This work further shows that networks with widely different architectures, different cell types, and different functions all operate according to the same general mechanism early in their development.. 4, 103.\n                            Dept. of Biological Science, BRF 206, Florida State Univ., Tallahassee, FL 32306, USA. joel@neuro.fsu.edu"],"submitter_mail":["laibe@ebi.ac.uk"],"submitter_affiliation":["EMBL-EBI"],"pubmed_abstract":["Spontaneous episodic activity is a fundamental mode of operation of developing networks. Surprisingly, the duration of an episode of activity correlates with the length of the silent interval that precedes it, but not with the interval that follows. Here we use a modeling approach to explain this characteristic, but thus far unexplained, feature of developing networks. Because the correlation pattern is observed in networks with different structures and components, a satisfactory model needs to generate the right pattern of activity regardless of the details of network architecture or individual cell properties. We thus developed simple models incorporating excitatory coupling between heterogeneous neurons and activity-dependent synaptic depression. These models robustly generated episodic activity with the correct correlation pattern. The correlation pattern resulted from episodes being triggered at random levels of recovery from depression while they terminated around the same level of depression. To explain this fundamental difference between episode onset and termination, we used a mean field model, where only average activity and average level of recovery from synaptic depression are considered. In this model, episode onset is highly sensitive to inputs. Thus noise resulting from random coincidences in the spike times of individual neurons led to the high variability at episode onset and to the observed correlation pattern. This work further shows that networks with widely different architectures, different cell types, and different functions all operate according to the same general mechanism early in their development."],"pubmed_title":["Mechanism for the universal pattern of activity in developing neuronal networks."],"pubmed_authors":["Tabak Joël J, Mascagni Michael M, Bertram Richard R"],"additional_accession":[]},"is_claimable":false,"name":"Tabak2010_NeuronalNetworks","description":"\n      \n        This a model from the article:      \n        Mechanism for the universal pattern of activity in developing neuronal networks.\n        \n          Tabak J, Mascagni M, Bertram R.      J Neurophysiol\n          2010 Feb 17; [Epub ahead of print]      20164396\n          ,      \n        Abstract:\n        \n          Spontaneous episodic activity is a fundamental mode of operation of developing\nnetworks. Surprisingly, the duration of an episode of activity correlates with\nthe length of the silent interval that precedes it, but not with the interval\nthat follows. Here we use a modeling approach to explain this characteristic but\nso far unexplained feature of developing networks. Because the correlation\npattern is observed in networks with different structures and components, a\nsatisfactory model needs to generate the right pattern of activity regardless of\nthe details of network architecture or individual cell properties. We thus\ndeveloped simple models incorporating excitatory coupling between heterogeneous\nneurons and activity-dependent synaptic depression. These models robustly\ngenerated episodic activity with the correct correlation pattern. The\ncorrelation pattern resulted from episodes being triggered at random levels of\nrecovery from depression while they terminated around the same level of\ndepression. To explain this fundamental difference between episode onset and\ntermination, we then used a mean field model, where only average activity and\naverage level of recovery from synaptic depression are considered. In this\nmodel, episode onset is highly sensitive to inputs. Thus, noise resulting from\nrandom coincidences in the spike times of individual neurons led to the high\nvariability at episode onset and to the observed correlation pattern. This work\nfurther demonstrates that networks with widely different architectures,\ndifferent cell types and different functions, all operate according to the same\ngeneral mechanism early in their development.      \n      This model was taken from the      CellML repository\n          and automatically converted to SBML.      \n          The original model was:      \n        Tabak J, Mascagni M, Bertram R. (2010) - version=1.0\n      \n      \n          The original CellML model was created by:      \n      Geoffrey Nunns\n      \n          gnunns1@jhu.edu      \n          The University of Auckland      \n    This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.      \n          To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to      CC0 Public Domain Dedication\n          for more information.      \n  In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..      \n  \n          To cite BioModels Database, please use:      Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.\n\n\n","dates":{"last_modification":"2010-06-25","publication":"2005-01-01","submission":"2010-06-23"},"accession":"MODEL1006230028","cross_references":{"pubmed":["20164396"],"biomodels__db":["MODEL1006230028"],"go":["GO:0010644"],"taxonomy":["9606"],"bto":["BTO:0000938"]}}