BioModelsapplication/xmlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL1006230028?filename=MODEL1006230028.pdfhttps://www.ebi.ac.uk/biomodels/model/download/MODEL1006230028?filename=MODEL1006230028-biopax2.owlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL1006230028?filename=MODEL1006230028-biopax3.owlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL1006230028?filename=MODEL1006230028_url.xmlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL1006230028?filename=MODEL1006230028_urn.xmlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL1006230028?filename=MODEL1006230028.xpphttps://www.ebi.ac.uk/biomodels/model/download/MODEL1006230028?filename=MODEL1006230028.scihttps://www.ebi.ac.uk/biomodels/model/download/MODEL1006230028?filename=MODEL1006230028.pnghttps://www.ebi.ac.uk/biomodels/model/download/MODEL1006230028?filename=MODEL1006230028.vcmlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL1006230028?filename=MODEL1006230028.mprimaryOK200Camille LaibeNon-curatedordinary differential equation modelL2V4https://www.ebi.ac.uk/biomodels/MODEL100623002820164396falseBioModelsSBMLModelsTabak2010 NeuronalNetworks2010MODEL1006230028Joël Tabak, Michael Mascagni, R BertramJoël Tabak20164396, 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. Dept. of Biological Science, BRF 206, Florida State Univ., Tallahassee, FL 32306, USA. joel@neuro.fsu.edulaibe@ebi.ac.ukEMBL-EBISpontaneous 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.Mechanism for the universal pattern of activity in developing neuronal networks.Tabak Joël J, Mascagni Michael M, Bertram Richard RfalseTabak2010_NeuronalNetworks This a model from the article: Mechanism for the universal pattern of activity in developing neuronal networks. Tabak J, Mascagni M, Bertram R. J Neurophysiol 2010 Feb 17; [Epub ahead of print] 20164396 , 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 so 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 then 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 demonstrates that networks with widely different architectures, different cell types and different functions, all operate according to the same general mechanism early in their development. This model was taken from the CellML repository and automatically converted to SBML. The original model was: Tabak J, Mascagni M, Bertram R. (2010) - version=1.0 The original CellML model was created by: Geoffrey Nunns gnunns1@jhu.edu The University of Auckland 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. 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 for more information. 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.. 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. 2010-06-252005-01-012010-06-23MODEL100623002820164396MODEL1006230028GO:00106449606BTO:0000938