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name: ainf
type: Parameter

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value: 0.01
name: n
type: Parameter

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value: 0.3
name: s
type: Parameter

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value: 0.05
name: ks
type: Parameter

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units: litre
size: 1.0
spatial dimensions: 3.0
type: Compartment

MODEL1006230028
parameter: 908589
curated: false
parameter: 908601
volume units: 290956
is described by: http://identifiers.org/pubmed/20164396
parameter: 908574
created:
  W3 CDTF: Fri Jun 25 13:38:30 BST 2010
time units: 324623
parameter: 908586
parameter: 908571
rule: 908569
area units: 291053
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notes: 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, Masc…
parameter: 908583
creator:
  ORG:
    orgname: Auckland Bioengineering Institute, The Auckland University
  n:
    given: Geoffrey
    family: Nunns
compartment: 908557
modified:
  W3 CDTF: Fri Jun 25 13:38:30 BST 2010
substance units: 178797
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modified:
  W3 CDTF: Fri Jun 25 13:38:30 BST 2010
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creator:
  ORG:
    orgname: Auckland Bioengineering Institute, The Auckland University
  n:
    given: Geoffrey
    family: Nunns
parameter: 908598
modified:
  W3 CDTF: Fri Jun 25 13:38:30 BST 2010
name: Tabak2010_NeuronalNetworks
length unit: 455845
creator:
  ORG:
    orgname: Auckland Bioengineering Institute, The Auckland University
  n:
    given: Geoffrey
    family: Nunns
type: SBMLModel
parameter: 908577
parameter: 908604
parameter: 908592
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type: Parameter

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name: noise
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908607
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value: 0.17
name: th0
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value: 250.0
name: taus
type: Parameter

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value: NaN
name: sinf
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908577
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value: 0.8
name: w
type: Parameter

908571
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value: 0.1
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type: Parameter

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