MODEL1006230028
type: SBMLModel
created:
  W3 CDTF: Fri Jun 25 13:38:30 BST 2010
creator:
  ORG:
    orgname: Auckland Bioengineering Institute, The Auckland University
  n:
    given: Geoffrey
    family: Nunns
parameter: 908589
curated: false
parameter: 908601
volume units: 290956
is described by: http://identifiers.org/pubmed/20164396
parameter: 908574
time units: 324623
parameter: 908586
parameter: 908571
rule: 908569
creator:
  ORG:
    orgname: Auckland Bioengineering Institute, The Auckland University
  n:
    given: Geoffrey
    family: Nunns
area units: 291053
creator:
  ORG:
    orgname: Auckland Bioengineering Institute, The Auckland University
  n:
    given: Geoffrey
    family: Nunns
rule: 908561
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
rule: 908566
compartment: 908557
substance units: 178797
parameter: 908580
parameter: 908607
parameter: 908595
rule: 908562
parameter: 908598
modified:
  W3 CDTF: Fri Jun 25 13:38:30 BST 2010
name: Tabak2010_NeuronalNetworks
length unit: 455845
modified:
  W3 CDTF: Fri Jun 25 13:38:30 BST 2010
modified:
  W3 CDTF: Fri Jun 25 13:38:30 BST 2010
parameter: 908577
parameter: 908604
parameter: 908592
rule: 908563

908589
type: Parameter
constant: true
value: 0.0
name: wi

908601
type: Parameter
constant: true
value: 250.0
name: taus

908574
type: Parameter
constant: false
value: NaN
name: ainf

908586
type: Parameter
constant: true
value: 0.01
name: n

908571
type: Parameter
constant: false
value: 0.1
name: a

908583
type: Parameter
constant: true
value: 0.17
name: th0

908557
type: Compartment
constant: true
units: litre
size: 1.0
spatial dimensions: 3.0

908580
type: Parameter
constant: true
value: -0.05
name: ka

908607
type: Parameter
constant: true
value: 0.2
name: ths

908595
type: Parameter
constant: false
value: 0.3
name: s

908598
type: Parameter
constant: false
value: NaN
name: sinf

908577
type: Parameter
constant: true
value: 0.8
name: w

908604
type: Parameter
constant: true
value: 0.05
name: ks

908592
type: Parameter
constant: false
value: NaN
name: noise