Interaction of leak and IMI conductance on the STG over broad temperature range (Stadele et al 2015)

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Accession:184404
The ZIP file contains a Hodgkin-Huxley based circuit model and the simulation environment MadSim used to study the interaction of leak and IMI on the gastric mill network of the crab (Cancer borealis) as represented in (C. Städele, S. Heigele and W. Stein, 2015) MadSim, the simulation environment used for this study, is freeware and included in the package.
Reference:
1 . Stein W, Straub O, Ausborn J, Mader W, Wolf H (2008) Motor pattern selection by combinatorial code of interneuronal pathways. J Comput Neurosci 25:543-61 [PubMed]
2 . Ausborn J, Stein W, Wolf H (2007) Frequency control of motor patterning by negative sensory feedback. J Neurosci 27:9319-28 [PubMed]
3 . Städele C, Heigele S, Stein W (2015) Neuromodulation to the Rescue: Compensation of Temperature-Induced Breakdown of Rhythmic Motor Patterns via Extrinsic Neuromodulatory Input. PLoS Biol 13:e1002265 [PubMed]
4 . Daur N, Diehl F, Mader W, Stein W (2012) The stomatogastric nervous system as a model for studying sensorimotor interactions in real-time closed-loop conditions. Front Comput Neurosci 6:13 [PubMed]
Citations  Citation Browser
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Stomatogastric ganglion;
Cell Type(s): Stomatogastric Ganglion (STG) Lateral Gastric (LG) cell;
Channel(s): I MI;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s): CabTRP 1a;
Simulation Environment: MadSim;
Model Concept(s): Temporal Pattern Generation; Invertebrate; Neuromodulation;
Implementer(s):
Search NeuronDB for information about:  I MI; CabTRP 1a;
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StaedeleEtAl2015
madSim 6.11
parameter
standard-kanaele
! bloss oinr.knl *
1 kanal.knl *
caK-Kanal.kca
dialog texte de.txt
dialog texte en.txt
form parameter.txt
form parameter.xls
GB.channel *
GB.channel.description *
GB.channel.description.doc *
gb-parameter.txt *
gb-parameter.xls *
HH.channel *
HH.channel.description *
HH.channel.description.doc *
ioTabelle langsam.txt *
ioTabelle normal.txt *
ioTabelle.txt *
izhikevich-typen.txt *
izhikevich-typen.txt.orig *
meldung texte de.txt
meldung texte en.txt
meldung texte en.txt.bak
neuronParameter.xls
ONOFF.channel *
ONOFF.channel.description *
ONOFF.channel.description.doc *
ONOFF.NEU *
reizFuerServerBetrieb kurz.txt *
reizFuerServerBetrieb original.txt *
reizFuerServerBetrieb.txt *
STANDARD.CA *
STANDARD.GEN *
STANDARD.K *
STANDARD.NA *
STANDARD.NEU *
STANDARD.ON *
standardap 2000ms.TXT *
standardap 200ms.txt *
standardap original.TXT *
standardap.txt *
swim example.txt *
SWIM.channel *
SWIM.channel.description *
SWIM.channel.description.doc *
tooltip texte de.txt
tooltip texte en.txt
tooltip texte en.txt.bak
userDef SWIM kanal original.txt *
                            
k * (W + exp((E - V0) / s)) V0 positiv;1
k * (W + exp((E - V0) / s)) V0 negativ;2
k * (W + exp((E - V0) / -s)) V0 negativ;3
k * (W + exp((E - V0) / -s)) V0 positiv;4
(W + exp((E - V0) / s)) / k;5
(W + exp((E - V0) / -s)) / k;6
k * (E - V0) / (W - exp((E - V0) / s));12
k * (E - V0) / (W - exp((E - V0) / -s));13
k * (V0 - E) / (W - exp((E - V0) / s));14
k * (V0 - E) / (W - exp((E - V0) / -s));15
k + (k / V0 * E) / (W + exp((E + V0) / s));21
k / (W + exp((E - V0) / s));22
k / (W - exp((E - V0) / -s));23
k / (W + exp((E - V0) / -s));24
1 / (W + exp((E - V0) / s));25
1 / (W + exp((E - V0) / -s));26
k / (W - exp((E - V0) / s));27
k / W;28
k / (W + exp((E + V0) / s));31
1.0 / W;32
k;101
F - k / (W + exp((E - V0) / -s));102 - 105 (initialisiert fuer na-kanal h und m)
?m-unendlich fuer na-kanal das entspricht alpha (aktivierung m-tor)?:
k / (W + exp((-E_POT[0] - 0.0255) / 0.00529));102
?tau-m fuer na-kanal  das entspricht beta (deaktivierung m-tor)?
 (1.32 - (k / (W + exp((-0.12 - E_POT[0]) / 0.025))));103
?h-unendlich	fuer na-kanal entspricht alpha (aktivierung h-tor)?
k / (W + exp((E_POT[0] + 0.0489) / 0.00518));104
?tau-h fuer na-kanal das entspricht beta (deaktivierung h-tor)?
 (0.67 * (k / (W + exp((-0.0629 - E_POT[0]) / 0.01))) * (1.5 + A / (W + exp((E_POT[0] + 0.0349) / 0.0036))));105