A model of the femur-tibia control system in stick insects (Stein et al. 2008)

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Accession:118092
We studied the femur-tibia joint control system of the insect leg, and its switch between resistance reflex in posture control and "active reaction" in walking. The "active reaction" is basically a reversal of the resistance reflex. Both responses are elicited by the same sensory input and the same neuronal network (the femur-tibia network). The femur-tibia network was modeled by fitting the responses of model neurons to those obtained in animals. Each implemented neuron has a physiological counterpart. The strengths of 16 interneuronal pathways that integrate sensory input were then assigned three different values and varied independently, generating a database of more than 43 million network variants. The uploaded version contains the model that best represented the resistance reflex. Please see the README for more help. We demonstrate that the combinatorial code of interneuronal pathways determines motor output. A switch between different behaviors such as standing to walking can thus be achieved by altering the strengths of selected sensory integration pathways.
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]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Neuron or other electrically excitable cell; Synapse;
Brain Region(s)/Organism:
Cell Type(s): Stick insect nonspiking interneuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: MadSim;
Model Concept(s): Detailed Neuronal Models; Invertebrate; Synaptic Integration;
Implementer(s): Mader, Wolfgang [wolfgang.mader at uni-ulm.de];
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madSim4.5
parameter
Rueckkopplungs-Objekte-Dateien
! bloss oinr.knl *
1 kanal.knl *
akt+inakt.knl
dialog texte de.txt
dialog texte en.txt
dialog texte en_alt.txt
Diffgleichungen_in_madSim.pdf
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 test.txt
ioTabelle.txt *
izhikevich-typen.txt *
izhikevich-typen.txt.orig *
meldung texte de.txt
meldung texte en.txt
neuronParameter.xls
ONOFF.channel *
ONOFF.channel.description *
ONOFF.channel.description.doc *
ONOFF.NEU *
reizFuerServerBetrieb kurz.txt *
reizFuerServerBetrieb original.txt *
reizFuerServerBetrieb.txt *
Rueckkopplungs-Objekte.htm
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
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userDef SWIM kanal original.txt *
userDef SWIM kanal.knl
                            
anzahl user-SWIM-kanaele=1
neuron 0 user-SWIM-kanal 0 gleichgewichtspotential=-0.01
neuron 0 user-SWIM-kanal 0 leitfaehigkeit=3.71e-008
neuron 0 user-SWIM-kanal 0 power=1
neuron 0 user-SWIM-kanal 0 torvariable initialwert=0.0457132
neuron 0 user-SWIM-kanal 0 form alpha=24
neuron 0 user-SWIM-kanal 0 rate alpha=1
neuron 0 user-SWIM-kanal 0 V0 alpha=-0.07
neuron 0 user-SWIM-kanal 0 schrittweite alpha=0.007
neuron 0 user-SWIM-kanal 0 form beta=5
neuron 0 user-SWIM-kanal 0 rate beta=0.33
neuron 0 user-SWIM-kanal 0 V0 beta=-0.11
neuron 0 user-SWIM-kanal 0 schrittweite beta=0.013
neuron 0 user-SWIM-kanal 0 berechnungsmodus fuer A und B=2
neuron 0 user-SWIM-kanal 0 bezeichnung=iH (postinhibitory rebound)
neuron 0 user-SWIM-kanal 0 ende=1