Muscle spindle feedback circuit (Moraud et al, 2016)

 Download zip file 
Help downloading and running models
Accession:189786
Here, we developed a computational model of the muscle spindle feedback circuits of the rat ankle that predicts the interactions between Epidural Stimulation and spinal circuit dynamics during gait.
Reference:
1 . Moraud EM, Capogrosso M, Formento E, Wenger N, DiGiovanna J, Courtine G, Micera S (2016) Mechanisms Underlying the Neuromodulation of Spinal Circuits for Correcting Gait and Balance Deficits after Spinal Cord Injury. Neuron 89:814-28 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Spinal motoneuron;
Cell Type(s): Spinal cord motor neuron slow twitch;
Channel(s): I K; I K,Ca; I Na,p; I Sodium;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; Python;
Model Concept(s): Sensory processing; Neuromodulation;
Implementer(s): Capogrosso, Marco ; Formento, Emanuele ;
Search NeuronDB for information about:  I Na,p; I K; I K,Ca; I Sodium;
//SYNAPTIC CONNECTIONS EXTENSORS - IPSILATERAL

objref r
r = new Random()
//time tau random delay based on diameter variance (20%)
objref taur
taur =new Random()
{taur.normal(0,0.03)}


//SYNAPSES BETWEEN IAf AND MOTONEURONS
//EACH MOTONEURON IS CONNECTED TO ALL Ia FIBERS
{r.uniform(0, nIAf-1)}
for i=nCell, nCell+nMN-1{
    if (!pc.gid_exists(i)) { continue }
    target = pc.gid2cell(i)//target cell
    for j=0, nIAf-1{
        syn = target.synlist.object(j)
        nc = pc.gid_connect(j+nMN+nCell, syn)
        nc.weight = hi_motor_S
        nc.delay = Ia+taur.normal(0,0.03)
        nclist_hi_motor_S.append(nc)
    }
}

//SYNAPSES BETWEEN IAf AND IAInt
{r.uniform(0, nIAf-1)}
for i=nMN+nIAf+nCell, nMN+nIAf+nIAint+nCell-1{
	if (!pc.gid_exists(i)) { continue }
	target = pc.gid2cell(i)//target cell
	for j=0, FromAll_IAf_ToOne_IAint-1{
		nc = pc.gid_connect(nMN+nCell+int(r.repick()), target)
		nc.weight = hi
    	nc.delay = Ia+taur.normal(0,0.03)
        nclist_hi.append(nc)
    }
}

//SYNAPSES BETWEEN EXIN AND MOTONEURONS
{r.uniform(0, nEXIN-1)}
for i=nCell, nCell+nMN-1{
    if (!pc.gid_exists(i)) { continue }
    target = pc.gid2cell(i)//target cell
    for j=0, FromAll_EXIN_ToOne_MN-1{
        syn = target.synlist.object(j+nIAf)
        nc = pc.gid_connect(nCell+nIAf+nMN+nIAint+nIIf+int(r.repick()), syn)
        nc.weight = hi_motor_Ex
        nc.delay = tausyn
        nclist_hi_motor_Ex.append(nc)
    }
}

//CONNESSIONE TRA  IIf AND EXIN
{r.uniform(0, nIIf-1)}
for i=nCell+nIAf+nMN+nIAint+nIIf, nCell+nIAf+nMN+nIAint+nIIf+nEXIN-1{
    if (!pc.gid_exists(i)) { continue }
    target = pc.gid2cell(i)//target cell
    for j=0, FromAll_IIf_ToOne_EXIN-1{
        nc = pc.gid_connect(nCell+nIAf+nMN+nIAint+int(r.repick()), target)
        nc.weight = hi
        nc.delay = II+taur.normal(0,0.03)
        nclist_hi.append(nc)
    }
}

//CONNESSIONE TRA  IIf AND IAint
{r.uniform(0, nIIf-1)}
for i=nCell+nMN+nIAf, nCell+nMN+nIAf+nIAint-1{
    if (!pc.gid_exists(i)) { continue }
    target = pc.gid2cell(i)//target cell
    for j=0, FromAll_IIf_ToOne_IAint-1{
        nc = pc.gid_connect(nCell+nIAf+nMN+nIAint+int(r.repick()), target)
        nc.weight = hi
        nc.delay = II+taur.normal(0,0.03)
        nclist_hi.append(nc)
    }
}


Loading data, please wait...