Direct recruitment of S1 pyramidal cells and interneurons via ICMS (Overstreet et al., 2013)

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Accession:147460
Study of the pyramidal cells and interneurons recruited by intracortical microstimulation in primary somatosensory cortex. Code includes morphological models for seven types of pyramidal cells and eight types of interneurons, NEURON code to simulate ICMS, and an artificial reconstruction of a 3D slab of cortex implemented in MATLAB.
Reference:
1 . Overstreet CK, Klein JD, Helms Tillery SI (2013) Computational modeling of direct neuronal recruitment during intracortical microstimulation in somatosensory cortex. J Neural Eng 10:066016 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Axon;
Brain Region(s)/Organism:
Cell Type(s): Neocortex U1 L6 pyramidal corticalthalamic GLU cell; Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell; Neocortex bitufted interneuron;
Channel(s): I Na,p; I_Ks;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; MATLAB;
Model Concept(s): Intracortical Microstimulation;
Implementer(s): Overstreet, Cynthia [cynthiakoverstreet at gmail.com];
Search NeuronDB for information about:  Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell; Neocortex U1 L6 pyramidal corticalthalamic GLU cell; I Na,p; I_Ks;
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OverstreetEtAl2013
Interneuron
NEURON_code
README.txt
AXNODE.mod *
xtra.mod *
anat_type1.hoc
anat_type2.hoc
anat_type3.hoc
anat_type4.hoc
anat_type5.hoc
anat_type6.hoc
anat_type7.hoc
anat_type8.hoc
calcrxc.hoc *
field.hoc *
fixnseg.hoc *
initxcstim.hoc
interpxyz.hoc *
moveandstimtype1.hoc
moveandstimtype2.hoc
moveandstimtype3.hoc
moveandstimtype4.hoc
moveandstimtype5.hoc
moveandstimtype6.hoc
moveandstimtype7.hoc
moveandstimtype8.hoc
rigc.ses *
setpointers.hoc *
stim.hoc *
stimbipolar.hoc *
vrecc.ses
                            
/* Sets nseg in each section to an odd value
   so that its segments are no longer than 
     d_lambda x the AC length constant
   at frequency freq in that section.

   Be sure to specify your own Ra and cm before calling geom_nseg()

   To understand why this works, 
   and the advantages of using an odd value for nseg,
   see  Hines, M.L. and Carnevale, N.T.
        NEURON: a tool for neuroscientists.
        The Neuroscientist 7:123-135, 2001.
*/

// these are reasonable values for most models
freq = 100      // Hz, frequency at which AC length constant will be computed
d_lambda = 0.1


func lambda_f() { local i, x1, x2, d1, d2, lam
        if (n3d() < 2) {
                return 1e5*sqrt(diam/(4*PI*$1*Ra*cm))
        }
// above was too inaccurate with large variation in 3d diameter
// so now we use all 3-d points to get a better approximate lambda
        x1 = arc3d(0)
        d1 = diam3d(0)
        lam = 0
        for i=1, n3d()-1 {
                x2 = arc3d(i)
                d2 = diam3d(i)
                lam += (x2 - x1)/sqrt(d1 + d2)
                x1 = x2   d1 = d2
        }
        //  length of the section in units of lambda
        lam *= sqrt(2) * 1e-5*sqrt(4*PI*$1*Ra*cm)

        return L/lam
}

proc geom_nseg() {
  soma area(0.5) // make sure diam reflects 3d points
  forall { nseg = int((L/(d_lambda*lambda_f(freq))+0.9)/2)*2 + 1  }
}

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