Multitarget pharmacology for Dystonia in M1 (Neymotin et al 2016)

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" ... We developed a multiscale model of primary motor cortex, ranging from molecular, up to cellular, and network levels, containing 1715 compartmental model neurons with multiple ion channels and intracellular molecular dynamics. We wired the model based on electrophysiological data obtained from mouse motor cortex circuit mapping experiments. We used the model to reproduce patterns of heightened activity seen in dystonia by applying independent random variations in parameters to identify pathological parameter sets. ..."
1 . Neymotin SA, Dura-Bernal S, Lakatos P, Sanger TD, Lytton WW (2016) Multitarget Multiscale Simulation for Pharmacological Treatment of Dystonia in Motor Cortex. Front Pharmacol 7:157 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Molecular Network;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex L5/6 pyramidal GLU cell; Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell; Neocortex V1 interneuron basket PV GABA cell; Neocortex fast spiking (FS) interneuron; Neocortex spiking regular (RS) neuron; Neocortex spiking low threshold (LTS) neuron; Neocortex layer 4 neuron; Neocortex layer 2-3 interneuron; Neocortex layer 4 interneuron; Neocortex layer 5 interneuron; Neocortex layer 6a interneuron;
Channel(s): I A; I h; I_SERCA; Ca pump; I K,Ca; I Calcium; I L high threshold; I T low threshold; I N; I_KD; I M; I Na,t;
Gap Junctions:
Receptor(s): GabaA; GabaB; AMPA; mGluR;
Gene(s): HCN1;
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON; Python;
Model Concept(s): Oscillations; Activity Patterns; Beta oscillations; Reaction-diffusion; Calcium dynamics; Pathophysiology; Multiscale;
Implementer(s): Neymotin, Sam [Samuel.Neymotin at]; Dura-Bernal, Salvador [salvadordura at];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; Neocortex V1 interneuron basket PV GABA cell; Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell; GabaA; GabaB; AMPA; mGluR; I Na,t; I L high threshold; I N; I T low threshold; I A; I M; I h; I K,Ca; I Calcium; I_SERCA; I_KD; Ca pump; Gaba; Glutamate;
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from neuron import h
import numpy
from vector import *

nqsdel = h.nqsdel

# converts 2D numpy array into an NQS
#  NB: each row of the numpy array will be a Vector(column) in the NQS
def np2nqs (npa,names=[]):
  nrow,ncol = numpy.shape(npa)
  if nrow < 2:
    print "np2nqs ERRA: must have at least 2 rows!"
    return None
  nqo = NQS(nrow)
  for i in xrange(nrow):
    vrow = py2vec( npa[i] )
  for i in xrange(len(names)): # assign names
    nqo.s[i].s = names[i]
  return nqo

# converts nqs to numpy array
#  NB: each Vector(column) of nq is a column in the numpy array returned
def nqs2np (nq,sidx=None,eidx=None,Full=False):
  if Full: nq.tog("DB")
  nrow = int(nq.size())
  if sidx is None: sidx = 0
  if eidx is None: eidx = int(nq.m[0]) - 1
  ncol = eidx - sidx + 1
  npa = numpy.zeros( (nrow,ncol) )
  for i in xrange(sidx,eidx+1,1): npa[:,i-sidx] = numpy.array(nq.getcol(nq.s[i].s).to_python() )
  return npa

# convert nqs to a python dictionary
def nqs2pyd (nq,Full=False):
  d = {};
  if Full: nq.tog("DB");
  for i in xrange(int(nq.m[0])): d[nq.s[i].s] = vec2np(nq.getcol(nq.s[i].s))
  return d

# adds cols of nq2 to nq1
def NQAddToCols (nq1, nq2):
  cols = int(nq1.m[0])
  if cols != int(nq2.m[0]):
    print "unequal cols!"
    return False
  for i in xrange(cols):
  return True

# divide cols of nq by N
def NQDivCols (nq,N):
  cols = int(nq.m[0])
  for i in xrange(cols): nq.v[i].div(N)