3D model of the olfactory bulb (Migliore et al. 2014)

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Accession:151681
This entry contains a link to a full HD version of movie 1 and the NEURON code of the paper: "Distributed organization of a brain microcircuit analysed by three-dimensional modeling: the olfactory bulb" by M Migliore, F Cavarretta, ML Hines, and GM Shepherd.
Reference:
1 . Migliore M, Cavarretta F, Hines ML, Shepherd GM (2014) Distributed organization of a brain microcircuit analyzed by three-dimensional modeling: the olfactory bulb. Front Comput Neurosci 8:50 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Channel/Receptor; Dendrite;
Brain Region(s)/Organism: Olfactory bulb;
Cell Type(s): Olfactory bulb main mitral cell; Olfactory bulb main interneuron granule MC cell;
Channel(s): I Na,t; I A; I K;
Gap Junctions:
Receptor(s): NMDA; Glutamate; Gaba;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Pattern Recognition; Activity Patterns; Bursting; Temporal Pattern Generation; Oscillations; Synchronization; Active Dendrites; Detailed Neuronal Models; Synaptic Plasticity; Action Potentials; Synaptic Integration; Unsupervised Learning; Olfaction;
Implementer(s): Hines, Michael [Michael.Hines at Yale.edu]; Migliore, Michele [Michele.Migliore at Yale.edu]; Cavarretta, Francesco [francescocavarretta at hotmail.it];
Search NeuronDB for information about:  Olfactory bulb main mitral cell; Olfactory bulb main interneuron granule MC cell; NMDA; Glutamate; Gaba; I Na,t; I A; I K;
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bulb3d
readme.html
ampanmda.mod *
distrt.mod *
fi.mod *
kamt.mod *
kdrmt.mod *
naxn.mod *
ThreshDetect.mod *
all2all.py *
balance.py *
bindict.py
BulbSurf.py
colors.py *
common.py
complexity.py *
custom_params.py *
customsim.py
destroy_model.py *
determine_connections.py
distribute.py *
fig7.py
fixnseg.hoc *
getmitral.py
gidfunc.py *
glom.py
granule.hoc *
granules.py
input-odors.txt *
loadbalutil.py *
lpt.py *
mayasyn.py
mgrs.py
misc.py
mitral.hoc *
mitral_dend_density.py
mkmitral.py
modeldata.py *
multisplit_distrib.py *
net_mitral_centric.py
odordisp.py *
odors.py *
odorstim.py
params.py
parrun.py
realgloms.txt *
runsim.py
split.py *
util.py *
weightsave.py *
                            
from util import *

import fileinput
from common import getmodel

def weight_load(filename):
  model = getmodel()
  for l in fileinput.input(filename):
    tk = l.split()
    gid = int(tk[0])
    s = int(tk[1])
    
    # inhib check    
    if gid % 2 != 0:
      gid += 1
      inhib = True
    else:
      inhib = False

    # has key
    if model.mgrss.has_key(gid):
      rsyn = model.mgrss[gid]
      
      if inhib and rsyn.gd2fi:
        rsyn.gd2fi.weight[1] = s
      elif not inhib and rsyn.md2ampanmda:
        rsyn.md2ampanmda.weight[1] = s

  fileinput.close()
        
    
#mg_dict_filename = 'mg_dict.txt'
def weight_file(prefix):
  wtime = h.startsw()
  mingroupsize = max(nhost/64, 1)
  ng = nhost/mingroupsize
  for r in group_serialize(ng):
    name = prefix + '.' + str(r[0])
    
    if r[1]:
      f = open(name, 'a')
      #fdic = open(mg_dict_filename + '.' + str(r[0]), 'a')
    else:
      f = open(name, 'w')
      #fdic = open(mg_dict_filename + '.' + str(r[0]), 'w')
      
    vs = getmodel().mgrss.values
    for rs in vs():
      s = rs.wstr()
      f.write(s)
      #sdic = rs.mg_dic_str()
      #fdic.write(sdic)
    f.close()
    #fdic.close()
    
  if rank == 0 : print "weight_files %s.[0:%d] write time %g s" % (prefix, ng, h.startsw()-wtime)