3D olfactory bulb: operators (Migliore et al, 2015)

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Accession:168591
"... Using a 3D model of mitral and granule cell interactions supported by experimental findings, combined with a matrix-based representation of glomerular operations, we identify the mechanisms for forming one or more glomerular units in response to a given odor, how and to what extent the glomerular units interfere or interact with each other during learning, their computational role within the olfactory bulb microcircuit, and how their actions can be formalized into a theoretical framework in which the olfactory bulb can be considered to contain "odor operators" unique to each individual. ..."
Reference:
1 . Migliore M, Cavarretta F, Marasco A, Tulumello E, Hines ML, Shepherd GM (2015) Synaptic clusters function as odor operators in the olfactory bulb. Proc Natl Acad Sci U S A 112:8499-504 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
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): AMPA; NMDA; Gaba;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON; Python;
Model Concept(s): Activity Patterns; Dendritic Action Potentials; Active Dendrites; Synaptic Plasticity; Action Potentials; Synaptic Integration; Unsupervised Learning; Sensory processing; Olfaction;
Implementer(s): 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; AMPA; NMDA; Gaba; I Na,t; I A; I K; Gaba; Glutamate;
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figure1eBulb3D
readme.html
ampanmda.mod *
distrt.mod *
fi.mod *
fi_stdp.mod *
kamt.mod *
kdrmt.mod *
naxn.mod *
ThreshDetect.mod *
.hg_archival.txt
all2all.py *
balance.py *
bindict.py
binsave.py
binspikes.py
BulbSurf.py
catfiles.sh
colors.py *
common.py
complexity.py *
custom_params.py *
customsim.py
destroy_model.py *
determine_connections.py
distribute.py *
falsegloms.txt
fixnseg.hoc *
g37e1i002.py
gidfunc.py *
Glom.py *
granule.hoc *
granules.py
grow.py
input-odors.txt *
loadbalutil.py *
lpt.py *
m2g_connections.py
mayasyn.py
mgrs.py
misc.py
mitral.hoc *
mkdict.py
mkmitral.py
modeldata.py *
multisplit_distrib.py *
net_mitral_centric.py
odordisp.py *
odors.py *
odorstim.py
params.py
parrun.py
realgloms.txt *
realSoma.py *
runsim.py
spike2file.hoc *
split.py *
util.py *
vrecord.py
weightsave.py *
                            
import params
import misc

ggid2pos = {}
pos2ggid = {}
Ngranule = 0
moves = set([ (0., 0., 0.) ])

# init granules
def initgranules():
  global Ngranule

  ggid2pos.clear()
  pos2ggid.clear()

  eup = misc.Ellipsoid(params.bulbCenter, params.somaAxis[0])
  edw = misc.Ellipsoid(params.bulbCenter, params.granAxisInf)

  for index in range(params.Nx_granule * params.Ny_granule * params.Nz_granule):
    pos = [ 0. ] * 3
    pos[0] = ((index % (params.Nx_granule * params.Ny_granule)) % params.Nx_granule) * params.grid_dim + params.granule_origin[0]
    pos[1] = int((index % (params.Nx_granule * params.Ny_granule)) / params.Nx_granule) * params.grid_dim + params.granule_origin[1]
    pos[2] = int(index / (params.Nx_granule * params.Ny_granule)) * params.grid_dim + params.granule_origin[2]

    
    if eup.normalRadius(pos) < 1. and edw.normalRadius(pos) > 1.:
      pos = tuple(pos)
      ggid = len(ggid2pos) + params.gid_granule_begin
      
      ggid2pos.update({ ggid:pos })
      pos2ggid.update({ pos:ggid })

  Ngranule = len(ggid2pos)



# init moves
def initmoves():
  depth = 1 #int(round(params.granule_field_radius / params.grid_dim))
  #print depth
  d = params.grid_dim

  oldmoves = set([ (0.,0.,0.) ])
  for i in range(depth):
    newmoves = set()
    for m in oldmoves:
      for dx in range(-d, d+1, d):
        for dy in range(-d, d+1, d):
          for dz in range(-d, d+1, d):
            p = (m[0]+dx, m[1]+dy, m[2]+dz)
            newmoves.add(p)
            moves.add(p)
    oldmoves = newmoves

# get granules under position
def get_below(p):

  def get_nears(q):
    npt = []
    for dx, dy, dz in moves:
      npt.append((q[0]+dx, q[1]+dy, q[2]+dz))
    return npt

  def get_p(x, u):
    dim = params.grid_dim
    o = params.granule_origin
    q = ()
    for i in range(3):
      q += (int(round((p[i] + x * u[i] - o[i])/dim)*dim+o[i]),)
    return q

  # scroll the line from points to the center
  u = misc.versor(params.bulbCenter, p)
  L = misc.distance(p, params.bulbCenter)
  dx = L / params.grid_dim

  nnpts = set()
  x = 0.
  while x <= L:
    q = get_p(x, u)
    if q not in nnpts:
      nnpts.update(get_nears(q))
    x += dx
    
  return nnpts

initmoves()
initgranules()