Parallel odor processing by mitral and middle tufted cells in the OB (Cavarretta et al 2016, 2018)

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Accession:240116
"[...] experimental findings suggest that MC and mTC may encode parallel and complementary odor representations. We have analyzed the functional roles of these pathways by using a morphologically and physiologically realistic three-dimensional model to explore the MC and mTC microcircuits in the glomerular layer and deeper plexiform layers. [...]"
References:
1 . Cavarretta F, Burton SD, Igarashi KM, Shepherd GM, Hines ML, Migliore M (2018) Parallel odor processing by mitral and middle tufted cells in the olfactory bulb. Sci Rep 8:7625 [PubMed]
2 . Cavarretta F, Marasco A, Hines ML, Shepherd GM, Migliore M (2016) Glomerular and Mitral-Granule Cell Microcircuits Coordinate Temporal and Spatial Information Processing in the Olfactory Bulb. Front Comput Neurosci 10:67 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Olfactory bulb;
Cell Type(s): Olfactory bulb main tufted middle GLU cell; Olfactory bulb main interneuron granule MC GABA cell; Olfactory bulb main interneuron granule TC GABA cell; Olfactory bulb (accessory) mitral cell; Olfactory bulb main tufted cell external; Olfactory bulb short axon cell;
Channel(s): I A; I Na,t; I_Ks; I K;
Gap Junctions: Gap junctions;
Receptor(s): AMPA; GabaA; NMDA;
Gene(s):
Transmitter(s): Glutamate; Gaba;
Simulation Environment: NEURON;
Model Concept(s): Action Potentials; Action Potential Initiation; Active Dendrites; Long-term Synaptic Plasticity; Synaptic Integration; Synchronization; Pattern Recognition; Spatio-temporal Activity Patterns; Temporal Pattern Generation; Sensory coding; Sensory processing; Olfaction;
Implementer(s): Cavarretta, Francesco [francescocavarretta at hotmail.it]; Hines, Michael [Michael.Hines at Yale.edu];
Search NeuronDB for information about:  Olfactory bulb main interneuron granule MC GABA cell; Olfactory bulb main tufted middle GLU cell; Olfactory bulb main interneuron granule TC GABA cell; GabaA; AMPA; NMDA; I Na,t; I A; I K; I_Ks; Gaba; Glutamate;
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modeldb-bulb3d
sim
ampanmda.mod
distrt.mod *
fi.mod
fi_stdp.mod *
gap.mod
Gfluct.mod
kamt.mod
kdrmt.mod
ks.mod
naxn.mod
orn.mod
ThreshDetect.mod *
all.py
all2all.py *
assembly.py
balance.py *
bindict.py
binsave.py
binspikes.py
blanes.hoc
blanes.py
blanes_exc_conn.txt
blanes6.dic
bulb3dtest.py
cancel.py
catfiles.sh
cellreader.py
cellwriter.py
cfg27.py
common.py
complexity.py *
convertdic.py
destroy_model.py
determine_connections.py
distribute.py *
dsac.py
Eta.txt *
fillgloms.py
fixnseg.hoc *
g_conn_stats.py
gapjunc.py
gen_weights.py
geodist.py
geodist.txt
getmitral.py
gidfunc.py
GJ.py
gj_nrn.hoc
Glom.py *
granule.hoc
granules.py
graphmeat.py
grow.py
growdef.py *
growout.py
job
Kod.txt *
lateral_connections.py
loadbalutil.py *
lpt.py *
mcgrow.py
MCrealSoma.py *
mgrs.py
misc.py
mitral.hoc
mkassembly.py
mkmitral.py
modeldata.py
mtgrow.py
MTrealSoma.py
MTrealSoma2.py
mtufted.hoc
multisplit_distrib.py
net_mitral_centric.py
Nod.txt *
odors.py
odorstim.py
odstim2.txt *
pad.txt *
params.py
parrun.py
pathdist.py
realgloms.txt *
runsim.py
spike2file.hoc *
spk2weight.py
split.py
subsetsim.py
test_complexity.py
txt2bin.py
util.py *
vrecord.py
weightsave.py
                            
radius_factor = 2.0
from mayavi import mlab
from mayavi.mlab import figure
fig = figure(bgcolor=(0,0,0))

from tvtk.api import tvtk
from misc import Spherical as sph, convert as convdir
from numpy import pi, sin, cos

# render semaphore
class render:
  __count = 0

  @staticmethod
  def down():
    render.__count += 1
    fig.scene.disable_render = True
    
  @staticmethod
  def up():
    if render.__count > 0:
      render.__count -= 1
      
    if render.__count == 0:
      fig.scene.disable_render = False  

def get_line(a, b):
  src = tvtk.LineSource(point1=a, point2=b)
  mapper = tvtk.PolyDataMapper(input=src.output)
  actor = tvtk.Actor(mapper=mapper)
  fig.scene.add_actor(actor)
  return actor

def get_trunkcone(b, a):
  phi_base, theta_base = sph.to(a, b)[1:]

  quads = tvtk.CellArray() #vtk.vtkCellArray()
  points = tvtk.Points()   #vtk.vtkPoints()
  Nface = 3
  for i in range(Nface+1):
    # rotate
    phi, theta = convdir((i%Nface)*2*pi/Nface, pi*0.5, phi_base, theta_base)

    # generate  new points
    p = tuple(sph.xyz(a[3]*0.5*radius_factor, phi, theta, a[:3]))
    q = tuple(sph.xyz(b[3]*0.5*radius_factor, phi, theta, b[:3]))

    # insert points
    points.append(p)
    points.append(q)

    if i >= 1:
      # create a face            
      quad = tvtk.Quad()
      n = points.number_of_points-1

      quad.point_ids.set_id(0, n-3) # p
      quad.point_ids.set_id(1, n-2) # q
      quad.point_ids.set_id(2, n)   # q
      quad.point_ids.set_id(3, n-1) # p

      # insert the new face
      quads.insert_next_cell(quad)

  # create the actor
  polydata = tvtk.PolyData(points=points, polys=quads)
  mapper = tvtk.PolyDataMapper(input=polydata)
  actor = tvtk.Actor(mapper=mapper)
  fig.scene.add_actor(actor)
  return actor

def get_cone(base, radius, v):
  if type(base) != tuple:
    base = tuple(base)
  if type(v) != tuple:
    v = tuple(v)
  src = tvtk.ConeSource(center=base, radius=radius*radius_factor, height=radius, direction=v, resolution=20)
  mapper = tvtk.PolyDataMapper(input=src.output)
  actor = tvtk.Actor(mapper=mapper)
  fig.scene.add_actor(actor)
  return actor

def get_sphere(p, radius, res=8):
  if type(p) != tuple:
    p = tuple(p)
  src = tvtk.SphereSource(center=p, radius=radius, phi_resolution=res, theta_resolution=res)
  mapper = tvtk.PolyDataMapper(input=src.output)
  actor = tvtk.Actor(mapper=mapper)
  fig.scene.add_actor(actor)
  return actor

def get_many_spheres(pts, radius, res=32):
  actors = []
  render.down()
  for p in pts:
    actors.append(get_sphere(p, radius, resolution=res))
  render.up()
  return actors

def get_ellipsoid(center, axis, res=8):
  dphi = 2*pi/res
  dtheta = pi/res
  [phi, theta] = numpy.mgrid[0:2*pi+dphi:dphi, 0:pi+dtheta:dtheta]
  x = axis[0]*0.5*cos(phi)*sin(theta)+center[0]
  y = axis[1]*0.5*sin(phi)*sin(theta)+center[1]
  z = axis[2]*0.5*cos(theta)+center[2]
  return mlab.mesh(x, y, z, color=(0,0,0)).actor

def get_mesh(x, y, z):
  return mlab.mesh(x, y, z, color=(0,0,0)).actor

def remove_actor(actor):
  fig.scene.remove_actor(actor)
  

def start():
  mlab.show()

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