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
                            
import params
import misc
from math import pi 

# 3d coords to ellipsoidal
def p2e(p, axis=params.bulbAxis):
  from copy import copy  
  p = copy(p)
  for i in range(3):
    p[i] -= params.bulbCenter[i]
    p[i] /= axis[i] / 2.0
  phi, theta = misc.Spherical.to(p)[1:]
  phi %= 2*pi
  return phi, theta

def e2p(phi, theta, axis=params.bulbAxis):
  p = misc.Spherical.xyz(1, phi, theta)
  for i in range(3):
    p[i] *= axis[i]/2.0
    p[i] += params.bulbCenter[i]
  return p

def geodist(q, p):
  phiq, thetaq = p2e(q)
  phip, thetap = p2e(p)

  if phiq < phip:
    def swap(a, b):
      return b, a
  
    phip, phiq = swap(phip, phiq)
    thetap, thetaq = swap(thetap, thetaq)

  if phiq > phip and (phiq - phip) % (2 * pi) > pi:
    phiq = -(2 * pi - phiq)

  def pt(t):
    phi = (phiq-phip)*t+phip
    theta = (thetaq-thetap)*t+thetap
    return e2p(phi, theta)

  t = 0.0
  
  dt = (1.0/360*2*pi)/max([abs(phiq-phip), abs(thetaq-thetap)])
  tot = 0.0
  a = pt(0)
  seq = [a]
  while t < 1:
    t += dt
    b = pt(t)
    seq.append(b)
    tot += misc.distance(a, b)
    a = b
  return tot, seq

def glomdist(i, j):
  gl1 = params.glomRealCoords[i]
  gl2 = params.glomRealCoords[j]
  return geodist(gl1, gl2)[0]
  


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