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
                            
# -*- coding: cp1252 -*-

# bulb spatial definition

bulbCenter = [ 50., 1275., 0. ]
bulbAxis = [ 2100., 2800., 2100.]
glomAxis = [ bulbAxis[i]+300 for i in range(3) ]
granAxisUp = [ bulbAxis[i]-700 for i in range(3) ]
granAxisDw = [ bulbAxis[i]-1500 for i in range(3) ]

GLOM_RADIUS = 50.



try:
    
    from neuron import h
    h.celsius = 35
    
except:
    pass


filename = 'cfg27'

def load_params(_filename):
    global filename
    global stream_last
    global stream_orn_w
    global stream_orn_act
    
    filename = _filename
    try:
      module = __import__(filename)
      globals().update(vars(module))
      stream_last = stream_last_fixed+stream_ods_shift
      stream_orn_w = stream_last; stream_last += 6
      stream_orn_act = stream_last; stream_last += 1
      glom2blanes = []
      with open('blanes_exc_conn.txt', 'r') as fi:
        l = fi.readline()
        while l:
          tk = l.split()
          glom2blanes.append((int(tk[0]), int(tk[1])))
          l = fi.readline()
    except:
      print 'error during params import'

from copy import copy
from math import pi, sqrt

use_fi_stdp = False # FastInhibSTDP vs FastInhib


#plasticity
mc_fi_tau1 = 1
mc_fi_tau2 = 5

mt_fi_tau1 = 1
mt_fi_tau2 = 5

gran_voxel = 17  #19
gran_connect_radius = 41.
mean_synapse_invl = 10.0



glomRealCoords = []
with open('realgloms.txt', 'r') as fi:
    line = fi.readline()
    while line:
        token = line.split()
        glomRealCoords.append([ float(token[0]), float(token[1]), float(token[2]) ])
        line = fi.readline()


glomRealCoords = glomRealCoords[:127]
# cell numbers    
Ngloms = len(glomRealCoords) # glomeruli

# mitral
gid_mitral_begin = 0
Nmitral_per_glom = 5 # mitral per glomerolus
Nmitral = Ngloms * Nmitral_per_glom

# middle tufted
gid_mtufted_begin = gid_mitral_begin+Nmitral
Nmtufted_per_glom = 2*Nmitral_per_glom # twice than mitral!
Nmtufted = Ngloms * Nmtufted_per_glom

gid_granule_begin = gid_mtufted_begin + Nmtufted
import granules
granules.init(bulbCenter, granAxisUp, granAxisDw, gran_voxel, gid_granule_begin)
Ngranule = len(granules.ggid2pos)

gid_blanes_begin = gid_granule_begin+Ngranule
NBlanes_per_glom = 3
NBlanes = Ngloms*NBlanes_per_glom
gid_mbs_begin = gid_blanes_begin+NBlanes
gid_bc2gc_begin = gid_mbs_begin+Nmtufted*NBlanes

# reciprocal synapse
gid_mgrs_begin = gid_bc2gc_begin+NBlanes*Ngranule
if gid_mgrs_begin % 2 != 0:
  gid_mgrs_begin += 1

# Random123 secondary stream identifiers
# note: the primary stream index is the "gid" which is ordered as
# Nmitral, Ngranule, synapses
# Not all secondary identifiers are used for all types
stream_last=1
stream_soma = stream_last; stream_last += 1
stream_dend = stream_last; stream_last += 1
stream_apic = stream_last; stream_last += 1
stream_tuft = stream_last; stream_last += 1
stream_latdendconnect = stream_last; stream_last += 1000 #allows per dendrite stream
stream_dsac = stream_last; stream_last += Nmitral+Nmtufted
stream_granule_diam = stream_last; stream_last += 1
stream_granule_type = stream_last; stream_last += 1
stream_granule_dsac = stream_last; stream_last += 8
stream_gap_junction = stream_last; stream_last += 1
stream_blanes = stream_last+Ngranule+Nmtufted; stream_last += 1
stream_blanes_conn = stream_last+Ngranule; stream_last += 1
stream_last_fixed = stream_last

# for the odorstim
stream_last = stream_last_fixed
stream_orn_w = stream_last; stream_last += 6
stream_orn_act = stream_last; stream_last += 1
stream_sniff_delay = stream_last; stream_last += 1


granule_rn_mean = 603.2
granule_rn_std = 363.4/10.

#granule_rn_mean = 728.7
#granule_rn_std = 293.06/10


def ranstream(id1, id2):
    r = h.Random()
    r.Random123(id1, id2)
    return r




# learning
orn_g_mc_std = 1e-4
orn_g_mt_std = 1e-4


if False:
  orn_g_mc_baseline = 0
  orn_g_mc_max = 15e-3
  orn_g_mt_baseline = 0
  orn_g_mt_max = 15e-3
else:
  orn_g_mc_baseline = 5e-4
  orn_g_mc_max = 4e-3
  orn_g_mt_baseline = 5e-4
  orn_g_mt_max = 10e-3


scrambled_weights = False
gap_junctions_active = True
glomerular_layer = 2


load_params('cfg27')

gc_type3_prob=0

vclamp = []


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