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
                            
from util import *
from all2all import all2all
import heapq

def lpt(cx, npart):
  ''' from the list of (cx, gid) return a npart length list with each partition
      being a total_cx followed by a list of (cx, gid).
  '''
  cx.sort(key=lambda x:x[0], reverse=True)
  # initialize a priority queue for fast determination of current
  # partition with least complexity. The priority queue always has
  # npart items in it. At this time we do not care which partition will
  # be associated with which rank so a partition on the heap is just
  # (totalcx, [list of (cx, gid)]
  h = []
  for i in range(npart):
    heapq.heappush(h, (0.0, []))
  #each cx item goes into the current least complex partition
  for c in cx:
    lp = heapq.heappop(h) # least partition
    lp[1].append(c)
    heapq.heappush(h, (lp[0]+c[0], lp[1]))
  parts = [heapq.heappop(h) for i in range(len(h))]
  return parts

def statistics(parts):
  npart = len(parts)
  total_cx = 0
  max_part_cx = 0
  ncx = 0
  max_cx = 0
  for part in parts:
    ncx += len(part[1])
    total_cx += part[0]
    if part[0] > max_part_cx:
      max_part_cx = part[0]
    for cx in part[1]:
      if cx[0] > max_cx:
        max_cx = cx[0]
  avg_part_cx =total_cx/npart
  loadbal = 1.0
  if max_part_cx > 0.:
    loadbal = avg_part_cx/max_part_cx
  s = "loadbal=%g total_cx=%g npart=%d ncx=%d max_part_cx=%g max_cx=%g"%(loadbal,total_cx,npart,ncx,max_part_cx, max_cx)
  return s

if __name__ == '__main__':
  from util import serialize, finish
  for cx in ([(i, i) for i in range(10)],[]):
    print len(cx), ' complexity items ', cx
    pinfo = lpt(cx, 3)
    print len(pinfo), ' lpt partitions ', pinfo
    print statistics(pinfo)

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