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
vis
bulbdef.py
bulbdict.py
bulbgui.py
bulbvis.py
cellreader.py
cellwriter.py
cfg27.py
dummysyns.txt
Eta.txt *
firing.py
geodist.py
geodist.txt
ggid2type.txt
gidfunc.py
glomdist.py
granules.py
granules.txt
graphmeat.py
growdef.py *
ipsc.py
ispkdata.py
Kod.txt *
misc.py
Nod.txt *
odors.py
odstim2.txt *
pad.txt *
realgloms.txt *
spikes.py
spikesreader.py
spk2gd.py
spk2weight.py
spkgraph.py
winflag.txt
                            
import misc
import bulbdef

odors = {}

def hill(eta, k, n, Fmax, cc): return Fmax/(1+((1+(0.0+k)/cc)/eta)**n)
def PG(val): return 0.5/(1+0.01*(1.0/val-1))

class odor:
  def __init__(self, name, eta, k):
    self.name = name
    self.eta = eta
    self.k = k
    
  def getORNs(self, cc):
    return [ hill(self.eta[i], self.k[i], 2, 25.0, cc) for i in range(bulbdef.Ngloms) ]

  def afterPG_1(self, cc):
    act = self.getORNs(cc)
    mu = misc.mean(act)
    for i in range(len(act)):
      act[i] -= mu
      if act[i] < 0:
        act[i] = 0
    return act
      
  def afterPG_2(self, cc):
    act = self.afterPG_1(cc)
    for i in range(len(act)):
      if act[i] > 0:
        act[i] -= PG(act[i])
        if act[i] < 0:
          act[i] = 0
    return act

    

def init(kfilename, etafilename):
  odors.clear()
  
  import fileinput
  
  for l in fileinput.input(etafilename):
    data = l.split('\t')
    odors.update({ data[0]: odor(data[0], [ float(x) for x in data[1:] ], []) })
    
  for l in fileinput.input(kfilename):
    data = l.split('\t')
    odors[data[0]].k = [ float(x) for x in data[1:] ]
    

init('Kod.txt', 'Eta.txt')

if __name__ == '__main__':
  o = odors['Mint']

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