Olfactory bulb microcircuits model with dual-layer inhibition (Gilra & Bhalla 2015)

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Accession:153574
A detailed network model of the dual-layer dendro-dendritic inhibitory microcircuits in the rat olfactory bulb comprising compartmental mitral, granule and PG cells developed by Aditya Gilra, Upinder S. Bhalla (2015). All cell morphologies and network connections are in NeuroML v1.8.0. PG and granule cell channels and synapses are also in NeuroML v1.8.0. Mitral cell channels and synapses are in native python.
Reference:
1 . Gilra A, Bhalla US (2015) Bulbar microcircuit model predicts connectivity and roles of interneurons in odor coding. PLoS One 10:e0098045 [PubMed]
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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 mitral GLU cell; Olfactory bulb main interneuron periglomerular GABA cell; Olfactory bulb main interneuron granule MC GABA cell;
Channel(s): I A; I h; I K,Ca; I Sodium; I Calcium; I Potassium;
Gap Junctions:
Receptor(s): AMPA; NMDA; Gaba;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: Python; MOOSE/PyMOOSE;
Model Concept(s): Sensory processing; Sensory coding; Markov-type model; Olfaction;
Implementer(s): Bhalla, Upinder S [bhalla at ncbs.res.in]; Gilra, Aditya [aditya_gilra -at- yahoo -period- com];
Search NeuronDB for information about:  Olfactory bulb main mitral GLU cell; Olfactory bulb main interneuron periglomerular GABA cell; Olfactory bulb main interneuron granule MC GABA cell; AMPA; NMDA; Gaba; I A; I h; I K,Ca; I Sodium; I Calcium; I Potassium; Gaba; Glutamate;
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olfactory-bulb-gilra-bhalla
cells
PG_nrn
readme.txt *
hpg.mod *
kamt.mod *
kdrmt.mod *
naxn.mod *
nmdanetOB.mod *
TCa_d.mod *
coeff-table.txt *
forfig3.hoc *
gc.hoc *
Level2.xml
mitral.hoc *
mitral_davison2007_neurml_L1.xml
mod_func.c
mosinit.hoc *
nrnmech.dll
PGsimple_aditya2010_neuroML_L1_L2.xml
PGTest_nrn.py
                            
#!/usr/bin/env python

# This program creates a Davison mitral cell model along with tables to pull data
import sys
import math
# The PYTHONPATH should contain the location of moose.py and _moose.so
# files.  Putting ".." with the assumption that moose.py and _moose.so
# has been generated in ${MOOSE_SOURCE_DIRECTORY}/pymoose/ (as default
# pymoose build does) and this file is located in
# ${MOOSE_SOURCE_DIRECTORY}/pymoose/examples
# sys.path.append('../..')
try:
    from neuron import *
except ImportError:
    print "ERROR: Could not import neuron. Please add the directory containing neuron.py in your PYTHONPATH"
    import sys
    sys.exit(1)

from pylab import *

h.load_file("stdlib.hoc")
h.load_file("stdrun.hoc") # for h.stop ,etc.

h.xopen("/home/aditya/nrn-7.1/share/nrn/lib/hoc/celbild/celtopol.hoc")
h.xopen("/home/aditya/nrn-7.1/share/nrn/lib/hoc/celbild/celgeom.hoc")
h.xopen("/home/aditya/nrn-7.1/share/nrn/lib/hoc/celbild/inhomofn.hoc")
h.xopen("/home/aditya/nrn-7.1/share/nrn/lib/hoc/celbild/psubset.hoc")
h.xopen("/home/aditya/nrn-7.1/share/nrn/lib/hoc/celbild/celset.hoc")
h.xopen("/home/aditya/nrn-7.1/share/nrn/lib/hoc/celbild/celmemb.hoc")
h.xopen("/home/aditya/nrn-7.1/share/nrn/lib/hoc/celbild/celmang.hoc")
h.xopen("/home/aditya/nrn-7.1/share/nrn/lib/hoc/celbild/celbild1.hoc")

# USING PHYSIOLOGICAL UNITS AS NEURON USES THEM
TSTOP = 1600.0 # ms # 500ms delay, 600ms current injection, 500ms decay time

class PGTest:

    def __init__(self):        
        print "NeuroML import of Cell..."

        cellb = h.CellBuild() # Cell Builder # This requires all the above xopen loading of files!

        neuromlFile = "../PG_aditya2010_neuroML_L1_L2.xml"
        #neuromlFile = "Level2.xml"

        cellb.manage.neuroml(neuromlFile)

        cellb.continuous = 1
        cellb.cexport() # export the cell from Cell Builder to neuron top level

        print "Loaded NeuroML from: ",  neuromlFile
        self.PGsoma = h.soma
        ##### NOTE: soma is mapped from a cable, hence it is a section, not a segment! Hence soma.pas does not work.
        ##### soma(0.5) refers to a segment, hence soma(0.5).pas works.
        ##### Can also do for seg in soma.allseg(), but I typically ensure we have only one segment per section.
        
        self.stim = h.IClamp(0.5, sec=self.PGsoma) # current clamp in soma
        
        self.somavecV = h.Vector()
        self.vect = h.Vector()
        
    def run(self):
        self.stim.amp = 0.01 # nA i.e 10pA
        self.stim.delay = 500 #ms
        self.stim.dur = 600 #ms
        h.tstop = TSTOP
        h.dt = 0.001 # ms
        self.somavecV.record(self.PGsoma(0.5)._ref_v)
        self.vect.record(h._ref_t)
        h.run()
                
if __name__ == "__main__":
    pg = PGTest()
    print "soma diameter = ",pg.PGsoma.diam," microns."
    print "soma length = ",pg.PGsoma.L," microns."
    print "Specific axial resistance RA = ",pg.PGsoma.Ra," Ohm-cm."
    soma_crosssectionA = 3.14159*(pg.PGsoma.diam/2.0)**2 * 1e-8 # cm^2 from micron^2
    soma_surfaceA = 3.14159*pg.PGsoma.diam*pg.PGsoma.L * 1e-8 # cm^2 from micron^2
    print "soma Rm = ",(1.0/pg.PGsoma(0.5).pas.g)/soma_surfaceA," Ohms."
    print "soma Cm = ",pg.PGsoma.cm*soma_surfaceA*1e-6," Farads." # Farads from microFarads
    print "soma Ra = ",pg.PGsoma.Ra * pg.PGsoma.L*1e-4/soma_crosssectionA," Ohms."
    print "soma Na gmax = ",pg.PGsoma(0.5).Na_rat_ms.gmax*soma_surfaceA," Siemens."
    print "soma K gmax = ",pg.PGsoma(0.5).KDR_ms.gmax*soma_surfaceA," Siemens."
    pg.run()
    plot(pg.somavecV,',-r')
    show()