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]
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;
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys
import math

from pylab import *

# 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:
    import moose
except ImportError:
    print "ERROR: Could not import moose. Please add the directory containing moose.py in your PYTHONPATH"
    import sys
    sys.exit(1)

from synapseConstants import *
from moose.utils import * # for BSplineFill

## if plastic synapse, choose the short term plasticity syn chan
if GABA_plastic: GABAbaseSynChan = moose.STPSynChan
else: GABAbaseSynChan = moose.SynChan

class granule_mitral_GABA(GABAbaseSynChan):
    """Non-saturating GABA synapse from granule to mitral cell."""
    def __init__(self, synchan_activation_correction, *args):
        GABAbaseSynChan.__init__(self,*args)
        self.Ek = granule_mitral_GABA_Ek
        self.Gbar = granule_mitral_GABA_Gbar
        self.tau1 = granule_mitral_GABA_tau1
        self.tau2 = granule_mitral_GABA_tau2
        self.addField('graded')
        if GRANULE_INH_GRADED:
            self.setField('graded','True')
            ######## Graded synapse
            inhsyntable = moose.Table(self.path+"/graded_table")
            inhsyntable.xmin = -43e-3#-52e-3 # V
            inhsyntable.xmax = -19e-3#-28e-3 # V
            inhsyntable.xdivs = 12
            # adjust activation curve so that all the dynamics happens between -52 and -28mV.
            act = [0.0] # at -52mV
            act.extend( [1/(1+math.exp(-(vm + 31.5e-3)/1.0e-3)) for vm in arange(-41e-3,-21.00001e-3,2e-3)] )
            act.extend([1.0]) # for -28mV
            for i,activation in enumerate(act):
                inhsyntable[i] = activation*synchan_activation_correction # synchan_activation_correction depends on SIMDT!
            inhsyntable.tabFill(1000,BSplineFill)
            inhsyntable.connect("outputSrc",self,"activation")
        else:
            self.setField('graded','False')
        self.addField('mgblock')
        self.setField('mgblock','False')

        ## Only depression, no facilitation:
        ## Venki Murthy 2005 paper
        self.tauD1 = GABA_recovery_time
        self.deltaF = 0.0
        self.d1 = GABA_depression_factor
        self.d2 = 1.0

        ########## Remember that the Vm graded synapse is actually a proxy for Ca dependent synapse:
        ########## Graded Ca dependent synapse
        #inhsyntable = moose.Table(granule._gran.path+"/InhSynTable_"+mcstr+"_"+gcstr+"_"+nsstr)
        #inhsyntable.xmin = 0 # Ca concentration - mMolar = mol/m^3 SI
        #inhsyntable.xmax = 4.0e-4 # Ca concentration - mMolar = mol/m^3 SI
        #inhsyntable.xdivs = 10
        ##for i,activation in enumerate([0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,1.0,1.0]):
        #for i,activation in enumerate([0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,1.0,1.0]):
        #    inhsyntable[i] = activation*synchan_activation_correction
        #inhsyntable.tabFill(1000,BSplineFill)
        #inhsyntable.connect("outputSrc",inhsyn,"activation")
        #granule._granPeriCaPool.connect('concSrc',inhsyntable,'msgInput')

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