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 -*-

# This program creates a Migliore & Shepherd 2008 gran cell model along with tables to pull data.
# Only the two biggest compartments are modelled.
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('../..')

import moose

from mooseConstants import *
from globalConstants import *
    
from pylab import * # part of matplotlib that depends on numpy but not scipy
import random
import pickle

class BBGranule:

    def __init__(self, moosename='/granule', table=False):
        self.context = moose.PyMooseBase.getContext()
        self.path = moosename
        self.setupClocks()
        self.loadCell()
        if table:
            self.setupTables()
        # print self._gran.method ######## By default Cell object uses hsolve method
        print("<< "+moosename+" fully loaded >>")

    def loadCell(self):
        self.context.readCell('gran_aditya_migliore.p',self.path)
        self._gran = moose.Cell(self.path)
        self._granSoma = moose.Compartment(self.path+'/soma')
        self._granSomaKA = moose.Compartment(self.path+'/soma/KA_ms')
        self._granPeri = moose.Compartment(self.path+'/periphery')
        self._granPeriNa = moose.HHChannel(self.path+'/periphery/Na_rat_ms')
        self._granPeriKA = moose.HHChannel(self.path+'/periphery/KA_ms')
                
    def setupTables(self):
        self._data = moose.Neutral(self.path+"/data")
        # Setup the tables to pull data
        self._vmTableSoma = moose.Table("vmTableSoma", self._data)
        self._vmTableSoma.stepMode = TAB_BUF #TAB_BUF: table acts as a buffer.
        self._vmTableSoma.connect("inputRequest", self._granSoma, "Vm")
        self._vmTableSoma.useClock(PLOTCLOCK)
        
    def setupClocks(self):
        ###### I suppose by default all elements use clock 0 the global clock. Not sure, Niraj checked that hsolve uses clock 1
        self.context.setClock(0, SIMDT, 0)
        self.context.setClock(1, SIMDT, 0) #### The hsolve and ee methods use clock 1
        self.context.setClock(2, SIMDT, 0) #### hsolve uses clock 2 for mg_block, nmdachan and others.
        self.context.setClock(PLOTCLOCK, PLOTDT, 0)

def setup_iclamp(compartment, name, delay1, width1, level1):
    moose.Neutral('/elec') # If /elec doesn't exists it creates /elec and returns a reference to it. If it does, it just returns its reference.
    pulsegen = moose.PulseGen('/elec/pulsegen'+name)
    iclamp = moose.DiffAmp('/elec/iclamp'+name)
    iclamp.saturation = 1e6
    iclamp.gain = 1.0
    pulsegen.trigMode = 0 # free run
    pulsegen.baseLevel = 0.0
    pulsegen.firstDelay = delay1
    pulsegen.firstWidth = width1
    pulsegen.firstLevel = level1
    pulsegen.secondDelay = 1e6
    pulsegen.secondLevel = 0.0
    pulsegen.secondWidth = 0.0
    pulsegen.connect('outputSrc',iclamp,'plusDest')
    iclamp.connect('outputSrc',compartment,'injectMsg')
    return pulsegen


if __name__ == "__main__":
    sys.path.append("channels/")
    from NaGranChannelMS import *
    from KAChannelMS import *
    from KDRChannelMS import *
    NaGranChannelMS("/library/Na_rat_ms") # maintaining the old name to avoid changing .p file even though the Na channel here is from Migliore and Shepherd 2008
    KAChannelMS("/library/KA_ms")
    KDRChannelMS("/library/KDR_ms")
    
    seed() ##### Seed numpy's random number generator. If no parameter is given, it uses current system time
    gran = BBGranule(table=True)
    iclamp = setup_iclamp(gran._granSoma, '_gran', 50e-3, 300e-3, 50e-12) # 500pA for 100ms to get a single AP
    gran.context.reset()
    gran.context.step(500e-3)
    plot(gran._vmTableSoma,'r,-')
    #gran._granPeriKA.Gbar = 0
    #gran._granSomaKA.Gbar = 0
    #gran.context.reset()
    #gran.context.step(500e-3)
    #plot(gran._vmTableSoma,'g,-')
    show()
    
    

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