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;
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olfactory-bulb-gilra-bhalla
channels
neuron_channels
CaHVA_Chan.xml
CaL_Chan.xml
CaLChannel.py
CaPool.py
CaTChannel.py
channelConstants.py
granuleDefaults.py
Ih_cb.xml
KAChannel.py
KAChannelMS.py
KCaA.dat
KCaA_PG.dat
KCaB.dat
KCaB_PG.dat
KCaChannel.py
KCaChannel_PG.py
KCaMPIChannel.py
KCaMPIChannel_PG.py
KDRChannelMS.py
kfast_k.inf *
kfast_k.tau *
kfast_n.inf *
kfast_n.tau *
KFastChannel.py
KMChannel.py
kslow_k.inf *
kslow_k.tau *
kslow_n.inf *
kslow_n.tau *
KSlowChannel.py
load_channels.py
MOOSEChannelTest.py
NaChannel.py
NaGranChannel.py
NaMitChannelMS.py
tabchannels.dat *
TCa_d.xml
                            
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys
import math
import os

# 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 channelConstants import *

VK = -70.0e-3 # Volts

GK = 28*sarea # Siemens, from mit4.hoc


class KSlowChannel(moose.HHChannel):
    """K slow channel inherits from HHChannel."""
    def __init__(self, *args):
        """Setup the Na channel with defaults"""
        moose.HHChannel.__init__(self,*args)
        self.Ek = VK
        self.Gbar = GK
        self.addField('ion')
        self.setField('ion','K')
        self.Xpower = 2 # This will create HHGate instance xGate inside the Na channel
        self.Ypower = 1 # This will create HHGate instance yGate inside the Na channel
        ## Below gates get created after Xpower or Ypower are set to nonzero values
        ## I don't anymore have to explicitly create these attributes in the class
        #self.xGate = moose.HHGate(self.path + "/xGate")
        #self.yGate = moose.HHGate(self.path + "/yGate")
        selfdir = os.path.dirname(__file__)+os.sep
        fkslow_ninf=open(selfdir+"kslow_n.inf")
        fkslow_ntau=open(selfdir+"kslow_n.tau")
        fkslow_kinf=open(selfdir+"kslow_k.inf")
        fkslow_ktau=open(selfdir+"kslow_k.tau")
        kslow_NDIVS=1000 # ensure that there are 1001 lines in these above data files
        # VMIN and VMAX for kfast should be hard set to -100mV and 50mV, as per the data files, irrespective of other channels.
        VMIN_k = -0.1 # V
        VMAX_k = 0.050 # V
        self.xGate.A.xmin = VMIN_k
        self.xGate.A.xmax = VMAX_k
        self.xGate.A.xdivs = kslow_NDIVS
        self.xGate.B.xmin = VMIN_k
        self.xGate.B.xmax = VMAX_k
        self.xGate.B.xdivs = kslow_NDIVS
        self.yGate.A.xmin = VMIN_k
        self.yGate.A.xmax = VMAX_k
        self.yGate.A.xdivs = kslow_NDIVS
        self.yGate.B.xmin = VMIN_k
        self.yGate.B.xmax = VMAX_k
        self.yGate.B.xdivs = kslow_NDIVS

        for i in range(kslow_NDIVS+1):
            ## The files are from Davison's model, physiological units ms^-1, so convert
            ninf=float(fkslow_ninf.readline().split()[1]) # split each line in the file on whitespace and take the second value (first value is the voltage).
            ntau=1.0e-3*float(fkslow_ntau.readline().split()[1])
            self.xGate.A[i] = ninf/ntau
            self.xGate.B[i] = 1.0/ntau
            kinf=float(fkslow_kinf.readline().split()[1])
            ktau=1.0e-3*float(fkslow_ktau.readline().split()[1])
            self.yGate.A[i] =kinf/ktau
            self.yGate.B[i] =1.0/ktau

        fkslow_ninf.close()
        fkslow_ntau.close()
        fkslow_kinf.close()
        fkslow_ktau.close()

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