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

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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.
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;
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]; 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;
kfast_k.inf *
kfast_k.tau *
kfast_n.inf *
kfast_n.tau *
kslow_k.inf *
kslow_k.tau *
kslow_n.inf *
kslow_n.tau *
tabchannels.dat *
#!/usr/bin/env python
import sys
import math

# The PYTHONPATH should contain the location of and
# files.  Putting ".." with the assumption that and
# 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
except ImportError:
    print "ERROR: Could not import moose. Please add the directory containing in your PYTHONPATH"
    import sys

from channelConstants import *

VKDR = -90.0e-3 # Volts

gransarea = 5e-9 # m^2 default surface area of soma from granule.tem
GKDR = 20*gransarea # Siemens

vhalfm=-50e-3 # V
zetam=0.055e3 # /V
gmm=0.5 # dimensionless

qt=q10**((CELSIUS-24.0)/10.0) # CELSIUS is a global constant
# (CELSIUS-24)/10 - integer division!!!! Ensure floating point division
# FOR INTEGERS: be careful ^ is bitwise xor in python - used here qt=2, but above qt=3!

def calc_KA_malp(v):
    return math.exp(zetam*(v-vhalfm))

def calc_KA_mbet(v):
    return math.exp(zetam*gmm*(v-vhalfm))

def calc_KA_mtau(v):
    return calc_KA_mbet(v)/(qt*a0m*(1+calc_KA_malp(v))) * 1e-3 # convert to seconds

def calc_KA_minf(v):
    return 1/(1 + math.exp(-(v-21e-3)/10e-3))

class KDRChannelMS(moose.HHChannel):
    """K Delayed Rectifier channel translated from Migliore and Shepherd 2007."""
    def __init__(self, *args):
        """Setup the KDR channel with defaults"""
        self.Ek = VKDR
        self.Gbar = GKDR
        self.Xpower = 1 # This will create HHGate instance xGate inside the Na channel
        #self.Ypower = 0 # 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")
        self.xGate.A.xmin = VMIN
        self.xGate.A.xmax = VMAX
        self.xGate.A.xdivs = NDIVS
        self.xGate.B.xmin = VMIN
        self.xGate.B.xmax = VMAX
        self.xGate.B.xdivs = NDIVS
        v = VMIN

        for i in range(NDIVS+1):
            mtau = calc_KA_mtau(v)
            self.xGate.A[i] = calc_KA_minf(v)/mtau
            self.xGate.B[i] = 1.0/mtau
            v = v + dv

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