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

 Download zip file 
Help downloading and running models
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 os
import sys
import math

sys.path.extend(["..","../channels","../neuroml","../simulations"])

from moose_utils import *

from load_channels import *
from MorphML_reader import *
from simset_inhibition import *

SETTLETIME = 50e-3 # s
RUNTIME = REALRUNTIME + SETTLETIME

from pylab import * # part of matplotlib that depends on numpy but not scipy

class granTest:

    def __init__(self):
        load_channels()
        MML = MorphML()
        gran_dict = MML.readMorphMLFromFile('../cells/granule_granadityaMS2007_neuroML_L1_L2_L3.xml',{})
        self.context = moose.PyMooseBase.getContext()
        granCellId = self.context.deepCopy(self.context.pathToId('/library/granule'),self.context.pathToId('/'),"granule")
        self.granCell = moose.Cell(granCellId)
        # note that soma is not just /granule/soma, it'll also have its segid appended - thus the general purpose function below:
        self.granSoma = moose.Compartment(get_matching_children(self.granCell, ['Soma','soma'])[0]) # take the first [0] available soma!!
        self.somaVm = setupTable('soma_vm', self.granSoma,'Vm')

if __name__ == "__main__":
    gran = granTest()
    iclamp = setup_iclamp(gran.granSoma, 'gran_iclamp', SETTLETIME, 100e-3, 500e-12) # 500pA for 100ms to get a single AP
    resetSim(gran.context, SIMDT, PLOTDT)
    gran.context.step(RUNTIME)
    timevec = arange(0.0,RUNTIME+1e-10,SIMDT)
    plot(timevec, gran.somaVm,'r,-', label='gran soma Vm (V)')
    legend()
    xlabel('time (s)')
    show()


Loading data, please wait...