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 os
import sys
import math

sys.path.extend(["..","../channels"])
from channelConstants import * # for CELSIUS, the global temperature

import moose
from moose.neuroml import *

def load_cells():
    MML = MorphML({'temperature':CELSIUS})
    ## writing ../cells/ below allows load_cells() to be called from within any sister directory

    ## original mitral cell of Upi with half RM (half tau), -58mV resting potential
    ## more Na & K in primary dend, some Na & K in tuft, electrode leak removed.
    #mitral_dict = MML.readMorphMLFromFile('../cells/mitral_bbmit1993davison_neuroML_L1_L2_L3_mod.xml',{})
    ## modified as above + for spike initiation at soma for small tuft input and at tuft for large tuft input:
    ## RA reduced; added hillock and initial segment (with Migliore & Shepherd's Na channel), tuft and prim dend more excitable
    mitral_dict = MML.readMorphMLFromFile('../cells/mitral_bbmit1993davison_neuroML_L1_L2_L3_mod_withspikeinit.xml',{})
    #mitral_dict = MML.readMorphMLFromFile('../cells/mitral_bbmit1993davison_neuroML_L1_L2_L3.xml',{})
    #mitral_dict = MML.readMorphMLFromFile('../cells/mitral_bbmit1993davison_neuroML_TEST_L1_L2_L3.xml',{})
    ## modified mitral cell with changes as above plus: RM is 1/5th,
    ## Migliore & Shepherd Na channels, special hillock MS Na channel
    #mitral_dict = MML.readMorphMLFromFile('../cells/mitral_bbmit1993davisonMS_neuroML_L1_L2_L3.xml',{})

    ## Low threshold spiking cell, LTS with shoulder and plateau potential due to extra TCa (low threshold Ca).
    PG_dict = MML.readMorphMLFromFile('../cells/PG_aditya2013unified_neuroML_L1_L2_L3.xml',{})
    ### For the 2012 cell, to get the Ca conc correct, below code was used. Obsolete with the new 2013 cell.
    ### Low threshold spiking cell, LTS with shoulder and plateau potential due to extra TCa (low threshold Ca).
    ### Be sure to uncomment the CaBasal setting below too!
    ### For just the new 2012 PG cell set CaPool.CaBasal to 0.05 mM, else the PG fires a settling burst at t=0.
    ### PG_dict['PG'] = { segid1 : [ segname,(proximalx,proximaly,proximalz),
    ###  (distalx,distaly,distalz),diameter,length,[potential_syn1, ... ] ] , ... }
    ### This is a complete hackjob, ideally, I should use a different CaPool.
    #PG_dict = MML.readMorphMLFromFile('../cells/PG_aditya2012_neuroML_L1_L2_L3.xml',{})
    #for seg in PG_dict['PG'].values():
    #    segname = seg[0]
    #    segpath = '/library/PG/'+segname
    #    if not moose.context.exists(segpath):
    #        print "Missing compartment",segpath
    #        sys.exit(1)
    #    compartment = moose.Compartment(segpath)
    #    for child in compartment.getChildren(compartment.id): # channels and synapses
    #        if moose.Neutral(child).className in ['CaConc']:
    #            CaPool = moose.CaConc(child)
    #            CaPool.CaBasal = 0.05 # mM
    ### hackjob for PG cell 2012 ends.

    granule_dict = MML.readMorphMLFromFile('../cells/granule_granadityaMS2007_neuroML_L1_L2_L3.xml',{})

    ## make one dictionary out of above dicts.
    cellSegmentList = {}
    cellSegmentList.update(mitral_dict)
    cellSegmentList.update(PG_dict)
    cellSegmentList.update(granule_dict)
    return cellSegmentList

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