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;
## This file used to be programmatically generated for converging to best fit Activity Dependent Inhibition curve.
## But that doesn't give decent result, so set by hand.

import sys
sys.path.extend(["../networks"])
## do not import networkConstants as that imports this file, and it's circular then!!!
from networkConstantsMinimal import *
## STRONG_SYNAPSES is defined in networkConstants, but can't import it due to reason above,
## so duplicating the directed and frac_directed check below again.

## For STRONG_SYNAPSES i.e differential connectivity set mitral -> granule base excitation to 0.2nS
## else, for random / uniform connectivity, set the base value to 0.3nS
## This is to get the same amount of activity dependent inhibition (Arevian et al)
## for the different network connectivities...
if directed and frac_directed>0.0:
    mitral_granule_AMPA_Gbar = 0.2e-9 # Siemens
    granule_mitral_GABA_Gbar = 1.0e-9#12.0e-09 # Siemens
else: #### confirm ADI for 0% frac_directed setting below
    ## 0.3e-9 for 3% frac_directed, _mod mitral,
    ## but 0.2e-9 for 1% frac_directed, _mod_spikeinit mitral
    mitral_granule_AMPA_Gbar = 0.2e-9#0.3e-9 # Siemens
    granule_mitral_GABA_Gbar = 1.5e-9#12.0e-09 # Siemens
## For the _mod mitral with _spikeinit,
## self Gbar below must be reduced to 5 pS, else huge self-inhibition
## For the _mod mitral, 50 pS is fine, it doesn't get affected much by inhibition!
self_mitral_GABA_Gbar = 5e-12#5e-12#50e-12 # Siemens

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