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;
# -*- coding: utf-8 -*-
# ALL SI UNITS
# milliMolar is same as mol/m^3

# These are the simulation parameters for testing recurrent and lateral inhibition on mitrals via granules
# Only 2 gloms, 2 mitral sisters per glom.

from pylab import *
rc('path',simplify= False) # To ensure matplotlib connects points at expense of speed.

## assuming sys.path already has '../networks'
from networkConstants import * # has PROXIMAL_CONNECTION, DIRECTED and FRAC_DIRECTED

## set to 1 for 2MITS / 2 for 2GLOMS option set in generate_neuroML.py
## note: mit 2 is randomly connected to mit 0; mit 3 is super-inh onto mit 0.
mitralsidekickidx = 2

SIMDT = 5e-5 # seconds
## Correction factor for event-based vs graded synchans
## In event-based case, activation = weight/simdt while in graded case activation is used directly.
## Further, due to every sim point in graded case acting like an event,
## moose gives a convolved Gk which is much larger (say 1000 times) than the event based one.
## So I should correct for simdt and the activation weighted multiple events at every simdt.
#synchan_activation_correction = 1/SIMDT / 1000 # for SIMDT=1e-5
synchan_activation_correction = 1/SIMDT / 250 # for SIMDT=5e-5

PLOTDT = 5e-5 # seconds
# add SETTLETIME from stimuliConstants.py to the REALRUNTIME to get the simulation RUNTIME
REALRUNTIME = 1.0 # seconds - for active inhibition - stimulus only for 500ms

VOLTAGE_CLAMP = False

NUMBINS = 10
###### Somehow mpiexec doesn't like number of processes > 1000 (perhaps memory overflow)
###### and gives errors somewhere in the middle of the run while trying to send to the boss,
###### after which boss decides the below:
## rank 0 in job 35  gulabjamun.ncbs.res.in_56625 caused collective abort of all ranks
############ So ensure that NUMAVG+1 < 1000
####### For a network rather than isolated mitral cell, even 500 processes make the cluster hang.
####### Has worked only for 32 processes, didn't test inbetween yet.
##### Basically memory overflow problems - use less memory by using less data tables
NUMAVG = 50 # don't make NUMAVG higher than 200.

netseedstr = "300.0"#"500.0"
mitdistancestr = "_mitdist50.0" #microns
IN_VIVO = False

## Better to compare MAXSYNS when comparing PGvsGranule and synaptic strengths,
## else there will be +-0.25x random variation.
OBNet_file = '../netfiles/syn_conn_array_10000_singlesclubbed100_jointsclubbed1_numgloms2_seed'\
    +netseedstr+mitdistancestr
granfilebase = "../firefiles/firefiles_baseline/firetimes_gran_baseline_invitro"
if DIRECTED:
    OBNet_file += '_directed'+str(FRAC_DIRECTED)
    if PROXIMAL_CONNECTION:
        OBNet_file += '_proximal'
    else:
        OBNet_file += '_distal'
if IN_VIVO: OBNet_file += '_2GLOMS.xml'
else: OBNet_file += '_2GLOMS_INVITRO.xml'

## SPIKEBLOCK below: TTX and TEA: set Na and Kfast to zero
## ONLY implemented for mitral channels!!!
SPIKEBLOCK = False # only for mitral cells presently

NO_SPINE_INH = False
NO_SINGLES = False
NO_JOINTS = False
NO_MULTIS = False
NO_PGS = False

CLUB_MITRALS = False # If unmodeled mitrals connect to same granule as modeled mitrals, provide extra excitation to granule from modeled mitral
ONLY_TWO_MITS = True#False # Only the two central mitrals with indices (neuroml id-s) given in networkConstants.py to test lateral inhibition.

ODOR_GIVEN = False # No odor given only current injection as below
offInject = 750e-12#200e-12#450e-12 # current in A, roughly 24Hz (8-9 in 400ms) firing w/o lateral inh -- Arevian et al
#offInject = 1000e-12 # current in A, roughly 40Hz firing w/o lateral inh -- Friedman & Strowbridge 2000
onInject = 2000e-12#1500e-12#1750e-12 # current in B, roughly 80Hz firing w/ lateral - Arevian et al 2008 use 60 to 100Hz in mitral B.

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