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]
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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 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 -*-

import math, os
import pickle

from pylab import *

## USAGE: python2.6 plot_chisquares.py


from networkConstants import * # has central_glom
from data_utils import *
from fit_odor_morphs import *

from results_catalogue import *

fig1 = figure(facecolor='none') # 'none' is transparent
## A super axes to set common x and y axes labels
bigAxes1 = fig1.add_axes([0.1,0.1,0.8,0.8],frameon=False) # hide frame
#bigAxes1.set_xticklabels(['none', 'singles',\
#    's+joints', 's+j+PGs'],fontsize=20)
bigAxes1.text(-0.1,0.3,'chisquare', fontsize=24, rotation='vertical')
bigAxes1.text(-0.1,-0.11,'inhibition: none, singles, s+joints, s+j+PGs',\
    fontsize=24, rotation='horizontal')
bigAxes1.set_title('Morph chisquare vs inhibition',fontsize=36)

plotnum = 1
for runnum in range(len(filelist[0])):
    chisqlist = []
    print seeds[runnum]
    ## loop over files with the same seeds but different inhibitory components
    for filename in array(filelist)[:,runnum]:
        filenamefull = '../results/odor_morphs/'+filename
        chisq_mitlist = []
        for fitted_mitral in fitted_mitral_list:
            if fit_type=='arb':
                paramsfilename = filenamefull+'_params'+str(fitted_mitral)
            elif fit_type=='lin':
                paramsfilename = filenamefull+'_paramslin'+str(fitted_mitral)
            print paramsfilename
            if not os.path.exists(paramsfilename):
            f = open(paramsfilename,'r')
            params,chisq = pickle.load(f)

    ## hardcoded - I know there are 4 files for different inhibitions
    ax1 = fig1.add_subplot(2,3,plotnum)
    ## plot for mit0 and mit1
    #ymax = ax1.get_ylim()[1]+0.01
    ## very important to give it after the plot functions else autoscales
    ## or set autoscaling off - gca().set_autoscale_on(False)
    ## - taken from http://old.nabble.com/ylim-does-not-work-td19000814.html
    ax1.set_title( str(seeds[runnum]), size='large')

    plotnum += 1