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
import pickle

from pylab import *

sys.path.extend(["..","../networks","../generators","../simulations"])
from simset_inhibition import * # has REALRUNTIME
from stimuliConstants import * # has SETTLETIME
from data_utils import *

SETTLETIME = 500e-3 # s, increased as per inhibition_recvslat.py
RUNTIME = REALRUNTIME + SETTLETIME

## data_recvslat.pickle is generated by inhibition_recvslat.py
## USAGE: python2.6 plot_inhibition_recvslat.py data_recvslat.pickle

if __name__ == "__main__":

    mit_responses = pickle.load( open( sys.argv[1], "rb" ) )

    timevec = arange(0.0,RUNTIME+1e-10,SIMDT)*1e3
    tmin,tmax = 400,1200 # ms
    Vrest = -65 # mV
    Vmin,Vmax = -80,40 # mV
    fig = figure(figsize=(columnwidth*1.5,linfig_height*1.5),dpi=300,facecolor='w')

    ## mitral A
    ax1 = fig.add_subplot(2,2,1)
    ax1.plot((tmin,tmax),(Vrest,Vrest),linestyle='dashed',linewidth=plot_linewidth,color=(0.5,0.5,0.5))
    ax1.plot(timevec,mit_responses[1][0]*1e3,color='r',linewidth=plot_linewidth,label='Lateral')
    beautify_plot(ax1,x0min=False,y0min=False,xticksposn='none',yticksposn='left')
    ax1.set_xlim(tmin,tmax)
    ax1.set_ylim(Vmin,Vmax)
    ax1.set_yticks([Vmin,Vmax])
    axes_labels(ax1,'','Vm (mV)',ypad=-2)
    ax2 = fig.add_subplot(2,2,3)
    ax2.plot((tmin,tmax),(Vrest,Vrest),linestyle='dashed',linewidth=plot_linewidth,color=(0.5,0.5,0.5))
    ax2.plot(timevec,mit_responses[0][0]*1e3,color='r',linewidth=plot_linewidth,label='Recurrent')
    beautify_plot(ax2,x0min=False,y0min=False,xticksposn='bottom',yticksposn='left')
    ax2.set_xlim(tmin,tmax)
    ax2.set_xticks([tmin,800,tmax])
    ax2.set_ylim(Vmin,Vmax)
    ax2.set_yticks([Vmin,Vmax])
    axes_labels(ax2,'time (ms)','Vm (mV)',xpad=2,ypad=-2)

    ## inset plot
    from mpl_toolkits.axes_grid.inset_locator import mark_inset
    ## add_axes() takes figure coordinates,
    ## just passing transform=ax.transAxes as an argument to add_axes() does nothing.
    ## So you need to do some complicated jugglery
    ## (from http://matplotlib.1069221.n5.nabble.com/Adding-custom-axes-within-a-subplot-td20316.html)
    Bbox = matplotlib.transforms.Bbox.from_bounds(.75, .6, .5, .5) 
    trans = ax1.transAxes + fig.transFigure.inverted() 
    l, b, w, h = matplotlib.transforms.TransformedBbox(Bbox,trans).bounds
    axinset = fig.add_axes([l, b, w, h])
    axinset.plot(timevec,mit_responses[1][0]*1e3,color='r',linewidth=plot_linewidth,label='Lateral')
    ## thin frame
    for loc, spine in axinset.spines.items(): # items() returns [(key,value),...]
        spine.set_linewidth(axes_linewidth)
    axinset.set_xlim(910,975)
    axinset.set_ylim(-71.5,-70)
    axinset.set_xticks([])
    axinset.set_yticks([])
    mark_inset(ax1, axinset, loc1=2, loc2=4, fc="none", ec="0.5", linewidth=axes_linewidth)
    
    ## mitral B
    ax1 = fig.add_subplot(2,2,2)
    ax1.plot((tmin,tmax),(Vrest,Vrest),linestyle='dashed',linewidth=plot_linewidth,color=(0.5,0.5,0.5))
    ax1.plot(timevec,mit_responses[1][1]*1e3,color='g',linewidth=plot_linewidth,label='Lateral')
    beautify_plot(ax1,x0min=False,y0min=False,xticksposn='none',yticksposn='none')
    ax1.set_xlim(tmin,tmax)
    ax1.set_ylim(Vmin,Vmax)
    ax1.set_yticks([])
    axes_labels(ax1,'','')
    ax2 = fig.add_subplot(2,2,4)
    ax2.plot((tmin,tmax),(Vrest,Vrest),linestyle='dashed',linewidth=plot_linewidth,color=(0.5,0.5,0.5))
    ax2.plot(timevec,mit_responses[0][1]*1e3,color='g',linewidth=plot_linewidth,label='Recurrent')
    beautify_plot(ax2,x0min=False,y0min=False,xticksposn='bottom',yticksposn='none')
    ax2.set_xlim(tmin,tmax)
    ax2.set_xticks([tmin,800,tmax])
    ax2.set_ylim(Vmin,Vmax)
    ax2.set_yticks([])
    axes_labels(ax2,'time (ms)','',xpad=2)

    #fig.tight_layout()
    fig.subplots_adjust(top=0.95,left=0.1,right=0.97, wspace=0.25,hspace=0.25)
    #fig_clip_off(fig)
    fig.savefig('../figures/recvslat.svg',dpi=fig.dpi)
    fig.savefig('../figures/recvslat.png',dpi=fig.dpi)

    show()


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