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 -*-

## USAGE: python2.6 calc_entropy_morphs.py ../results/odor_morphs/2011-01-13_odormorph_SINGLES_JOINTS_PGS.pickle

from scipy import optimize
from pylab import *
import pickle
import sys
import math

sys.path.extend(["..","../networks","../generators","../simulations"])

from stimuliConstants import * # has SETTLETIME, inputList and pulseList, GLOMS_ODOR, GLOMS_NIL
from networkConstants import * # has central_glom
from data_utils import * # has info th functions
from analysis_utils import * # has read_morphfile() and NUM_REBINS, etc.
from infoth_test import * # has plot_table()

info_dt = 10e-3
## last time bin if smaller than info_dt will get thrown away.
num_infobins = int((ODORRUNTIME-SETTLETIME)/info_dt)
timerange = num_infobins*info_dt

def plot_table(rasters,rowlabels,collabels,data,cellcolours,titlestr,figfilename):
    ## 'plot' a table
    fig = figure(figsize=(8, 6), dpi=100)
    ax = fig.add_axes([0.14, 0.85, 0.95, 0.1])
    axes_off(ax)
    ## loop over rasters in reverse order, as they are plotted from bottom upwards
    numrasters = len(rasters)
    for rasteri,raster in enumerate(rasters[::-1]):
        raster = array(raster)
        ## find out indices of 1-s and plot them:
        rasterindices = where(raster==1)[0]
        ax.plot(rasterindices,zeros(len(rasterindices))+rasteri,'|k',\
            markersize=40/2**(numrasters-1), markeredgewidth='2') # | is the marker
    ax.set_ylim(-0.5,rasteri+0.5)
    dirtItable = ax.table(cellText=data, cellColours=cellcolours, rowLoc='right',\
        rowLabels=rowlabels, colLabels=collabels, colLoc='center', loc='bottom')
    table_props = dirtItable.properties()
    table_cells = table_props['child_artists']
    for cell in table_cells:
        cell.set_height(1.5)
        cell.set_fontsize(18)
    ax.set_title(titlestr,fontsize=14)
    ## tight_layout() doesn't seem to work with table
    #fig.tight_layout()
    #fig.savefig(figfilename,dpi=300)

def calc_morph_entropyrates(filename):
    f = open(filename,'r')
    #### each mitral_responses_list[avgnum][odornum][mitralnum][spikenum] stores spiketime
    #### mitral_responses_binned_list[avgnum][odornum][mitralnum][binnum]
    mitral_responses_list, mitral_responses_binned_list = pickle.load(f)
    f.close()
    spiketrains_mits = []
    nummitrals = len(mitral_responses_list[0][0])
    for mitnum in range(nummitrals):
        spiketrains = []
        for belowtrials in mitral_responses_list:
            for belowodornums in belowtrials:
                spiketrain = \
                    get_spiketrain_from_spiketimes(belowodornums[mitnum],SETTLETIME,timerange,num_infobins)
                spiketrains.append(spiketrain)
        spiketrains_mits.append(spiketrains)
        print "Entropy rate of mitral num",mitnum,'=',calc_entropyrate(spiketrains,markovorder=5)

    collabels = ['Order 1','2','4']
    rowlabels = ['Delay 0','1','2','3']
    dirtIs = []
    cellcolours = []
    for delay in [0,1,2,3]:
        print "delay =",delay
        dirtIorders = []
        cellcoloursorders = []
        for order in [1,2,4]:
            print "order =",order
            I2to0 = calc_dirtinforate(spiketrains_mits[2],spiketrains_mits[0],order,order,delay,delay)
            I0to2 = calc_dirtinforate(spiketrains_mits[0],spiketrains_mits[2],order,order,delay,delay)
            I4to1 = calc_dirtinforate(spiketrains_mits[4],spiketrains_mits[1],order,order,delay,delay)
            I1to4 = calc_dirtinforate(spiketrains_mits[1],spiketrains_mits[4],order,order,delay,delay)
            I1to2 = calc_dirtinforate(spiketrains_mits[0],spiketrains_mits[1],order,order,delay,delay)
            dirtIstr = '2to0 = {:1.3f}, 0to2 = {:1.3f}\n4to1 = {:1.3f}, 1to4 = {:1.3f}\n0to1 = {:1.3f}'\
                .format(I2to0,I0to2,I4to1,I1to4,I1to2)
            print dirtIstr
            dirtIorders.append(dirtIstr)
            if I2to0>0.9: cellcoloursorders.append('r')
            else: cellcoloursorders.append('w')
        dirtIs.append(dirtIorders)
        cellcolours.append(cellcoloursorders)
    titlestr = ""
    plot_table([spiketrains_mits[0][19],spiketrains_mits[1][19],\
        spiketrains_mits[2][19],spiketrains_mits[4][19]],\
        rowlabels,collabels,dirtIs,cellcolours,titlestr,'copycat_mydefn.svg')

if __name__ == "__main__":
    if len(sys.argv) > 1:
        filename = sys.argv[1]
    else:
        print "Specify data file containing pickled list."
        sys.exit(1)

    calc_morph_entropyrates(filename)
    show()

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