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;
import sys
sys.path.extend([".."])

from data_utils import *

def check_substr():
    """check find_substr_endchars"""
    a = '101010100'
    print a
    print [ch for ch in find_substr_endchars(a,'1010')]

## test entropy rate

def IID_entropyrate():
    """equal prob of 1s and 0s IID. Entropy rate should be 1 bit per use."""
    spiketrains = [[int(uniform(0,1)+0.5) for i in range(1000)]]
    print "Equal prob of 1s and 0s IID. Hrate =",calc_entropyrate(spiketrains,1)

def markov_order1_entropyrate():
    """markov process of order 1
    p(0)=0.6, p(1)=0.4"""
    spiketrains = []
    for j in range(10):
        mc = [1]
        for i in range(10000):
            if mc[-1]:
                p = uniform(0,1)
                if p<0.25: mc.append(1)
                else: mc.append(0)
            else:
                p = uniform(0,1)
                if p<0.5: mc.append(1)
                else: mc.append(0)
        spiketrains.append(mc)
    entropyrates = []
    ## careful: don't go to higher orders.. machine stalls badly..
    for i in range(10):
        entropyrates.append(calc_entropyrate(spiketrains,i))
    figure(facecolor='w')
    plot(entropyrates,'r-,')

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, 0.75, 0.85, 0.1])
    axes_off(ax)
    ## loop over rasters in reverse order, as they are plotted from bottom upwards
    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=20, 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(1.35)
        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 plot_dirtIrates(spiketrains1,spiketrains2,delay1,delay2,\
                    dirtIcutoff,filename='none.svg'):
    collabels = ['Order 1','2','3']
    rowlabels = ['Delay 0','1','2','3']
    dirtIs = []
    cellcolours = []
    for measure_delay in range(4):
        print "delay =",measure_delay
        dirtIorders = []
        cellcoloursorders = []
        for i in range(1,4):
            print "markovorder =",i
            causal_dirtI = calc_dirtinforate(spiketrains1,spiketrains2,\
                i,i,measure_delay,measure_delay)
            oppcausal_dirtI = calc_dirtinforate(spiketrains2,spiketrains1,\
                i,i,measure_delay,measure_delay)
            dirtIstr = '    causal = {:1.3f}\nopp causal = {:1.3f}'\
                .format(causal_dirtI,oppcausal_dirtI)
            print dirtIstr
            dirtIorders.append(dirtIstr)
            if causal_dirtI>dirtIcutoff: cellcoloursorders.append('r')
            else: cellcoloursorders.append('w')
        dirtIs.append(dirtIorders)
        cellcolours.append(cellcoloursorders)
    titlestr = "Copy IID spike train 1 to 2 with delays = "\
            +str(delay1)+" & "+str(delay2)+\
            "\n Measure with markov order & common delay as below"+\
            "\n causal is 1->2, opp causal is 2->1"
    plot_table([spiketrains1[0][0:100],spiketrains2[0][0:100]],\
        rowlabels,collabels,dirtIs,cellcolours,titlestr,filename)


def copycat_dirtIrate(delay=0):
    """Test directed information: trains1 has full causal exc effect on trains2.
    Copies spiketrain1 to 2 with causal delay i.e. delay+1.
    spiketrains and spiketrains1 is IID, p(0)=0.5."""
    spiketrains = []
    spiketrains1 = []
    spiketrains2 = []
    for j in range(10):
        ## need delay+1 num of copies of 1, to access mc1[-delay-1] below
        ## maintain same length of spike trains, hence common start length
        mc = [1]*(delay+1)
        mc1 = [1]*(delay+1)
        mc2 = [1]*(delay+1)
        for i in range(10000):
            ## mc2 fully depends on mc1 with delay
            if mc1[-delay-1]:
                mc2.append(1)
            else:
                mc2.append(0)
            ## mc1 is IID
            p = uniform()
            if p<0.5: mc1.append(1)
            else: mc1.append(0)
            ## mc is IID
            p = uniform()
            if p<0.5: mc.append(1)
            else: mc.append(0)
        spiketrains.append(mc)
        spiketrains1.append(mc1)
        spiketrains2.append(mc2)

    collabels = ['Order 1','2','3']
    rowlabels = ['Delay 0','1','2','3']
    dirtIs = []
    cellcolours = []
    for measure_delay in range(4):
        print "delay =",measure_delay
        dirtIorders = []
        cellcoloursorders = []
        for i in range(1,4):
            print "order =",i
            causal_dirtI = calc_dirtinforate(spiketrains1,spiketrains2,\
                i,i,measure_delay,measure_delay)
            oppcausal_dirtI = calc_dirtinforate(spiketrains2,spiketrains1,\
                i,i,measure_delay,measure_delay)
            acausal_dirtI = calc_dirtinforate(spiketrains,spiketrains2,\
                i,i,measure_delay,measure_delay)
            dirtIstr = '    causal = {:1.3f}\nopp causal = {:1.3f}\n    acausal = {:1.3f}'\
                .format(causal_dirtI,oppcausal_dirtI,acausal_dirtI)
            print dirtIstr
            dirtIorders.append(dirtIstr)
            if causal_dirtI>0.9: cellcoloursorders.append('r')
            else: cellcoloursorders.append('w')
        dirtIs.append(dirtIorders)
        cellcolours.append(cellcoloursorders)
    titlestr = "Copy IID spike train 1 to 2 with causal delay = "+str(delay)+\
            "\n Measure with markov order & common delay as below"+\
            "\n causal is 1->2, opp causal is 2->1, acausal is IID->2"
    plot_table([spiketrains1[0][0:100],spiketrains2[0][0:100]],\
        rowlabels,collabels,dirtIs,cellcolours,titlestr,'copycat_mydefn.svg')

def partialcopycat_dirtIrate(delay1=0,delay2=0,inh=True):
    """Test directed information: trains1 has partial causal exc effect on trains2.
    Copies partially spiketrain1 to 2, with 1 delayed by delay1 and 2 delayed by delay2,
    (these are causal delays, hence delay1+1 and delay2+1)
    and flipping if inh."""
    spiketrains1 = []
    spiketrains2 = []
    if inh:
        one=0
        zer=1
    else:
        one=1
        zer=0
    for j in range(10):
        ## need delay+1 num of copies of 1, to access mc1[-delay1-1] below
        ## maintain same length of spike trains, hence common start length
        delay = max(delay1,delay2)
        mc1 = [1]*(delay+1)
        mc2 = [1]*(delay+1)
        for i in range(10000):
            ## mc2 depends on mc1 and mc2,
            if mc1[-delay1-1] and mc2[-delay2-1]: # both are 1
                mc2.append(one)
            elif mc1[-delay1-1] or mc2[-delay2-1]: # elif either is 1
                p = uniform(0,1)
                if p<0.75: mc2.append(one)
                else: mc2.append(zer)
            else: # both are zero
                mc2.append(zer)
            ## mc1 is IID
            p = uniform()
            if p<0.5: mc1.append(1)
            else: mc1.append(0)
        spiketrains1.append(mc1)
        spiketrains2.append(mc2)

    plot_dirtIrates(spiketrains1,spiketrains2,delay1,delay2,0.1,'partialcopycat_mydefn.svg')

def bicausal_dirtIrate(delay1=0,delay2=0,inh=True):
    """Test directed information: bidirectional exc/inh effect."""
    spiketrains1 = []
    spiketrains2 = []
    if inh:
        one=0
        zer=1
    else:
        one=1
        zer=0
    for j in range(10):
        ## need delay+1 num of copies of 1, to access mc1[-delay1-1] below
        ## maintain same length of spike trains, hence common start length
        delay = max(delay1,delay2)
        mc1 = [1]*(delay+1)
        mc2 = [1]*(delay+1)
        for i in range(10000):
            ## mc1 and mc2 depend on mc1 and mc2, but asymmetrically
            ## delay is on both here!
            if mc1[-delay1-1] and mc2[-delay2-1]: # both are 1
                p = uniform(0,1)
                if p<0.85: mc2.append(one)
                else: mc2.append(zer)
                p = uniform(0,1)
                if p<0.65: mc1.append(one)
                else: mc1.append(zer)
            elif mc1[-delay1-1] or mc2[-delay2-1]: # elif either is 1
                p = uniform(0,1)
                if p<0.6: mc2.append(one)
                else: mc2.append(zer)
                p = uniform(0,1)
                if p<0.5: mc1.append(one)
                else: mc1.append(zer)
            else: # both are zero
                p = uniform(0,1)
                if p<0.8: mc2.append(zer)
                else: mc2.append(one)
                p = uniform(0,1)
                if p<0.6: mc1.append(zer)
                else: mc1.append(one)
        spiketrains1.append(mc1)
        spiketrains2.append(mc2)

    plot_dirtIrates(spiketrains1,spiketrains2,delay1,delay2,0.01,'bicausal_mydefn.svg')


if __name__ == "__main__":
    #copycat_dirtIrate()
    partialcopycat_dirtIrate(delay1=1,delay2=2,inh=False)
    partialcopycat_dirtIrate(delay1=1,delay2=2,inh=True)
    #bicausal_dirtIrate(delay1=1,delay2=2,inh=True)
    show()

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