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_corrs.py ../results/odor_morphs/2011-01-13_odormorph_SINGLES_JOINTS_PGS.pickle

from scipy import stats
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 simset_odor import * # has NUMBINS, overridden below / in function call
from networkConstants import * # has central_glom
from sim_utils import * # has rebin() to alter binsize
from data_utils import * # has crosscorrgram

## I override the NUMBINS (and bin_width_time) defined in simset_odor above, and I rebin() below
NUMBINS = 17
## smooths / overlapping bins. non-overlapping would be RESPIRATION/NUMBINS
bin_width_time = 2.0*RESPIRATION/NUMBINS

NUMMIX = len(inputList)

def get_tvecs(flist, morphnum, mitnum):
    """ returns a NUMAVGS array of firing times for this morphnum and mitnum. """
    ## cannot convert to numpy array(),
    ## as number of firing times is not fixed
    ## thus cannot use flist[:,morphnum,mitnum]
    return [flist[i][morphnum][mitnum] for i in range(len(flist))]

def calc_corrs(filename, norm_str, numbins=NUMBINS, bin_width_time=bin_width_time, printinfo = True):
    """
    Plots correlations between air and odor responses of sister cells.
    """
    f = open(filename,'r')
    #### mitral_responses_list[avgnum][odornum][mitralnum][tnum]
    #### mitral_responses_binned_list[avgnum][odornum][mitralnum][binnum]
    mitral_responses_list, mitral_responses_binned_list = pickle.load(f)
    f.close()

    ###################### Input conditioning
    ## By default, rebin takes only the last respiratory cycle i.e. last numbins
    mitral_responses_binned_list = \
        rebin(mitral_responses_list, numbins, bin_width_time)
    #### very important to convert to numpy array,
    #### else where() below returns empty list.
    mitral_responses_binned_list = array(mitral_responses_binned_list)
    mitral_responses_mean = mean(mitral_responses_binned_list, axis=0)
    mitral_responses_std = std(mitral_responses_binned_list, axis=0)
    ## since I fit the mean response, I must use standard error/deviation of the _mean_
    ## = standard deviation of a repeat / sqrt(num of repeats).
    NUMAVGs = len(mitral_responses_binned_list)
    mitral_responses_se = mitral_responses_std/sqrt(NUMAVGs)

    ## mean frate change sisters in glom0 and non-sisters (glom1) for each odor
    odorA_Dfrate_mit0 = mean(mitral_responses_mean[5,central_glom] - mitral_responses_mean[6,central_glom])
    odorA_Dfrate_mit1 = mean(mitral_responses_mean[5,central_glom+1] - mitral_responses_mean[6,central_glom+1])
    odorB_Dfrate_mit0 = mean(mitral_responses_mean[0,central_glom] - mitral_responses_mean[6,central_glom])
    odorB_Dfrate_mit1 = mean(mitral_responses_mean[0,central_glom+1] - mitral_responses_mean[6,central_glom+1])
    #odorA_Dfrate_mit2 = mean(mitral_responses_mean[5,central_glom+2] - mitral_responses_mean[6,central_glom+2])
    #odorA_Dfrate_mit3 = mean(mitral_responses_mean[5,central_glom+3] - mitral_responses_mean[6,central_glom+3])
    #odorB_Dfrate_mit2 = mean(mitral_responses_mean[0,central_glom+2] - mitral_responses_mean[6,central_glom+2])
    #odorB_Dfrate_mit3 = mean(mitral_responses_mean[0,central_glom+3] - mitral_responses_mean[6,central_glom+3])
    Dfrates = (odorA_Dfrate_mit0,odorA_Dfrate_mit1,odorB_Dfrate_mit0,odorB_Dfrate_mit1)
    #    odorA_Dfrate_mit2,odorA_Dfrate_mit3,odorB_Dfrate_mit2,odorB_Dfrate_mit3)

    ## If one of the arrays is all zeroes, stats.pearsonr gives a one-time warning
    ## BINNED cross correlation
    air_corr = stats.pearsonr(mitral_responses_mean[6,central_glom],\
        mitral_responses_mean[6,central_glom+1])[0]
    odorA_corr = stats.pearsonr(mitral_responses_mean[5,central_glom],\
        mitral_responses_mean[5,central_glom+1])[0]
    odorB_corr = stats.pearsonr(mitral_responses_mean[0,central_glom],\
        mitral_responses_mean[0,central_glom+1])[0]
    if printinfo:
        print "air binned correlation between central sisters = ", air_corr
        print "odor A binned correlation between central sisters = ", odorA_corr
        print "odor B binned correlation between central sisters = ", odorB_corr
        #if isnan(air_corr): print mitral_responses_list, mitral_responses_binned_list

    ## Spike-based cross-correlation
    starttime = REALRUNTIME+SETTLETIME-2*RESPIRATION
    endtime = REALRUNTIME+SETTLETIME
    T = endtime-starttime
    ## Dhawale et al 2010: 5 ms time bin, T=0.5s.
    ## I have T=1.0s as rat resp is 1.0s whereas mouse is 0.5s.
    ## refractory period is typically 1ms, so have that as the bin size:
    ## must ensure that there are never more than one spike per bin per moving window.
    dt = 1e-3
    tcorrlist = arange(-T/4.0,T/4.0+1e-6,dt)
    v1 = get_tvecs(mitral_responses_list,6,central_glom)
    v2 = get_tvecs(mitral_responses_list,6,central_glom+1)
    airxcorrgram = crosscorrgram( v1, v2, dt, T/4.0, starttime, endtime, norm_str )
    v1 = get_tvecs(mitral_responses_list,5,central_glom)
    v2 = get_tvecs(mitral_responses_list,5,central_glom+1)
    odorAxcorrgram = crosscorrgram( v1, v2, dt, T/4.0, starttime, endtime, norm_str )
    v1 = get_tvecs(mitral_responses_list,0,central_glom)
    v2 = get_tvecs(mitral_responses_list,0,central_glom+1)
    odorBxcorrgram = crosscorrgram( v1, v2, dt, T/4.0, starttime, endtime, norm_str )
    #print "unbinned air correlation between central sisters max =",\
    #    max(airxcorrgram)
    #print "unbinned odor A correlation between central sisters max =",\
    #    max(odorAxcorrgram)
    #print "unbinned odor B correlation between central sisters max =",\
    #    max(odorBxcorrgram)

    return((air_corr,odorA_corr,odorB_corr),\
        (tcorrlist,airxcorrgram,odorAxcorrgram,odorBxcorrgram),\
        Dfrates)

def plot_corrs(tcorrlist,airxcorrgram,odorAxcorrgram,odorBxcorrgram):
    fig = figure(facecolor='none')
    ax = fig.add_subplot(111)
    plot(tcorrlist, airxcorrgram, color=(0,0,0), label='air')
    plot(tcorrlist, odorAxcorrgram, color=(1,0,0), label='odor A')
    plot(tcorrlist, odorBxcorrgram, color=(0,1,0), label='odor B')
    biglegend(ax=ax)
    axes_labels(ax,'time (s)','spike probability')
    title('Crosscorrelogram between sisters', fontsize=24)
    show()

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

    (air_corr,odorA_corr,odorB_corr),\
        (tcorrlist,airxcorrgram,odorAxcorrgram,odorBxcorrgram),\
        Dfrates = \
        calc_corrs(filename, "overall")
    plot_corrs(tcorrlist,airxcorrgram,odorAxcorrgram,odorBxcorrgram)

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