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

from OBNetwork import *
from stimuliConstants import * # has SETTLETIME, inputList and pulseList, GLOMS_ODOR, GLOMS_NIL
from simset_odor import * # has REALRUNTIME, NUMBINS
from sim_utils import * # has rebin() and imports data_utils.py for axes_off()
from data_utils import * # has mpi import and variables also

plot_images=False#True ## plot Adil style images?
## below requires scipy which requires lapack (present only on gj not on nodes)
## hence import only if not running in parallel
if plot_images and mpisize == 1:
    from fit_odor_morphs import * # has fit_morphs()

from pylab import * # part of matplotlib that depends on numpy but not scipy

NUMBINS = 17
BIN_WIDTH_TIME = RESPIRATION/8.0
fitted_mitral = 2*central_glom+0

def plot_varinh_responses(mitral_responses_avg,mitral_responses_se):

    if ONLY_TWO_MITS: mitlist = range(MIT_SISTERS)
    else: mitlist = range(NUM_GLOMS*MIT_SISTERS)
    for mitnum in mitlist:
        figure()
        title('Mitral '+str(mitnum))
        for inhnum in range(NUMINHS):
            sister_ratio = (mitnum%MIT_SISTERS)/float(MIT_SISTERS)
            errorbar(x=range(NUMBINS),y=mitral_responses_avg[inhnum,mitnum],\
                yerr=mitral_responses_se[inhnum,mitnum],\
                color=(inhnum/float(NUMINHS),1-inhnum/float(NUMINHS),0))

def read_odorresults_file(filename):
    f = open(filename,'r')
    (mitral_responses_list,mitral_responses_binned_list) = pickle.load(f)
    f.close()
    mitral_responses_binned_list = \
        rebin(mitral_responses_list, numbins=NUMBINS, bin_width_time=BIN_WIDTH_TIME)

    numavgs = len(mitral_responses_list)
    mitral_responses_avg = mean(mitral_responses_binned_list, axis=0)
    mitral_responses_std = std(mitral_responses_binned_list, axis=0)
    ## since I plot the mean response, I must plot standard error of the mean
    ## = standard deviation of a repeat / sqrt(num of repeats).
    mitral_responses_se = mitral_responses_std/sqrt(numavgs)
    
    return mitral_responses_avg, mitral_responses_se

def plot_varinh(filename):
    
    mitral_responses_avg, mitral_responses_se =\
        read_odorresults_file(filename)

    #### Usual firing rate vs time plots of responses
    plot_varinh_responses(mitral_responses_avg,mitral_responses_se)

    show()

def plot_varinh_diff(fn1,fn2):
    mitral_responses_avg1, mitral_responses_se1 =\
        read_odorresults_file(fn1)
    mitral_responses_avg2, mitral_responses_se2 =\
        read_odorresults_file(fn2)

    if ONLY_TWO_MITS:
        mitral_responses_avg = mitral_responses_avg1[:,:2] - mitral_responses_avg2[:,:2]
        mitral_responses_se = sqrt( mitral_responses_se1[:,:2]**2 + mitral_responses_se2[:,:2]**2 )
    else:
        mitral_responses_avg = mitral_responses_avg1 - mitral_responses_avg2
        mitral_responses_se = sqrt( mitral_responses_se2**2 + mitral_responses_se2**2 )

    #### plot (difference of firing rates) vs time
    plot_varinh_responses(mitral_responses_avg,mitral_responses_se)

if __name__ == "__main__":
    if len(sys.argv)<2:
        print "You need to specify the morph responses pickle filename."
        sys.exit(1)
    elif len(sys.argv)==2:
        plot_varinh(sys.argv[1])
    else:
        plot_varinh_diff(sys.argv[1],sys.argv[2])
    show()

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