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]
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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, pickle

sys.path.extend(["..","../networks","../simulations"])
from networkConstants import *
from stimuliConstants import *
from simset_odor import *

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

from data_utils import * # has axes_labels()

## USAGE: python2.6 plot_firerates_odors.py <firefiles_name>
## EXAMPLE: python2.6 plot_firefiles.py ../firefiles/firefiles760.0/firetimes_rndpulse_glom_0_pulse_5_avgnum0.txt

#### We have dual exponential functions as impulse response / kernels
#### for respiration, odor A and odor B (each is different for every glomerulus)
#### These kernels are convolved with the respiration pulse
#### to generate the firing rate which is fed to a Poissonian generator.

RUNTIME = PULSE_RUNTIME + SETTLETIME

## time points for the firing rate which is read from a pickled file
firingtsteps = arange(0,RUNTIME+1e-10,FIRINGFILLDT)# include the last RUNTIME point also.
numt = len(firingtsteps)
extratime = arange(0,3*RESPIRATION+1e-10,FIRINGFILLDT)
randompulsetime = arange(0,PULSE_RUNTIME+1e-10,FIRINGFILLDT)

if __name__ == "__main__":
    fname = sys.argv[1]
    f = open(fname,'r')
    lines = f.read() ## reads everything
    f.close()
    strtimes = lines.split()
    firetimes = [ float(strtime) for strtime in strtimes ]
    firetimes.sort()
    
    bindt = 25e-3
    if 'pulse' in fname:
        bintimes = arange(0.0,PULSE_RUNTIME,bindt)
        binfrate = array(plotBins(firetimes, int(round(PULSE_RUNTIME/bindt)),\
            PULSE_RUNTIME+SETTLETIME, SETTLETIME)) / NUM_ORN_FILES_PER_GLOM
    else:
        bintimes = arange(0.0,ODORRUNTIME-SETTLETIME,bindt)
        binfrate = array(plotBins(firetimes, int(round((ODORRUNTIME-SETTLETIME)/bindt)),\
            ODORRUNTIME, SETTLETIME)) / NUM_ORN_FILES_PER_GLOM

    fig = figure(facecolor='w')
    ax = fig.add_subplot(111)
    title(fname, fontsize=24)
    frateperglomList = []
    plot(bintimes[:len(binfrate)], binfrate, '-r', linewidth=2, marker=',')
    axes_labels(ax,'time (s)','ORN firing rate (Hz)',fontsize=24)
    
    show()