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

from pylab import *
import numpy

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

#### Generate sinusoids of different frequencies and amplitudes
#### to feed to gloms individually to obtain frequency response.
#### Cut off at zero below, and stimuliConstants has mean firing rate for odors,
#### keep amplitude less than mean firing rate to avoid cutoff,
#### and variance in firing rate of different ORNs is same as mean.
#### The firing rate as a function of time is fed
#### to a Poisson spike generator in generate_firefiles_sinusoids.py .

frateResponseList = []

sinepulsetime = arange(0,SIN_RUNTIME,FIRINGFILLDT)
len_pulsetime = len(sinepulsetime)

def firingRateSinusoid(DC,ampl,f):
    ## in Hz
    ## array of Gaussian distributed firing rates at each time point
    ## mean = FIRINGMEANA, standard deviation = sqrt(FIRINGMEANA)
    #pulse_steps = normal(loc=FIRINGMEANA,scale=sqrt(FIRINGMEANA),size=len_pulsetime)
    
    pulse_steps = array( [ DC + ampl*sin(2*pi*t*f) for t in sinepulsetime ] )
    
    ## clip firing rates below zero; in-place hence pulse_steps is also the output
    clip(pulse_steps,0,1e6,pulse_steps)
    return array(pulse_steps)

def sinusoid_stimuli():
    ## firing rates to generate Poisson input to mitrals and PGs
    for glomnum in range(NUM_GLOMS):
        frateResponseList.append([])
        for sine_f in sine_frequencies:
            ## firing rates
            frate = firingRateSinusoid(sine_ORN_mean,sine_amplitude,sine_f)
            ## important to put within [] or (...,) for extend
            frateResponseList[-1].extend([frate])

if __name__ == "__main__":
    ### Seed only if called directly, else do not seed.
    ### Also seeding this way ensures seeding after importing other files that may set seeds.
    ### Thus this seed overrides other seeds.
    seed([123.0])#[stim_rate_seednum]) ##### Seed numpy's random number generator.

    sinusoid_stimuli()

    filename = 'firerates/firerates_sinusoids_seed'+str(stim_rate_seednum)+\
        '_ampl'+str(sine_amplitude)+'_mean'+str(sine_ORN_mean)+'.pickle'
    fireratefile = open(filename,'w')
    pickle.dump( frateResponseList, fireratefile)
    fireratefile.close()
    print "wrote",filename
    
    figure(facecolor='w')
    title('psd of sinusoid')
    frate = frateResponseList[0][0]
    fftsq = abs(fft(array(frate)-frate.mean()))**2.0
    plot(fftsq**0.5)

    # glom0 & glom1
    figure(facecolor='w')
    title('Glomerulus 0 & 1')
    xlabel('time (s)', fontsize='large')
    ylabel('firing rate (Hz)', fontsize='large')
    plot(sinepulsetime, frateResponseList[0][0], color=(1,0,0))
    plot(sinepulsetime, frateResponseList[1][0], color=(0,1,0))
    
    show()