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 generate_firerates_odors import *

#### We have dual sigmoid functions as responses for
#### respiration, odor A and odor B (each is different for every glomerulus).
#### These responses are deconvolved with respiration to get kernels.
#### For Adil type odor morph experiments, these kernels are again convolved with respiration.
#### For Priyanka's data, these kernels are convolved with odor and air pulses.
#### This only gives firing rates.
#### The firing rate as a function of time is fed
#### to a Poisson spike generator in generate_firefiles.py .

frateOdorList = []
kernels = []

def compute_doublesigmoid_params(delay, risetime, duration):
    """ return the double sigmoid params for the given delay, risetime and duration."""
    ######### get double sigmoid params
    spread2_factor = 4.0 # not used presently
    spread1, center2, spread2 = \
        compute_dblsigmoid_params(risetime, duration, spread2_factor)
    #spread2 = spread2_factor*spread1
    ######### check if the solved params are reasonable
    ######### risetime and duration match and spread2 is at least twice spread1.
    actual_risetime = invert_dblsigmoid(0.0, spread1, center2, spread2, 0.9) - \
        invert_dblsigmoid(0.0, spread1, center2, spread2, 0.1)
    actual_duration = invert_dblsigmoid(0.0, spread1, center2, spread2, 0.5, risephase=False) - \
        invert_dblsigmoid(0.0, spread1, center2, spread2, 0.5)
    if abs(actual_risetime-risetime)>1e-4 or abs(actual_duration-duration)>1e-4:
        print "The actual risetime and duration are", actual_risetime, actual_duration
        print "But the required risetime and duration are", risetime, duration
        sys.exit(1)
    if spread2 < 2*spread1:
        print "spread2 < 2*spread1, spread1 =",spread1,", spread2 =",spread2
        sys.exit(1)
    ########## shift curve to include latency
    ## I had taken center1 = 0.0, now shift the curve
    ## so that t=0 is actually where first sigmoid is 0.05*peak
    ## and then add the delay / latency to it!
    offset = - invert_dblsigmoid(0.0, spread1, center2, spread2, 0.05) + delay
    return [offset, spread1, center2+offset, spread2]

def variedinh_stimuli():
    ## mitral and PG odor ORNs firing files

    ## glom0 - odor kernel
    odorparams0_e = [FIRINGMEANA]
    odorparams0_e.extend( compute_doublesigmoid_params(2*delay_mean,1.5*risetime_mean,1.5*duration_mean) )
    odorparams0_i = [0.0]
    odorparams0_i.extend( compute_doublesigmoid_params(delay_mean,risetime_mean,duration_mean) )
    kernel0odor = getkernel(odorparams0_e,odorparams0_i)
    ## glom0 - air kernel
    odorparams0_e = [0.0]#[FIRINGRATEAIR_MEAN]
    odorparams0_e.extend( compute_doublesigmoid_params(delay_mean,risetime_mean,duration_mean) )
    odorparams0_i = [0.0]
    odorparams0_i.extend( compute_doublesigmoid_params(delay_mean,risetime_mean,duration_mean) )
    kernel0air = getkernel(odorparams0_e,odorparams0_i)
    kernels.append((kernel0air,kernel0odor))
    ## glom0 - firing rates
    ## odorparams are relics of non-kernel era,
    ## actually not used by receptorFiringRate(), only kernels are used.
    frate = receptorFiringRate(1.0, 1.0, 0.0,\
        odorparams0_e, odorparams0_i,\
        odorparams0_e, odorparams0_i,\
        odorparams0_e, kernel0odor, kernel0air, kernel0air)
    frateOdorList.append([frate])

    risetime = risetime_mean
    duration = duration_mean
    for glomnum in range(1,NUM_GLOMS):
        print "Computing kernels and firing rates for glomerulus", glomnum

        ### For each glomerulus, set its responses to odor and air
        ### as difference of excitatory and inhibitory random dual-exponentials
        ### for each different respiration phase
        
        kernels.append([])        
        frateOdorList.append([])
        delta_delay = (delay_mean+3*delay_sd)/float(NUMINHS)
        for delay in arange(0.0,delay_mean+3*delay_sd,delta_delay):
            print "Computing kernels and firing rates for delay =", delay

            odorparamsA_e = [FIRINGMEANA]
            odorparamsA_e.extend( compute_doublesigmoid_params(delay,risetime,duration) )
            odorparamsA_i = [0.0]
            odorparamsA_i.extend( compute_doublesigmoid_params(delay,risetime,duration) )

            ## kernel for Priyanka can be obtained
            ## by numerically deconvolving with the respiration pulse.
            kernelA = getkernel(odorparamsA_e,odorparamsA_i)

            ### respiration also has both excitatory and inhibitory dual exponentials.
            odorparamsR_e = [FIRINGRATEAIR_MEAN]
            odorparamsR_e.extend( compute_doublesigmoid_params(delay,risetime,duration) )
            odorparamsR_i = [0.0]
            odorparamsR_i.extend( compute_doublesigmoid_params(delay,risetime,duration) )

            kernelR = getkernel(odorparamsR_e,odorparamsR_i)

            kernels[-1].extend( (kernelR,kernelA) )

            # firing rates
            frate = receptorFiringRate(1.0, 1.0, 0.0,\
                odorparamsA_e, odorparamsA_i,\
                odorparamsR_e, odorparamsR_i,\
                odorparamsR_e, kernelA, kernelR, kernelR)
            ## important to put within [] or (...,) for extend
            frateOdorList[-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([stim_rate_seednum]) ##### Seed numpy's random number generator. If no parameter is given, it uses current system time

    variedinh_stimuli()

    filename = 'firerates/firerates_2sgm_variedinh.pickle'
    fireratefile = open(filename,'w')
    pickle.dump( (frateOdorList,kernels), fireratefile)
    fireratefile.close()
    print "wrote",filename

    # glom0 & glom1
    figure(facecolor='w')
    title('Glomerulus 0 & 1')
    xlabel('time (s)', fontsize='large')
    ylabel('firing rate (Hz)', fontsize='large')
    plot(firingtsteps, frateOdorList[0][0], color=(0,0,0), marker=',')
    totinh = float(len(frateOdorList[1]))
    for inhidx,frate in enumerate(frateOdorList[1]):
        plot(firingtsteps, frate, color=(inhidx/totinh,1-inhidx/totinh,0), marker=',')
    
    show()