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;
from pylab import *

## delay - table 1 of Carey et al 2009:
## 154 +- 59 ms from 'inspiration start' to 'max of slope times curvature of response'
delay_mean = 154.0e-3
delay_sd = 59.0e-3
## rise-time - table 2 (anaethetized case): 122 +- 32 ms for 10% to 90% rise time
## Cannot use Gaussian for this - it has a long tail!
## but it is correlated with delay; and the easy formula
## for correlated random variables is for Gaussian random variables.
risetime_mean = 121e-3
risetime_sd = 32e-3
delay_risetime_correlation = 0.3 # delay and rise time are positively correlated.
## duration - table 2 (anaesthetized case): 443 +- 119 ms for response above 50% of peak
duration_mean = 443e-3
duration_sd = 119e-3

def gamma_scale_shape(mean,sd):
    theta = sd**2/mean
    return mean/theta, theta

scale,shape = gamma_scale_shape(delay_mean,delay_sd)
print scale,shape
figure()
gammas = gamma(scale,shape,100000)
hist(gammas,100)
title('delay distrib')
scale,shape = gamma_scale_shape(risetime_mean,risetime_sd)
print scale,shape
figure()
gammas = gamma(scale,shape,100000)
hist(gammas,100)
title('risetime distrib')
scale,shape = gamma_scale_shape(duration_mean,duration_sd)
print scale,shape
figure()
gammas = gamma(scale,shape,100000)
hist(gammas,100)
title('duration distrib')


k,theta = gamma_scale_shape(4.0,sqrt(8.0))
print k,theta
figure()
gammas = gamma(k,theta,100000)
hist(gammas,100)
show()

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