Olfactory bulb microcircuits model with dual-layer inhibition (Gilra & Bhalla 2015)

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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.
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;
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, math
from PyQt4 import QtGui,QtCore
from PyQt4.Qt import Qt
import time#, sched
import pickle
from updatepaintGL_aditya2 import newGLWindow

from oglfunc.objects import *
from oglfunc.group import *

## USAGE: python replay_sim3D.py <data file>
## TYPICAL: python replay_sim3D.py sim_saved.pickle

#s = sched.scheduler(time.time, time.sleep)

def rotate_y(x,y,z,theta):
    return (x*math.cos(theta)-z*math.sin(theta), y, x*math.sin(theta)+z*math.cos(theta))

f = open(sys.argv[1],'r')
q = pickle.load(f)

def updateframe(w,framenum):
    if framenum>=2:
        frameoldold = framenum-2
        frameold = framenum-1
    elif framenum>=1:
        frameoldold = framenum-1
        frameold = framenum-1
        frameoldold = framenum
        frameold = framenum
    dmin = w.minVal
    for i,vizObject in enumerate(w.vizObjects):
        ## decay the spike slowly over next three frames
        dbin = [q[framenum][i],q[frameold][i],q[frameoldold][i]]
        dmaxnow = max(dbin)
        d = (dmaxnow-dmin)/(dbin.index(dmaxnow)*0.3+1.0)
        cell_identity = vizObject.cell_identity
        if cell_identity == 0:
            idx = int(d*w.factor1)
        elif cell_identity == 1:
            idx = int(d*w.factor2)
        elif cell_identity == 2:
            idx = int(d*w.factor3)
def separate_cells(w):
    for i,vizObject in enumerate(w.vizObjects):
        ## moose path is stored in obj.l_coords[-1]
        obj_moosepath = vizObject.l_coords[-1]
        if 'mitral' in obj_moosepath:
            vizObject.cell_identity = 0
        elif 'granule' in obj_moosepath:
            vizObject.cell_identity = 1
        elif 'PG' in obj_moosepath:
            vizObject.cell_identity = 2

app = QtGui.QApplication(sys.argv)
newWin = newGLWindow()
w =  newWin.mgl ## instance of updatePaintGL
## don't have lights - colors should be the same from any direction
w.lights = False
## turn on visualization, use with self.qgl.updateViz()	
w.viz = 1 # after this all cells drawn will get color updated
## set the color map for visualization

## last entry in q is configuration of objects in 3D
config = q[-1]
for i in range(len(config)):
    a = locals()[config[i][0]](w,config[i][1],config[i][2])

tiltangle = 50.0 # degr
tot_rot = 20.0
## rotate during simulation from -tot_rot/2.0 to +tot_rot/2.0
w.rotate([0,0,1],-tot_rot/2.0) # rotate about axis-vector (z-axis) by xx (10) degrees
## view at tilt angle
w.rotate([1,0,0],-50.0) # rotate about axis-vector (y-axis) by xx (-50) degrees
#w.rotate([0,1,0],-10) # rotate about axis-vector (y-axis) by xx (-10) degrees
x0,y0,z0 = (-1.5,-4,-130)
#x0,y0,z0 = (0,-4.25,-130)

## categorize cells upfront as mit/gran/PG based on moose path,
## else too expensive to repeat string check for each frame

framemin = 600
framemax = 900
theta_dt = tot_rot/float(framemax-framemin) # degrees

## leave last entry in q which is configuration, rest are Vm-s at different times
for framenum,vals in enumerate(q[0:-1]):
    if framenum<600 or framenum>900: continue
    ## translate and rotate back to original config
    ## rotate about y
    ## again tilt it and translate for viewing
    print "at framenum =",framenum
    ## save pictures
    ####### grabFrameBuffer() has problems: a frame in 300 or so gets skewed / corrupted!
    pic = w.grabFrameBuffer()