Large scale model of the olfactory bulb (Yu et al., 2013)

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Accession:144570
The readme file currently contains links to the results for all the 72 odors investigated in the paper, and the movie showing the network activity during learning of odor k3-3 (an aliphatic ketone).
Reference:
1 . Yu Y, McTavish TS, Hines ML, Shepherd GM, Valenti C, Migliore M (2013) Sparse distributed representation of odors in a large-scale olfactory bulb circuit. PLoS Comput Biol 9:e1003014 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Channel/Receptor; Dendrite;
Brain Region(s)/Organism: Olfactory bulb;
Cell Type(s): Olfactory bulb main mitral GLU cell; Olfactory bulb main interneuron granule MC GABA cell;
Channel(s): I Na,t; I A; I K;
Gap Junctions:
Receptor(s): NMDA; Glutamate; Gaba;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Pattern Recognition; Activity Patterns; Bursting; Temporal Pattern Generation; Oscillations; Synchronization; Active Dendrites; Detailed Neuronal Models; Synaptic Plasticity; Action Potentials; Synaptic Integration; Unsupervised Learning; Olfaction;
Implementer(s): Hines, Michael [Michael.Hines at Yale.edu]; Migliore, Michele [Michele.Migliore at Yale.edu];
Search NeuronDB for information about:  Olfactory bulb main mitral GLU cell; Olfactory bulb main interneuron granule MC GABA cell; NMDA; Glutamate; Gaba; I Na,t; I A; I K; Gaba; Glutamate;
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YuEtAl2012
readme.html
ampanmda.mod
fi.mod
kamt.mod *
kdrmt.mod *
naxn.mod *
ThreshDetect.mod *
.hg_archival.txt
allsynhinton.hoc *
antest.ses *
clear.hoc *
connect.hoc
control.ses
default.hoc
granule.hoc *
hinton.hoc
init.hoc *
iterator.hoc *
lindgren.job
lptiter.hoc
mgrs.hoc
michele_movie.hoc
mitral.hoc
mosinit.hoc *
net.hoc
odors.txt
odors-forsim500-kensaku.txt
param.hoc
parinit.hoc
pattern.hoc
perfrun.hoc
record.hoc
show.hoc
showstim.hoc
showw.hoc
somesyn.hoc *
spike2file.hoc
spkdat2bin.hoc
split.hoc
start.hoc
start.ses *
stim-AB-rnd-500mt.hoc
stim-o11o12.hoc
stim-o14.hoc
stim-o26.hoc
stim-o26d1-mnoise5hz-gnoise-5s.hoc
stim-o5high-o6low.hoc
stim-odors-AB-seq.hoc
stim-pair.hoc
stim-seq-rnd.hoc
subset.hoc
subset_control.ses *
viewspikes.hoc
viewspikes1.hoc
weight_movie.hoc *
weightsave.hoc
                            
// write weight file, read weight file, initialize weights
// weight file format is
//nweight
//t
//srcgid targid s w   : nweight of these
//any number of above two

////////create a weight file
weight_start_ = 1e9
weight_interval_ = 1e9
objref fih_weight_, weight_file_
proc weight_snapshots() {local i, nw   localobj f
	weight_file_ = new File($s1)
	f = weight_file_
	weight_start_ = $2
	weight_interval_ = $3
	fih_weight_ = new FInitializeHandler(2, "w1_()")
	nw = 0
	for i=0, mgrs_list.count-1 {
		if (object_id(mgrs_list.object(i).md2ampanmda)) { nw += 1 }
		if (object_id(mgrs_list.object(i).gd2fi)) { nw += 1 }
	}
	nw = pc.allreduce(nw,1)
	if (pc.id == 0) {
		f.wopen()
		f.printf("%d\n", nw)
		f.close()
	}
}

proc w1_() { cvode.event(weight_start_, "w2_()") }

proc w2_() {local i  localobj f
	f = weight_file_
	for pnm.serialize() {
		f.aopen()
		if (pc.id == 0) {
			f.printf("%g\n", t)
		}
		for i=0, mgrs_list.count-1 {
			f.printf("%s\n", mgrs_list.object(i).ws_str().s)
		}
		f.close()
	}
	cvode.event(t + weight_interval_, "w2_()")
}
//////////// end of create a weight file

/////////// initialize weight from first group of a weight file
objref fih_weight_init_, weight_init_file_
proc weight_initialize() {
	weight_init_file_ = new File($s1)
	// after NET_RECEIVE INITIAL
	fih_weight_init_ = new FInitializeHandler(1, "w3_()")
}

proc w3_() {local i, nw, srcgid, targid, s, w, sgid \
    localobj f, map, newmap, mgrs, p, nc, syn, ncl
	f = weight_init_file_
	f.ropen()
	nw = f.scanvar()
	f.scanvar() // discard t
	p = new PythonObject()
	nrnpython("newmap = lambda key, value : {key:value}")
	map = p.newmap(-1,0)
	for i=0, nw - 1 {
		srcgid = f.scanvar
		targid = f.scanvar
		s = f.scanvar
		w = f.scanvar
		sgid = syn_gid(srcgid, targid)
//		if (pc.gid_exists(sgid)) {// correct even if multisplit
			// no way at present to efficiently derive the
			// MGRS from the srcgid, targid but we know it
			// exists. So save data in a Python map and
			// retrieve next loop
			map.update(p.newmap(sgid, s))
//printf("map %d %d %d %d\n", srcgid, targid, ncell, s)
//		}
	}
	f.close()
	// sweep mgrs_list
	for i=0, mgrs_list.count-1 {
		mgrs = mgrs_list.object(i)
		if (object_id(mgrs.gd2fi)) {
			s = map.__getitem__(mgrs.md_gid)
//printf("%d %d %g\n", mgrs.granule_gid, mgrs.mitral_gid, s)
			mgrs.set_sm(s)
		}
		if (object_id(mgrs.md2ampanmda)) {
			s = map.__getitem__(mgrs.gd_gid)
//printf("%d %d %g\n", mgrs.mitral_gid, mgrs.granule_gid, s)
			mgrs.set_sg(s)
		}
	}
}

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