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]
Citations  Citation Browser
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 cell; Olfactory bulb main interneuron granule MC 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 cell; Olfactory bulb main interneuron granule MC 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
                            
: copied by Hines from Exp2syn and added spike dependent plasticity
COMMENT
Two state kinetic scheme synapse described by rise time tau1,
and decay time constant tau2. The normalized peak condunductance is 1.
Decay time MUST be greater than rise time.

The solution of A->G->bath with rate constants 1/tau1 and 1/tau2 is
 A = a*exp(-t/tau1) and
 G = a*tau2/(tau2-tau1)*(-exp(-t/tau1) + exp(-t/tau2))
	where tau1 < tau2

If tau2-tau1 -> 0 then we have a alphasynapse.
and if tau1 -> 0 then we have just single exponential decay.

The factor is evaluated in the
initial block such that an event of weight 1 generates a
peak conductance of 1.

Because the solution is a sum of exponentials, the
coupled equations can be solved as a pair of independent equations
by the more efficient cnexp method.

ENDCOMMENT

NEURON {
	POINT_PROCESS FastInhib
	RANGE tau1, tau2, e, i
	NONSPECIFIC_CURRENT i
	RANGE gmax
	RANGE x, mgid, ggid, srcgid
	GLOBAL ltdinvl, ltpinvl, sighalf, sigslope

	RANGE g
}

UNITS {
	(nA) = (nanoamp)
	(mV) = (millivolt)
	(uS) = (microsiemens)
}

PARAMETER {
	tau1=1 (ms) <1e-9,1e9>
	tau2 = 200 (ms) <1e-9,1e9>
	gmax = .003 (uS) 
	e = -80	(mV)
	ltdinvl = 250 (ms)		: longer intervals, no change
	ltpinvl = 33.33 (ms)		: shorter interval, LTP
	sighalf = 25 (1)
	sigslope = 3 (1)
	x = 0 (um) : cartesian synapse location
	mgid = -1 : associated mitral gid
	ggid = -1 : associated granule gid
	srcgid = -1 : the gid of the granule detector
}

ASSIGNED {
	v (mV)
	i (nA)
	g (uS)
	factor
	w (uS)
	total (uS)
}

STATE {
	A
	B
}

INITIAL {
	LOCAL tp
	if (tau1/tau2 > .9999) {
		tau1 = .9999*tau2
	}
	A = 0
	B = 0
	tp = (tau1*tau2)/(tau2 - tau1) * log(tau2/tau1)
	factor = -exp(-tp/tau1) + exp(-tp/tau2)
	factor = 1/factor
}

BREAKPOINT {
	SOLVE state METHOD cnexp
	g = (B - A)*gmax
	i = g*(v - e)
}

DERIVATIVE state {
	A' = -A/tau1
	B' = -B/tau2
}

FUNCTION plast(step(1))(1) {
	plast = 1 - 1/(1 + exp((step - sighalf)/sigslope))
}

NET_RECEIVE(weight, s, w, tlast (ms)) {
	INITIAL {
		s = 0
		w = 0
		tlast = -1e9(ms)
	}
	if (t - tlast < ltpinvl) { : LTP
		s = s + 1
		if (s > 2*sighalf) { s = 2*sighalf }
	}else if (t - tlast > ltdinvl) { : no change
	}else{ : LTD
		s = s - 1
		if (s < 0) { s = 0 }
	}
	tlast = t
	w = weight*plast(s)
	A = A + w*factor
	B = B + w*factor
}