3D olfactory bulb: operators (Migliore et al, 2015)

 Download zip file   Auto-launch 
Help downloading and running models
Accession:168591
"... Using a 3D model of mitral and granule cell interactions supported by experimental findings, combined with a matrix-based representation of glomerular operations, we identify the mechanisms for forming one or more glomerular units in response to a given odor, how and to what extent the glomerular units interfere or interact with each other during learning, their computational role within the olfactory bulb microcircuit, and how their actions can be formalized into a theoretical framework in which the olfactory bulb can be considered to contain "odor operators" unique to each individual. ..."
Reference:
1 . Migliore M, Cavarretta F, Marasco A, Tulumello E, Hines ML, Shepherd GM (2015) Synaptic clusters function as odor operators in the olfactory bulb. Proc Natl Acad Sci U S A 112:8499-504 [PubMed]
Citations  Citation Browser
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
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): AMPA; NMDA; Gaba;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON; Python;
Model Concept(s): Activity Patterns; Dendritic Action Potentials; Active Dendrites; Synaptic Plasticity; Action Potentials; Synaptic Integration; Unsupervised Learning; Sensory processing; Olfaction;
Implementer(s): Migliore, Michele [Michele.Migliore at Yale.edu]; Cavarretta, Francesco [francescocavarretta at hotmail.it];
Search NeuronDB for information about:  Olfactory bulb main mitral cell; Olfactory bulb main interneuron granule MC cell; AMPA; NMDA; Gaba; I Na,t; I A; I K; Gaba; Glutamate;
/
figure1eBulb3D
readme.html
ampanmda.mod *
distrt.mod *
fi.mod *
fi_stdp.mod *
kamt.mod *
kdrmt.mod *
naxn.mod *
ThreshDetect.mod *
.hg_archival.txt
all2all.py *
balance.py *
bindict.py
binsave.py
binspikes.py
BulbSurf.py
catfiles.sh
colors.py *
common.py
complexity.py *
custom_params.py *
customsim.py
destroy_model.py *
determine_connections.py
distribute.py *
falsegloms.txt
fixnseg.hoc *
g37e1i002.py
gidfunc.py *
Glom.py *
granule.hoc *
granules.py
grow.py
input-odors.txt *
loadbalutil.py *
lpt.py *
m2g_connections.py
mayasyn.py
mgrs.py
misc.py
mitral.hoc *
mkdict.py
mkmitral.py
modeldata.py *
multisplit_distrib.py *
net_mitral_centric.py
odordisp.py *
odors.py *
odorstim.py
params.py
parrun.py
realgloms.txt *
realSoma.py *
runsim.py
spike2file.hoc *
split.py *
util.py *
vrecord.py
weightsave.py *
                            
: 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 = 50 (1)
	sigslope = 10 (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 = weight*plast(s)
		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
}