Look-Up Table Synapse (LUTsyn) models for AMPA and NMDA (Pham et al., 2021)

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Accession:267103
Fast input-output synapse model of glutamatergic receptors AMPA and NMDA that can capture nonlinear interactions via look-up table abstraction. Speeds are comparable to 'linear' exponential synapses. Download LUT files at: https://senselab.med.yale.edu/modeldb/data/267103/LUTs.zip
Reference:
1 . Pham DJ, Yu GJ, Bouteiller JC, Berger TW (2021) Bridging Hierarchies in Multi-Scale Models of Neural Systems: Look-Up Tables Enable Computationally Efficient Simulations of Non-linear Synaptic Dynamics Front. Comput. Neurosci.
Model Information (Click on a link to find other models with that property)
Model Type: Channel/Receptor; Synapse;
Brain Region(s)/Organism: Dentate gyrus;
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s): AMPA; NMDA;
Gene(s):
Transmitter(s): Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Multiscale;
Implementer(s):
Search NeuronDB for information about:  AMPA; NMDA; Glutamate;
COMMENT
_v2 edited by Jonathan Pham to be able to edit (rather than using exp2syn from nrn library)

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 is very small compared to tau1, this is an alphasynapse with time constant tau2.
If tau1/tau2 is very small, this is single exponential decay with time constant tau2.

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 Exp2Syn_v2
	RANGE tau1, tau2, e, i, v1
	NONSPECIFIC_CURRENT i

	RANGE g
}

UNITS {
	(pA) = (picoamp)
	(mV) = (millivolt)
	(nS) = (nanosiemens)
}

PARAMETER {
	tau1 = 0.1 (ms) <1e-9,1e9>
	tau2 = 10 (ms) <1e-9,1e9>
	e=0	(mV)
}

ASSIGNED {
	v (mV)
	v1 (mV)
	i (pA)
	g (nS)
	factor
}

STATE {
	A (nS)
	B (nS)
}

INITIAL {
	LOCAL tp
	if (tau1/tau2 > 0.9999) {
		tau1 = 0.9999*tau2
	}
	if (tau1/tau2 < 1e-9) {
		tau1 = tau2*1e-9
	}
	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
	i = g*(v - e)
	v1 = v
}

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

NET_RECEIVE(weight (nS)) {
	A = A + weight*factor
	B = B + weight*factor
}

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