Synaptic integration by MEC neurons (Justus et al. 2017)

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Accession:222359
Pyramidal cells, stellate cells and fast-spiking interneurons receive running speed dependent glutamatergic input from septo-entorhinal projections. These models simulate the integration of this input by the different MEC celltypes.
Reference:
1 . Justus D, Dalügge D, Bothe S, Fuhrmann F, Hannes C, Kaneko H, Friedrichs D, Sosulina L, Schwarz I, Elliott DA, Schoch S, Bradke F, Schwarz MK, Remy S (2017) Glutamatergic synaptic integration of locomotion speed via septoentorhinal projections. Nat Neurosci 20:16-19 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Entorhinal cortex;
Cell Type(s): Entorhinal cortex pyramidal cell; Entorhinal cortex stellate cell; Entorhinal cortex fast-spiking interneuron;
Channel(s): I K; I Na,t; I h;
Gap Junctions:
Receptor(s): AMPA;
Gene(s):
Transmitter(s): Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Synaptic Integration; Simplified Models;
Implementer(s): Justus, Daniel [daniel.justus at dzne.de];
Search NeuronDB for information about:  AMPA; I Na,t; I K; I h; Glutamate;
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NEURON_mEC
data
README.html
exp2syn_depress.mod
h.mod *
kap.mod *
kdr.mod *
nax.mod
vecevent.mod *
cinit.hoc
EPSPparam.hoc
GUI.hoc
GUIfunctions.hoc
init.hoc
insert_noise_Syn.hoc
insert_real_Syn.hoc
insertsyn.hoc
morphology.hoc
mosinit.hoc *
Parameters.hoc
run_real_input.hoc
screenshot1.png
screenshot2.png
screenshot3.png
Voltage.ses
                            
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 Exp2Syn_depress
	RANGE tau1, tau2, e, i, tau_recover, attenuation
	NONSPECIFIC_CURRENT i

	RANGE g
}

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

PARAMETER {
	tau1=.1 (ms) <1e-9,1e9>
	tau2 = 10 (ms) <1e-9,1e9>
	tau_recover = 100 (ms) <1e-9,1e9>
	attenuation = .8 <1e-9,1>
	e=0	(mV)
}

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

STATE {
	A (uS)
	B (uS)
}

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
	i = g*(v - e)
}

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

NET_RECEIVE(weight (uS),tsyn (ms),weight_attenuate ) {

	INITIAL{
		weight_attenuate =1
		tsyn=-1000
	}
	weight_attenuate  = (weight_attenuate -1) * exp(-1/tau_recover*(t-tsyn)) +1
	A = A + weight*factor*weight_attenuate 
	B = B + weight*factor*weight_attenuate 
	tsyn=t

	
	weight_attenuate  = weight_attenuate *attenuation

}

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