Inhibition of bAPs and Ca2+ spikes in a multi-compartment pyramidal neuron model (Wilmes et al 2016)

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Accession:187603
"Synaptic plasticity is thought to induce memory traces in the brain that are the foundation of learning. To ensure the stability of these traces in the presence of further learning, however, a regulation of plasticity appears beneficial. Here, we take up the recent suggestion that dendritic inhibition can switch plasticity of excitatory synapses on and off by gating backpropagating action potentials (bAPs) and calcium spikes, i.e., by gating the coincidence signals required for Hebbian forms of plasticity. We analyze temporal and spatial constraints of such a gating and investigate whether it is possible to suppress bAPs without a simultaneous annihilation of the forward-directed information flow via excitatory postsynaptic potentials (EPSPs). In a computational analysis of conductance-based multi-compartmental models, we demonstrate that a robust control of bAPs and calcium spikes is possible in an all-or-none manner, enabling a binary switch of coincidence signals and plasticity. ..."
Reference:
1 . Wilmes KA, Sprekeler H, Schreiber S (2016) Inhibition as a Binary Switch for Excitatory Plasticity in Pyramidal Neurons. PLoS Comput Biol 12:e1004768 [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: Neocortex; Hippocampus;
Cell Type(s): Hippocampus CA1 pyramidal GLU cell; Neocortex V1 L6 pyramidal corticothalamic GLU cell;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; Python;
Model Concept(s): Dendritic Action Potentials; Synaptic Plasticity; Synaptic Integration;
Implementer(s): Wilmes, Katharina A. [katharina.wilmes at googlemail.com];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; Neocortex V1 L6 pyramidal corticothalamic GLU cell;
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WilmesEtAl2016
mod_files
cad2.mod *
hh2.mod *
hh3.mod
it2.mod *
kap.mod *
kca.mod *
kdrca1.mod *
na3.mod *
na3dend.mod
na3shifted.mod *
sca.mod *
stdp_ca.mod
stdp_m.mod
                            
COMMENT

changed from (AS Oct0899)
ca.mod
Uses fixed eca instead of GHK eqn

HVA Ca current
Based on Reuveni, Friedman, Amitai and Gutnick (1993) J. Neurosci. 13:
4609-4621.

Author: Zach Mainen, Salk Institute, 1994, zach@salk.edu

ENDCOMMENT

INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}

NEURON {
	SUFFIX sca
	USEION ca READ eca WRITE ica
	RANGE m, h, gca, gbar
	RANGE minf, hinf, mtau, htau, inactF, actF
	GLOBAL q10, temp, tadj, vmin, vmax, vshift
}

PARAMETER {
        inactF = 3
	actF   = 1
	gbar = 0.1   	(pS/um2)	: 0.12 mho/cm2
	vshift = 0	(mV)		: voltage shift (affects all)

	cao  = 2.5	(mM)	        : external ca concentration
	cai		(mM)
						
	temp = 23	(degC)		: original temp 
	q10  = 2.3			: temperature sensitivity

	v 		(mV)
	dt		(ms)
	celsius		(degC)
	vmin = -120	(mV)
	vmax = 100	(mV)
}


UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
	(pS) = (picosiemens)
	(um) = (micron)
	FARADAY = (faraday) (coulomb)
	R = (k-mole) (joule/degC)
	PI	= (pi) (1)
} 

ASSIGNED {
	ica 		(mA/cm2)
	gca		(pS/um2)
	eca		(mV)
	minf 		hinf
	mtau (ms)	htau (ms)
	tadj
}
 

STATE { m h }

INITIAL { 
	trates(v+vshift)
	m = minf
	h = hinf
}

BREAKPOINT {
        SOLVE states
        gca = tadj*gbar*m*m*h
	ica = (1e-4) * gca * (v - eca)
} 

LOCAL mexp, hexp

PROCEDURE states() {
        trates(v+vshift)      
        m = m + mexp*(minf-m)
        h = h + hexp*(hinf-h)
	VERBATIM
	return 0;
	ENDVERBATIM
}


PROCEDURE trates(v) {  
                      
        LOCAL tinc
        TABLE minf, mexp, hinf, hexp
	DEPEND dt, celsius, temp, inactF
	
	FROM vmin TO vmax WITH 199

	rates(v): not consistently executed from here if usetable == 1

        tadj = q10^((celsius - temp)/10)
        tinc = -dt * tadj

        mexp = 1 - exp(tinc/mtau)
        hexp = 1 - exp(tinc/htau)
}


PROCEDURE rates(vm) {  
        LOCAL  a, b

	a = 0.055*(-27 - vm)/(exp((-27-vm)/3.8) - 1)/actF
	b = 0.94*exp((-75-vm)/17)/actF
	
	mtau = 1/(a+b)
	minf = a*mtau

		:"h" inactivation 

	a = 0.000457*exp((-13-vm)/50)/inactF
	b = 0.0065/(exp((-vm-15)/28) + 1)/inactF

	htau = 1/(a+b)		: originally *1
	hinf = a*htau
}

FUNCTION efun(z) {
	if (fabs(z) < 1e-4) {
		efun = 1 - z/2
	}else{
		efun = z/(exp(z) - 1)
	}
}

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