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
                            
TITLE Hippocampal HH channels
:
: modified by Katharina Wilmes: rates a and b slower
: 
: Fast Na+ and K+ currents responsible for action potentials
: Iterative equations
:
: Equations modified by Traub, for Hippocampal Pyramidal cells, in:
: Traub & Miles, Neuronal Networks of the Hippocampus, Cambridge, 1991
:
: range variable vtraub adjust threshold
:
: Written by Alain Destexhe, Salk Institute, Aug 1992
:

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

NEURON {
	SUFFIX hh3
	USEION na READ ena WRITE ina
	USEION k READ ek WRITE ik
	RANGE gnabar, gkbar, vtraub
	RANGE m_inf, h_inf, n_inf
	RANGE tau_m, tau_h, tau_n
	RANGE m_exp, h_exp, n_exp
}


UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
}

PARAMETER {
	gnabar	= .003 	(mho/cm2)
	gkbar	= .005 	(mho/cm2)

	ena	= 50	(mV)
	ek	= -90	(mV)
	celsius = 36    (degC)
	dt              (ms)
	v               (mV)
	vtraub	= -63	(mV)
}

STATE {
	m h n
}

ASSIGNED {
	ina	(mA/cm2)
	ik	(mA/cm2)
	il	(mA/cm2)
	m_inf
	h_inf
	n_inf
	tau_m
	tau_h
	tau_n
	m_exp
	h_exp
	n_exp
	tadj
}


BREAKPOINT {
	SOLVE states
	ina = gnabar * m*m*m*h * (v - ena)
	ik  = gkbar * n*n*n*n * (v - ek)
}


:DERIVATIVE states {   : exact Hodgkin-Huxley equations
:	evaluate_fct(v)
:	m' = (m_inf - m) / tau_m
:	h' = (h_inf - h) / tau_h
:	n' = (n_inf - n) / tau_n
:}

PROCEDURE states() {	: exact when v held constant
	evaluate_fct(v)
	m = m + m_exp * (m_inf - m)
	h = h + h_exp * (h_inf - h)
	n = n + n_exp * (n_inf - n)
	VERBATIM
	return 0;
	ENDVERBATIM
}

UNITSOFF
INITIAL {
	m = 0
	h = 0
	n = 0
:
:  Q10 was assumed to be 3 for both currents
:
: original measurements at roomtemperature?

	tadj = 3.0 ^ ((celsius-36)/ 10 )
}

PROCEDURE evaluate_fct(v(mV)) { LOCAL a,b,v2

	v2 = v - vtraub : convert to traub convention

	a = 0.1*0.32 * (13-v2) / ( exp((13-v2)/4) - 1)
	b = 0.2*0.28 * (v2-40) / ( exp((v2-40)/5) - 1)
	tau_m = 1 / (a + b) / tadj
	m_inf = a / (a + b)

	a = 0.128 * exp((17-v2)/18)
	b = 4 / ( 1 + exp((40-v2)/5) )
	tau_h = 1 / (a + b) / tadj
	h_inf = a / (a + b)

	a = 0.032 * (15-v2) / ( exp((15-v2)/5) - 1)
	b = 0.5 * exp((10-v2)/40)
	tau_n = 1 / (a + b) / tadj
	n_inf = a / (a + b)

	m_exp = 1 - exp(-dt/tau_m)
	h_exp = 1 - exp(-dt/tau_h)
	n_exp = 1 - exp(-dt/tau_n)
}

UNITSON







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