Biochemically detailed model of LTP and LTD in a cortical spine (Maki-Marttunen et al 2020)

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Accession:260971
"Signalling pathways leading to post-synaptic plasticity have been examined in many types of experimental studies, but a unified picture on how multiple biochemical pathways collectively shape neocortical plasticity is missing. We built a biochemically detailed model of post-synaptic plasticity describing CaMKII, PKA, and PKC pathways and their contribution to synaptic potentiation or depression. We developed a statistical AMPA-receptor-tetramer model, which permits the estimation of the AMPA-receptor-mediated maximal synaptic conductance based on numbers of GluR1s and GluR2s predicted by the biochemical signalling model. We show that our model reproduces neuromodulator-gated spike-timing-dependent plasticity as observed in the visual cortex and can be fit to data from many cortical areas, uncovering the biochemical contributions of the pathways pinpointed by the underlying experimental studies. Our model explains the dependence of different forms of plasticity on the availability of different proteins and can be used for the study of mental disorder-associated impairments of cortical plasticity."
Reference:
1 . Mäki-Marttunen T, Iannella N, Edwards AG, Einevoll GT, Blackwell KT (2020) A unified computational model for cortical post-synaptic plasticity. Elife [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Synapse;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex spiking regular (RS) neuron;
Channel(s): I Calcium;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s): Glutamate; Norephinephrine; Acetylcholine;
Simulation Environment: NEURON; NeuroRD;
Model Concept(s): Long-term Synaptic Plasticity;
Implementer(s): Maki-Marttunen, Tuomo [tuomomm at uio.no];
Search NeuronDB for information about:  I Calcium; Acetylcholine; Norephinephrine; Glutamate;
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synaptic
L23PC
mechanisms
Ca_HVA.mod *
Ca_LVAst.mod *
CaDynamics_E2.mod *
epsp.mod *
Ih.mod *
Im.mod *
K_Pst.mod *
K_Tst.mod *
Nap_Et2.mod *
NaTa_t.mod *
NaTs2_t.mod *
ProbAMPANMDA_EMS.mod
ProbAMPANMDA_EMS_group.mod
ProbAMPANMDA_EMS_groupdet.mod
ProbAMPANMDA_EMST.mod
ProbAMPANMDA2.mod
ProbAMPANMDA2group.mod
ProbAMPANMDA2groupdet.mod
ProbGABAAB_EMS.mod
ProbGABAAB_EMS_group.mod
ProbUDFsyn2.mod *
ProbUDFsyn2group.mod *
ProbUDFsyn2groupdet.mod *
SK_E2.mod *
SKv3_1.mod *
                            
:Reference :Colbert and Pan 2002

NEURON	{
	SUFFIX NaTa_t
	USEION na READ ena WRITE ina
	RANGE gNaTa_tbar, gNaTa_t, ina
}

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

PARAMETER	{
	gNaTa_tbar = 0.00001 (S/cm2)
}

ASSIGNED	{
	v	(mV)
	ena	(mV)
	ina	(mA/cm2)
	gNaTa_t	(S/cm2)
	mInf
	mTau
	mAlpha
	mBeta
	hInf
	hTau
	hAlpha
	hBeta
}

STATE	{
	m
	h
}

BREAKPOINT	{
	SOLVE states METHOD cnexp
	gNaTa_t = gNaTa_tbar*m*m*m*h
	ina = gNaTa_t*(v-ena)
}

DERIVATIVE states	{
	rates()
	m' = (mInf-m)/mTau
	h' = (hInf-h)/hTau
}

INITIAL{
	rates()
	m = mInf
	h = hInf
}

PROCEDURE rates(){
  LOCAL qt
  qt = 2.3^((34-21)/10)
	
  UNITSOFF
    if(v == -38){
    	v = v+0.0001
    }
		mAlpha = (0.182 * (v- -38))/(1-(exp(-(v- -38)/6)))
		mBeta  = (0.124 * (-v -38))/(1-(exp(-(-v -38)/6)))
		mTau = (1/(mAlpha + mBeta))/qt
		mInf = mAlpha/(mAlpha + mBeta)

    if(v == -66){
      v = v + 0.0001
    }

		hAlpha = (-0.015 * (v- -66))/(1-(exp((v- -66)/6)))
		hBeta  = (-0.015 * (-v -66))/(1-(exp((-v -66)/6)))
		hTau = (1/(hAlpha + hBeta))/qt
		hInf = hAlpha/(hAlpha + hBeta)
	UNITSON
}

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