Effects of increasing CREB on storage and recall processes in a CA1 network (Bianchi et al. 2014)

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Accession:151126
Several recent results suggest that boosting the CREB pathway improves hippocampal-dependent memory in healthy rodents and restores this type of memory in an AD mouse model. However, not much is known about how CREB-dependent neuronal alterations in synaptic strength, excitability and LTP can boost memory formation in the complex architecture of a neuronal network. Using a model of a CA1 microcircuit, we investigate whether hippocampal CA1 pyramidal neuron properties altered by increasing CREB activity may contribute to improve memory storage and recall. With a set of patterns presented to a network, we find that the pattern recall quality under AD-like conditions is significantly better when boosting CREB function with respect to control. The results are robust and consistent upon increasing the synaptic damage expected by AD progression, supporting the idea that the use of CREB-based therapies could provide a new approach to treat AD.
Reference:
1 . Bianchi D, De Michele P, Marchetti C, Tirozzi B, Cuomo S, Marie H, Migliore M (2014) Effects of increasing CREB-dependent transcription on the storage and recall processes in a hippocampal CA1 microcircuit. Hippocampus 24:165-77 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Hippocampus CA1 pyramidal cell; Hippocampus CA1 interneuron oriens alveus cell; Hippocampus CA1 basket cell;
Channel(s): I Na,t; I A; I K; I M; I h; I K,Ca; I Calcium; I_AHP; I Cl, leak; Ca pump;
Gap Junctions:
Receptor(s): GabaA; GabaB; AMPA; NMDA;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): STDP; Aging/Alzheimer`s; Depolarization block; Storage/recall; CREB;
Implementer(s): Bianchi, Daniela [danielabianchi12 -at- gmail.com]; De Michele, Pasquale [pasquale.demichele at unina.it];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal cell; Hippocampus CA1 interneuron oriens alveus cell; GabaA; GabaB; AMPA; NMDA; I Na,t; I A; I K; I M; I h; I K,Ca; I Calcium; I_AHP; I Cl, leak; Ca pump; Gaba; Glutamate;
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Bianchietal
Results
Weights
readme.txt
ANsyn.mod *
bgka.mod *
burststim2.mod
cad.mod
cagk.mod *
cal.mod *
calH.mod
car.mod *
cat.mod *
ccanl.mod *
d3.mod *
gskch.mod *
h.mod
IA.mod
ichan2.mod *
Ih.mod *
kadist.mod
kaprox.mod
Kaxon.mod *
kca.mod *
Kdend.mod *
kdr.mod
kdrax.mod
km.mod
Ksoma.mod *
LcaMig.mod *
my_exp2syn.mod *
na3.mod
na3dend.mod
na3notrunk.mod
Naaxon.mod *
Nadend.mod *
nap.mod *
Nasoma.mod *
nax.mod
nca.mod *
nmdanet.mod
regn_stim.mod
somacar.mod *
STDPE2Syn2.mod
axoaxonic_cell17S.hoc *
basket_cell17S.hoc *
bistratified_cell13S.hoc *
burst_cell.hoc *
HAM_SR1.ses
mosinit.hoc
olm_cell2.hoc
PureRec_phase.hoc
PureRec_phase_ser.hoc
pyramidal_cell4.hoc
ranstream.hoc *
stim_cell.hoc
Sto_phase.hoc
Sto_phase_ser.hoc
                            
TITLE T-calcium channel



UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
	(molar) = (1/liter)
	(mM) = (millimolar)

	FARADAY = 96520 (coul)
	R = 8.3134 (joule/degC)
	KTOMV = .0853 (mV/degC)
}



NEURON {
	SUFFIX cat
	USEION ca READ cai,cao 
        USEION Ca WRITE iCa VALENCE 2
        : The T-current does not activate calcium-dependent currents.
        : The construction with dummy ion Ca prevents the updating of the 
        : internal calcium concentration. 
        RANGE gcatbar, hinf, minf, taum, tauh, iCa
}

PARAMETER {
	v (mV)
	
      tBase = 23.5  (degC)
	celsius = 22  (degC)
	gcatbar = 0   (mho/cm2)  : initialized conductance
	ki = 0.001    (mM)
	cai = 5.e-5   (mM)       : initial internal Ca++ concentration
	cao = 2       (mM)       : initial external Ca++ concentration
       tfa = 1                  : activation time constant scaling factor
       : tfi = 0.68 
        tfi = 0.68               : inactivation time constant scaling factor
        eca = 140                : Ca++ reversal potential

}


STATE {
	m h 
}

ASSIGNED {
      iCa (mA/cm2)
      gcat (mho/cm2)
	hinf
	tauh
	minf
	taum
}

INITIAL {
	rates(v)
	m = minf
	h = hinf
     gcat = gcatbar*m*m*h*h2(cai)

}

BREAKPOINT {
	SOLVE states METHOD cnexp
	gcat = gcatbar*m*m*h*h2(cai)
	iCa = gcat*ghk(v,cai,cao)

}

DERIVATIVE states {	: exact when v held constant
	rates(v)
	m' = (minf - m)/taum
	h' = (hinf - h)/tauh
}


UNITSOFF
FUNCTION h2(cai(mM)) {
	h2 = ki/(ki+cai)
}

FUNCTION ghk(v(mV), ci(mM), co(mM)) (mV) {
        LOCAL nu,f

        f = KTF(celsius)/2
        nu = v/f
        ghk=-f*(1. - (ci/co)*exp(nu))*efun(nu)
}

FUNCTION KTF(celsius (DegC)) (mV) {
        KTF = ((25./293.15)*(celsius + 273.15))
}


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


FUNCTION alph(v(mV)) {
	TABLE FROM -150 TO 150 WITH 200
	alph = 1.6e-4*exp(-(v+57)/19)
}

FUNCTION beth(v(mV)) {
        TABLE FROM -150 TO 150 WITH 200
	:beth = 1/(exp((-v+15)/10)+1.0)
      beth = 1/(exp((-v+15)/10)+1.0)
}

FUNCTION alpm(v(mV)) {
	TABLE FROM -150 TO 150 WITH 200
	alpm = 0.1967*(-1.0*v+19.88)/(exp((-1.0*v+19.88)/10.0)-1.0)
}

FUNCTION betm(v(mV)) {
	TABLE FROM -150 TO 150 WITH 200
	betm = 0.046*exp(-v/22.73)
}


PROCEDURE rates(v (mV)) { :callable from hoc
        LOCAL a
        a = alpm(v)
        taum = 1/(tfa*(a + betm(v))) : estimation of activation tau
        minf =  a/(a+betm(v))        : estimation of activation steady state
        a = alph(v)
        tauh = 1/(tfi*(a + beth(v))) : estimation of inactivation tau
        hinf = a/(a+beth(v))         : estimation of inactivation steady state
        
}

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