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]
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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 GLU cell; Hippocampus CA1 interneuron oriens alveus GABA 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 GLU cell; Hippocampus CA1 interneuron oriens alveus GABA 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 simple NMDA receptors

: Modified from the original AMPA.mod, M.Migliore Mar 2012
: NetCon weight of 1e-3 gives 1nS peak conductance in 0Mg

COMMENT
-----------------------------------------------------------------------------

	Simple model for glutamate AMPA receptors
	=========================================

  - FIRST-ORDER KINETICS, FIT TO WHOLE-CELL RECORDINGS

    Whole-cell recorded postsynaptic currents mediated by AMPA/Kainate
    receptors (Xiang et al., J. Neurophysiol. 71: 2552-2556, 1994) were used
    to estimate the parameters of the present model; the fit was performed
    using a simplex algorithm (see Destexhe et al., J. Computational Neurosci.
    1: 195-230, 1994).

  - SHORT PULSES OF TRANSMITTER (0.3 ms, 0.5 mM)

    The simplified model was obtained from a detailed synaptic model that 
    included the release of transmitter in adjacent terminals, its lateral 
    diffusion and uptake, and its binding on postsynaptic receptors (Destexhe
    and Sejnowski, 1995).  Short pulses of transmitter with first-order
    kinetics were found to be the best fast alternative to represent the more
    detailed models.

  - ANALYTIC EXPRESSION

    The first-order model can be solved analytically, leading to a very fast
    mechanism for simulating synapses, since no differential equation must be
    solved (see references below).



References

   Destexhe, A., Mainen, Z.F. and Sejnowski, T.J.  An efficient method for
   computing synaptic conductances based on a kinetic model of receptor binding
   Neural Computation 6: 10-14, 1994.  

   Destexhe, A., Mainen, Z.F. and Sejnowski, T.J. Synthesis of models for
   excitable membranes, synaptic transmission and neuromodulation using a 
   common kinetic formalism, Journal of Computational Neuroscience 1: 
   195-230, 1994.


-----------------------------------------------------------------------------
ENDCOMMENT



NEURON {
	POINT_PROCESS NMDA
	RANGE R, g, mg, gNMDAmax,i
	NONSPECIFIC_CURRENT i
	GLOBAL Cdur, Alpha, Beta, Erev, Rinf, Rtau
}
UNITS {
	(nA) = (nanoamp)
	(mV) = (millivolt)
	(umho) = (micromho)
	(mM) = (milli/liter)
}

PARAMETER {
      gNMDAmax=1  (umho)
	Cdur	= 1		(ms)	: transmitter duration (rising phase)
	Alpha	= 0.35		(/ms)	: forward (binding) rate
	Beta	= 0.015		(/ms)	: backward (unbinding) rate
:	Beta	= 0.035		(/ms)	: backward (unbinding) rate
:	Alpha	= 0.072		(/ms)	: forward (binding) rate
:	Beta	= 0.0066		(/ms)	: backward (unbinding) rate
	Erev	= 0	(mV)		: reversal potential
	mg	= 1    (mM)		: external magnesium concentration
}


ASSIGNED {
	v		(mV)		: postsynaptic voltage
	i 		(nA)		: current = g*(v - Erev)
	g 		(umho)		: conductance
	Rinf				: steady state channels open
	Rtau		(ms)		: time constant of channel binding
	synon
}

STATE {Ron Roff}

INITIAL {
	Rinf = Alpha / (Alpha + Beta)
	Rtau = 1 / (Alpha + Beta)
	synon = 0
}

BREAKPOINT {
	SOLVE release METHOD cnexp
	g = 3.4*mgblock(v)*(Ron + Roff)*1(umho)
	i = g*(v - Erev)		
}

DERIVATIVE release {
	Ron' = (synon*Rinf - Ron)/Rtau
	Roff' = -Beta*Roff
}

: following supports both saturation from single input and
: summation from multiple inputs
: if spike occurs during CDur then new off time is t + CDur
: ie. transmitter concatenates but does not summate
: Note: automatic initialization of all reference args to 0 except first


FUNCTION mgblock(v(mV)) {
	TABLE 
	DEPEND mg
	FROM -140 TO 80 WITH 1000

	: from Jahr & Stevens

	mgblock = 1 / (1 + exp(0.062 (/mV) * -v) * (mg / 3.57 (mM)))
}


NET_RECEIVE(weight, on, nspike, r0, t0 (ms)) {
	: flag is an implicit argument of NET_RECEIVE and  normally 0
        if (flag == 0) { : a spike, so turn on if not already in a Cdur pulse
		nspike = nspike + 1
		if (!on) {
			r0 = r0*exp(-Beta*(t - t0))
			t0 = t
			on = 1
			synon = synon + weight
			state_discontinuity(Ron, Ron + r0)
			state_discontinuity(Roff, Roff - r0)
		}
		: come again in Cdur with flag = current value of nspike
		net_send(Cdur, nspike)
        }
	if (flag == nspike) { : if this associated with last spike then turn off
		r0 = weight*Rinf + (r0 - weight*Rinf)*exp(-(t - t0)/Rtau)
		t0 = t
		synon = synon - weight
		state_discontinuity(Ron, Ron - r0)
		state_discontinuity(Roff, Roff + r0)
		on = 0
	}
}