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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 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
                            
// Dummy cell containing a NetStim object
// BPG 5-12-08

begintemplate StimCell
public is_art
public init
public connect2target
public soma, stim

objref stim

proc init() {
	biophys()
}

create soma

proc biophys() {
//	soma stim = new NetStim()
	soma stim = new RegnStim()
    	stim.number = 10000
    	stim.start = 50
    	stim.interval = 25
    	stim.noise = 0
}

obfunc connect2target() { localobj nc //$o1 target point process, optional $o2 returned NetCon
  	soma nc = new NetCon(stim, $o1)
  	if (numarg() == 2) { $o2 = nc } // for backward compatibility
  	return nc
}

func is_art() { return 0 }

endtemplate StimCell

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