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
                            
// Data from Saraga et al. (2003) paper
// changed morphology and some channel densities (BPG 12-1-09)
//   OLM_Cell

begintemplate OLMCell
public is_art
public init, topol, basic_shape, subsets, geom, biophys, geom_nseg
public pre_list, connect2target

public soma, dend1, dend2, axon
public all

objref pre_list

proc init() {
  	topol()
  	subsets()
  	geom()
  	biophys()
  	geom_nseg()
  	pre_list = new List()
  	synapses()
  
}

create soma, dend1, dend2, axon

proc topol() { local i
  	connect dend1(0), soma(1)
  	connect dend2(0), soma(0)
  	connect axon(0), soma(1)
  	basic_shape()
}

proc basic_shape() {
  	soma {pt3dclear() pt3dadd(0, 0, 0, 1) pt3dadd(15, 0, 0, 1)}
  	dend1 {pt3dclear() pt3dadd(15, 0, 0, 1) pt3dadd(90, 0, 0, 1)}
  	dend2 {pt3dclear() pt3dadd(0, 0, 0, 1) pt3dadd(-74, 0, 0, 1)}
  	axon {pt3dclear() pt3dadd(15, 0, 0, 1) pt3dadd(15, 120, 0, 1)}
}

objref all
proc subsets() { local i
  	objref all
  	all = new SectionList()
    	soma all.append()
    	dend1 all.append()
    	dend2 all.append()
    	axon all.append()

}

proc geom() {
  	forsec all {  }
  	soma {  L = 20  diam = 10  }
  	dend1 {  L = 250  diam = 3  }
  	dend2 {  L = 250  diam = 3  }
  	axon {  L = 150  diam = 1.5  }
}

external lambda_f
proc geom_nseg() {
  forsec all { nseg = int((L/(0.1*lambda_f(100))+.9)/2)*2 + 1  }
}

proc biophys() {

	Rm = 20000
	//Rm = 1/5e-05		// original
	
  	forsec all {
  		Ra = 150
    		cm = 1.3
  	}
  	soma {
		insert IA
		gkAbar_IA = 0.0165
		insert Ih
            gkhbar_Ih = 0.00035
//		gkhbar_Ih = 0.0005
//		gkhbar_Ih = 0.001385
		insert Ksoma
		gksoma_Ksoma = 0.0319
		insert Nasoma
		gnasoma_Nasoma = 0.0107
		gl_Nasoma = 1/Rm
		el_Nasoma = -70  	
	}
  
  	dend1 {
		insert IA
    		gkAbar_IA = 0.004
     		//gkAbar_IA = 0.013
		//insert Ih
		//gkhbar_Ih = 0.001385
		insert Kdend
		gkdend_Kdend = 2*0.023
		insert Nadend
		gnadend_Nadend = 2*0.0117
		gl_Nadend = 1/Rm
		el_Nadend = -70
  	}
  
  	dend2 {
		insert IA
    		gkAbar_IA = 0.004
    		//gkAbar_IA = 0.013
		//insert Ih
		//gkhbar_Ih = 0.001385
		insert Kdend
		gkdend_Kdend = 2*0.023
		insert Nadend
		gnadend_Nadend = 2*0.0117
		gl_Nadend = 1/Rm
		el_Nadend = -70
  	}
  
  	axon {
		insert Kaxon
		gkaxon_Kaxon = 0.05104
		insert Naaxon
		gnaaxon_Naaxon = 0.01712
		gl_Naaxon = 1/Rm
		el_Naaxon = -70

  	}
}

obfunc connect2target() { localobj nc //$o1 target point process, optional $o2 returned NetCon
  	soma nc = new NetCon(&v(1), $o1)
  	nc.threshold = -10
  	if (numarg() == 2) { $o2 = nc } // for backward compatibility
  	return nc
}

objref syn_
proc synapses() {
  	/* E0 */   dend2 syn_ = new Exp2Syn(0.5)  pre_list.append(syn_)		// AMPA		PC
    	syn_.tau1 = 0.5
    	syn_.tau2 = 3
  	/* E1 */   dend1 syn_ = new Exp2Syn(0.5)  pre_list.append(syn_)		// AMPA		PC
    	syn_.tau1 = 0.5
    	syn_.tau2 = 3
  	/* I2 */   soma syn_ = new Exp2Syn(0.5)  pre_list.append(syn_)		// GABA-A	Septum
    	syn_.tau1 = 1
    	syn_.tau2 = 8
    	syn_.e = -75
  	/* I3 */   soma syn_ = new Exp2Syn(0.5)  pre_list.append(syn_)		// GABA-B	Septum
    	syn_.tau1 = 35
    	syn_.tau2 = 100
    	syn_.e = -75
}

func is_art() { return 0 }

endtemplate OLMCell


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