Linear vs non-linear integration in CA1 oblique dendrites (Gómez González et al. 2011)

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Accession:144450
The hippocampus in well known for its role in learning and memory processes. The CA1 region is the output of the hippocampal formation and pyramidal neurons in this region are the elementary units responsible for the processing and transfer of information to the cortex. Using this detailed single neuron model, it is investigated the conditions under which individual CA1 pyramidal neurons process incoming information in a complex (non-linear) as opposed to a passive (linear) manner. This detailed compartmental model of a CA1 pyramidal neuron is based on one described previously (Poirazi, 2003). The model was adapted to five different reconstructed morphologies for this study, and slightly modified to fit the experimental data of (Losonczy, 2006), and to incorporate evidence in pyramidal neurons for the non-saturation of NMDA receptor-mediated conductances by single glutamate pulses. We first replicate the main findings of (Losonczy, 2006), including the very brief window for nonlinear integration using single-pulse stimuli. We then show that double-pulse stimuli increase a CA1 pyramidal neuron’s tolerance for input asynchrony by at last an order of magnitude. Therefore, it is shown using this model, that the time window for nonlinear integration is extended by more than an order of magnitude when inputs are short bursts as opposed to single spikes.
Reference:
1 . Gómez González JF, Mel BW, Poirazi P (2011) Distinguishing Linear vs. Non-Linear Integration in CA1 Radial Oblique Dendrites: It's about Time. Front Comput Neurosci 5:44 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s): Hippocampus CA1 pyramidal GLU cell;
Channel(s): I Na,p; I CAN; I Sodium; I Calcium; I Potassium; I_AHP;
Gap Junctions:
Receptor(s): NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Active Dendrites; Detailed Neuronal Models; Synaptic Integration;
Implementer(s):
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; NMDA; I Na,p; I CAN; I Sodium; I Calcium; I Potassium; I_AHP;
// This function calculates the center of the soma averaged all their segments
// written by Jose Gomez, March 2006, jfcgomez@ull.es
//Input:	soma[i] sections for all i
//Output	center_soma section the coordenades of the center are in x3d(1), y3d(1) and z3d(1)


xcg=0
ycg=0
zcg=0
ncp=0
forsec "soma" {
		//print secname(),area(0.5)
		
		n=n3d()
		nseg=n
		ncp+=n		
		for i=0,n-1 {
			//print i,"\t",diam3d(i),"\t",x3d(i),"\t",y3d(i),"\t",z3d(i)
			xcg+=x3d(i)
			ycg+=y3d(i)
			zcg+=z3d(i)
		}

}

xcg/=ncp
ycg/=ncp
zcg/=ncp


//------------------------------------------------------------------
//print "ncp ", ncp,"Xcg= ", xcg,"\tYcg= ", ycg,"\tZcg= ", zcg

/*
create center_soma
center_soma{pt3dclear()
	pt3dadd(xcg-1, ycg, zcg, 1)		//-1 and + 1 are added to can see center_soma at display (shape)
	pt3dadd(xcg, ycg, zcg, 1)
	pt3dadd(xcg+1, ycg, zcg, 1)
}
access center_soma

print "Center of soma: ","Xcg= ", x3d(1) ,"\tYcg= ", y3d(1),"\tZcg= ", z3d(1)


//center_list is to be able to display the center_soma
objref center_list
center_list=new SectionList()
center_soma center_list.append()
*/

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