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;
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CA1_Gomez_2011
mechanism
x86_64
ampa.mod *
cad.mod
cal.mod
calH.mod
can.mod *
car.mod
cat.mod
d3.mod *
gabaa.mod *
gabab.mod
h.mod
hha_old.mod
hha2.mod
ican.mod
ipulse1.mod *
ipulse2.mod *
kadist.mod
kaprox.mod
kca.mod
kct.mod
KdBG.mod
km.mod
nap.mod *
netstim.mod *
netstimmm.mod *
nmda.mod *
NMDAb.mod
somacar.mod
                            
TITLE Kct current

COMMENT Equations from 
		  Shao L.R., Halvorsrud R., Borg-Graham L., Storm J.F. The role of BK-type Ca2_-dependent K+ channels in spike broadening during repetitive firing in rat hippocampal pyramidal cells J.Physiology (1999),521:135-146 
		  
		  The Krasnow Institute
		  George Mason University

Copyright	  Maciej Lazarewicz, 2001
		  (mlazarew@gmu.edu)
		  All rights reserved.
ENDCOMMENT

NEURON {
	SUFFIX  mykca
	USEION k READ ek WRITE ik
	USEION ca READ cai   
	RANGE  gkbar,ik
}

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

PARAMETER {
        gkbar	= 1.0e-3 (S/cm2)
}

ASSIGNED {
        v       (mV)
        cai	(mM)     :26/10/04   htan se sxolio
	celsius		 (degC)
	ek		(mV)
	ik	(mA/cm2)
	k1	(/ms)
	k2	(/ms)
	k3	(/ms)
	k4	(/ms)
	q10	(1)
}

STATE { cst ost ist }

BREAKPOINT { 
	SOLVE kin METHOD sparse
	ik = gkbar * ost *( v - ek ) 
}

INITIAL {
	SOLVE kin STEADYSTATE sparse
}

KINETIC kin {
:	rates(v, cani)
	rates(v, cai)
	~cst<->ost  (k3,k4)
	~ost<->ist  (k1,0.0)
	~ist<->cst  (k2,0.0)
	CONSERVE cst+ost+ist=1
}

:PROCEDURE rates( v(mV), cani(mM)) {
PROCEDURE rates( v(mV), cai(mM)) {
:	 k1=alp( 0.1, v,  -10.0,   1.0 ) : original
	 k1=alp( 0.1, v,  -10.0,   1.0 )
	 k2=alp( 0.1, v, -120.0, -10.0 ) :original
:	 k3=alpha( 0.001, 1.0, v, -20.0, 7.0 ) *1.0e8* ( cai*1.0(/mM) )^3  :original
	 k3=alpha( 0.001, 1.0, v, -20.0, 7.0 ) *1.0e8* (cai*1.0(/mM) )^3
	 k4=alp( 0.01, v, -44.0,  -5.0 ) :original
:	 k4=alp( 0.2, v, -44.0,  -5.0 )
}

FUNCTION alpha( tmin(ms), tmax(ms), v(mV), vhalf(mV), k(mV) )(/ms){
        alpha = 1.0 / ( tmin + 1.0 / ( 1.0 / (tmax-tmin) + exp((v-vhalf)/k)*1.0(/ms) ) )
}

FUNCTION alp( tmin(ms), v(mV), vhalf(mV), k(mV) )(/ms){
        alp = 1.0 / ( tmin + exp( -(v-vhalf) / k )*1.0(ms) )
}










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