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]
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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
                            
: km.mod
: Potassium channel, Hodgkin-Huxley style kinetics
: Based on I-M (muscarinic K channel)
: Slow, noninactivating
: Author: Zach Mainen, Salk Institute, 1995, zach@salk.edu
:
: modified to use CVode --Carl Gold 08/12/03



NEURON {
	SUFFIX km
	USEION k READ ek WRITE ik
	RANGE n, gbar,ik
	GLOBAL Ra, Rb, ninf, ntau
	GLOBAL q10, temp, tadj, vmin, vmax
}

UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
:	(pS) = (picosiemens)
:	(um) = (micron)
} 

PARAMETER {
	gbar = 0.03     (mho/cm2)
:	gbar = 10   	(pS/um2)	: 0.03 mho/cm2
	tha  = -30	(mV)		: v 1/2 for inf
	qa   = 9	(mV)		: inf slope		
	Ra   = 0.001	(/ms)		: max act rate  (slow)
	Rb   = 0.001	(/ms)		: max deact rate  (slow)
	temp = 23	(degC)		: original temp 	
	q10  = 2.3			: temperature sensitivity
	vmin = -120	(mV)
	vmax = 100	(mV)
} 


ASSIGNED {
	celsius		(degC)
	v 		(mV)
	ik 		(mA/cm2)
	ek		(mV)
	ninf
	ntau 		(ms)
	tadj
}
 

STATE { 
	n 
}

INITIAL {
        tadj = q10^((celsius - temp)/10(degC))  :temperature adjustment
	rates(v)
	n = ninf
}

BREAKPOINT {
        SOLVE states METHOD cnexp
: 	ik = tadj* gbar*n * (v - ek)
	ik = (1e-4) *tadj* gbar*n * (v - ek)
}


DERIVATIVE states {
	rates(v)
	n' = (ninf-n)/ntau
}


PROCEDURE rates(v(mV)) {  :Computes rate and other constants at current v.
                      :Call once from HOC to initialize inf at resting v.
	LOCAL a,b
        a = Ra * (v - tha)*1(/mV) / (1 - exp(-(v - tha)/qa))
        b = -Rb * (v - tha)*1(/mV) / (1 - exp((v - tha)/qa))
        ntau = 1/(a+b)/tadj
	ninf = a*ntau
}