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]
Citations  Citation Browser
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 K-A channel from Klee Ficker and Heinemann
: modified by Brannon and Yiota Poirazi (poirazi@LNC.usc.edu) 
: to account for Hoffman et al 1997 distal region kinetics
: used only in locations > 100 microns from the soma
:
: modified to work with CVode by Carl Gold, 8/10/03
:  Updated by Maria Markaki  12/02/03

NEURON {
	SUFFIX kad
	USEION k READ ek WRITE ik
        RANGE gkabar,gka,ik
        GLOBAL ninf,linf,taul,taun,lmin
}


UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
}


PARAMETER {    :parameters that can be entered when function is called in cell-setup   
:	gkabar = 0.008  (mho/cm2)  :suggested conductance value
	gkabar = 0      (mho/cm2)  :initialized conductance
        vhalfn = -1     (mV)       :activation half-potential
        vhalfl = -56    (mV)       :inactivation half-potential
       a0n = 0.1       (/ms)      :parameters used
       : a0l = 0.05       (/ms)      :parameters used
        zetan = -1.8    (1)        :in calculation of
        zetal = 3       (1)        :steady state values
        gmn   = 0.39    (1)        :and time constants
        gml   = 1       (1)
	lmin  = 2       (ms)
	nmin  = 0.1     (ms)
:	nmin  = 0.2     (ms)	:suggested
	pw    = -1      (1)
	tq    = -40     (mV)
	qq    = 5       (mV)
	q10   = 5                :temperature sensitivity
}


ASSIGNED {    :parameters needed to solve DE
	v               (mV)
        ek              (mV)
	celsius  	(degC)
	ik              (mA/cm2)
        ninf
        linf      
        taul            (ms)
        taun            (ms)
        gka             (mho/cm2)
}


STATE {       :the unknown parameters to be solved in the DEs 
	n l
}

: Solve qt once in initial block
LOCAL qt

INITIAL {    :initialize the following parameter using rates()
        qt = q10^((celsius-24)/10(degC))       : temperature adjustment factor
	rates(v)
	n=ninf
	l=linf
}

BREAKPOINT {
	SOLVE states METHOD cnexp
:	ik = gkabar*n*l*(v+70)
	ik = gkabar*n*l*(v-ek)
}


DERIVATIVE states {     : exact when v held constant; integrates over dt step
        rates(v)          : do this here
        n' = (ninf - n)/taun
        l' = (linf - l)/taul
}



PROCEDURE rates(v (mV)) {		 :callable from hoc
	LOCAL a

        a = alpn(v)
        ninf = 1/(1 + a)		 : activation variable steady state value
        taun = betn(v)/(qt*a0n*(1+a))	 : activation variable time constant
	if (taun<nmin) {taun=nmin}	 : time constant not allowed to be less than nmin

        a = alpl(v)
        linf = 1/(1+ a)                  : inactivation variable steady state value
	taul = 0.26(ms/mV)*(v+50)               : inactivation variable time constant
	if (taul<lmin) {taul=lmin}       : time constant not allowed to be less than lmin
}


FUNCTION alpn(v(mV)) { LOCAL zeta
  zeta = zetan+pw/(1+exp((v-tq)/qq))
UNITSOFF
  alpn = exp(1.e-3*zeta*(v-vhalfn)*9.648e4/(8.315*(273.16+celsius))) 
UNITSON
}

FUNCTION betn(v(mV)) { LOCAL zeta
  zeta = zetan+pw/(1+exp((v-tq)/qq))
UNITSOFF
  betn = exp(1.e-3*zeta*gmn*(v-vhalfn)*9.648e4/(8.315*(273.16+celsius))) 
UNITSON
}

FUNCTION alpl(v(mV)) {
UNITSOFF
  alpl = exp(1.e-3*zetal*(v-vhalfl)*9.648e4/(8.315*(273.16+celsius))) 
UNITSON
}

FUNCTION betl(v(mV)) {
UNITSOFF
  betl = exp(1.e-3*zetal*gml*(v-vhalfl)*9.648e4/(8.315*(273.16+celsius))) 
UNITSON
}