Firing patterns of CA3 hippocampal neurons (Soldado-Magraner et al. 2019)

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Accession:228599
" ... Here we demonstrate that the intrinsic firing patterns of CA3 neurons of the rat hippocampus in vitro undergo rapid long-term plasticity in response to a few minutes of only subthreshold synaptic conditioning. This plasticity on the spike-timing could also be induced by intrasomatic injection of subthreshold depolarizing pulses and was blocked by kinase inhibitors, indicating that discharge dynamics are modulated locally. Cluster analysis of firing patterns before and after conditioning revealed systematic transitions towards adapting and intrinsic burst behaviours, irrespective of the patterns initially exhibited by the cells. We used a conductance-based model to decide appropriate pharmacological blockade, and found that the observed transitions are likely due to recruitment of low-voltage calcium and Kv7 potassium conductances. We conclude that CA3 neurons adapt their conductance profile to the subthreshold activity of their input, so that their intrinsic firing pattern is not a static signature, but rather a reflection of their history of subthreshold activity. In this way, recurrent output from CA3 neurons may collectively shape the temporal dynamics of their embedding circuits."
Reference:
1 . Soldado-Magraner S, Brandalise F, Honnuraiah S, Pfeiffer M, Moulinier M, Gerber U, Douglas R (2019) Conditioning by Subthreshold Synaptic Input Changes the Intrinsic Firing Pattern of CA3 Hippocampal Neurons. J Neurophysiol [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: Hippocampus;
Cell Type(s): Hippocampus CA3 pyramidal GLU cell;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; Python;
Model Concept(s): Activity Patterns; Simplified Models;
Implementer(s): Honnuraiah, Suraj [hs at ini.phys.ethz.ch]; Gutierrez, Adrian [agutie at ini.uzh.ch]; Soldado-Magraner, Saray [ssaray at ini.uzh.ch];
Search NeuronDB for information about:  Hippocampus CA3 pyramidal GLU cell;
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SoldadoMagranerEtAl2019
readme.html
cacumm.mod
cagk.mod
cal2.mod *
can2.mod *
cat.mod *
kaprox.mod
kd.mod
kd_inc_tau.mod
kdrca1.mod
km.mod *
na3n.mod
sample_requirements.txt
screenshot.png
Single_Compartment_Complete_Conductance_List.txt
single_compartment_SoldadoMagranerEtAl.py
                            
TITLE  K-D channel
: M.Migliore jun 2006

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

}

PARAMETER {
	v (mV)
        ek (mV)		: must be explicitely def. in hoc
	celsius		(degC)
	gkdbar=.0 (mho/cm2)
        vhalfn=-33   (mV)
        a0n=0.01      (/ms)
        zetan=3    (1)
        gmn=0.7  (1)
	nmax=2  (1)
	q10=1
	sh = 0
}


NEURON {
	SUFFIX kd_slow
	USEION k READ ek WRITE ik
        RANGE gkd,gkdbar, sh
	GLOBAL ninf,taun
}

STATE {
	n
}

ASSIGNED {
	ik (mA/cm2)
        ninf
        gkd
        taun
}

BREAKPOINT {
	SOLVE states METHOD cnexp
	gkd = gkdbar*n
	ik = gkd*(v-ek)

}

INITIAL {
	rates(v)
	n=ninf
}


FUNCTION alpn(v(mV)) {
  alpn = exp(1.e-3*zetan*(v-vhalfn-sh)*9.648e4/(8.315*(273.16+celsius))) 
}

FUNCTION betn(v(mV)) {
  betn = exp(1.e-3*zetan*gmn*(v-vhalfn-sh)*9.648e4/(8.315*(273.16+celsius))) 
}

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

PROCEDURE rates(v (mV)) { :callable from hoc
        LOCAL a,qt
        qt=q10^((celsius-24)/10)
        a = alpn(v)
        ninf = 1/(1+a)
        taun = (betn(v)*10)/(qt*a0n*(1+a))
	if (taun<nmax) {taun=nmax/qt}
}















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