Fast Spiking Basket cells (Tzilivaki et al 2019)

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Accession:237595
"Interneurons are critical for the proper functioning of neural circuits. While often morphologically complex, dendritic integration and its role in neuronal output have been ignored for decades, treating interneurons as linear point neurons. Exciting new findings suggest that interneuron dendrites support complex, nonlinear computations: sublinear integration of EPSPs in the cerebellum, coupled to supralinear calcium accumulations and supralinear voltage integration in the hippocampus. These findings challenge the point neuron dogma and call for a new theory of interneuron arithmetic. Using detailed, biophysically constrained models, we predict that dendrites of FS basket cells in both hippocampus and mPFC come in two flavors: supralinear, supporting local sodium spikes within large-volume branches and sublinear, in small-volume branches. Synaptic activation of varying sets of these dendrites leads to somatic firing variability that cannot be explained by the point neuron reduction. Instead, a 2-stage Artificial Neural Network (ANN), with both sub- and supralinear hidden nodes, captures most of the variance. We propose that FS basket cells have substantially expanded computational capabilities sub-served by their non-linear dendrites and act as a 2-layer ANN."
Reference:
1 . Tzilivaki A, Kastellakis G, Poirazi P (2019) Challenging the point neuron dogma: FS basket cells as 2-stage nonlinear integrators Nature Communications 10(1):3664 [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; Prefrontal cortex (PFC);
Cell Type(s): Hippocampus CA3 interneuron basket GABA cell; Neocortex layer 5 interneuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; MATLAB; Python;
Model Concept(s): Active Dendrites; Detailed Neuronal Models;
Implementer(s): Tzilivaki, Alexandra [alexandra.tzilivaki at charite.de]; Kastellakis, George [gkastel at gmail.com];
Search NeuronDB for information about:  Hippocampus CA3 interneuron basket GABA cell;
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TzilivakiEtal_FSBCs_model
Multicompartmental_Biophysical_models
mechanism
x86_64
ampa.mod *
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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 proximal region kinetics
: used only in soma and sections located < 100 microns from the soma


NEURON {
	SUFFIX kapin
	USEION k READ ek WRITE ik
        RANGE gkabar, 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      (mho/cm2) :initialized conductance
        vhalfn = 11     (mV)      :activation half-potential
        vhalfl = -56    (mV) 	  :inactivation half-potential
	:vhalfl = -56    (mV) 	  :inactivation half-potential
        a0n = 0.05      (/ms)     :parameters used
        zetan = -1.5    (1)       :in calculation of (-1.5)
        zetal = 3       (1)       :steady state values(3)
        gmn = 0.55      (1)       :and time constants(0.55) change to get an effect on spike repolarization
        gml = 1         (1)
	:gml = 1         (1)
	lmin = 2        (ms)
	nmin = 0.1      (ms)
	pw = -1         (1)
	tq = -40	(mV)
	qq = 5		(mV)
	q10 = 5                   :temperature sensitivity
}



 
ASSIGNED {       :parameters needed to solve DE
	v               (mV)
        ek              (mV)      :K reversal potential  (reset in cell-setup.hoc)
	celsius         (degC)
	ik              (mA/cm2)
        ninf
        linf      
        taul            (ms)
        taun            (ms)
}


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

LOCAL qt

INITIAL {		:initialize the following parameter using rates()
        qt = q10^((celsius-24)/10(degC))         : temprature 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 {
	rates(v)
        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 = 12 (ms)
	: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
}


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