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
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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 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 kadcr
	USEION k READ ki, ko WRITE ik 		:Changed from READ ek, 23/04/2010,Nassi
        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      (mho/cm2)  :initialized conductance
        vhalfn = -1     (mV)       :activation half-potential (-1), change for pfc, activation at -40
        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) 
	:zetal = 3       (1)        :steady state values
        gmn   = 0.39    (1)        :and time constants
	:gmn   = 0.39    (1)        :and time constants, original
        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)
	ki		(mM)
	ko		(mM)
}


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
	ek=25 * log(ko/ki)		:Changed, added, 23/04/2010, Nassi
	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 = 6 (ms)
	taul = 0.26(ms/mV)*(v+50)               : inactivation variable time constant (0.26)
	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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