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
.libs
ampa.mod *
ampain.mod *
cadyn.mod *
cadynin.mod *
cal.mod *
calc.mod *
calcb.mod *
can.mod *
cancr.mod *
canin.mod *
car.mod *
cat.mod *
catcb.mod *
cpampain.mod *
gabaa.mod *
gabaain.mod *
gabab.mod *
h.mod *
hcb.mod *
hin.mod *
ican.mod *
iccb.mod *
iccr.mod *
icin.mod *
iks.mod *
ikscb.mod *
ikscr.mod *
iksin.mod *
kadist.mod *
kadistcr.mod *
kadistin.mod *
kaprox.mod *
kaproxcb.mod *
kaproxin.mod *
kca.mod *
kcain.mod *
kct.mod *
kctin.mod *
kdr.mod *
kdrcb.mod *
kdrcr.mod *
kdrin.mod *
naf.mod *
nafcb.mod *
nafcr.mod *
nafin.mod *
nafx.mod *
nap.mod *
netstim.mod *
NMDA.mod *
NMDAIN.mod *
sinclamp.mod *
vecstim.mod *
ampa.c
ampa.lo
ampain.c
ampain.lo
cadyn.c
cadyn.lo
cadynin.c
cadynin.lo
cal.c
cal.lo
calc.c
calc.lo
calcb.c
calcb.lo
can.c
can.lo
cancr.c
cancr.lo
canin.c
canin.lo
car.c
car.lo
cat.c
cat.lo
catcb.c
catcb.lo
cpampain.c
cpampain.lo
gabaa.c
gabaa.lo
gabaain.c
gabaain.lo
gabab.c
gabab.lo
h.c
h.lo
hcb.c
hcb.lo
hin.c
hin.lo
ican.c
ican.lo
iccb.c
iccb.lo
iccr.c
iccr.lo
icin.c
icin.lo
iks.c
iks.lo
ikscb.c
ikscb.lo
ikscr.c
ikscr.lo
iksin.c
iksin.lo
kadist.c
kadist.lo
kadistcr.c
kadistcr.lo
kadistin.c
kadistin.lo
kaprox.c
kaprox.lo
kaproxcb.c
kaproxcb.lo
kaproxin.c
kaproxin.lo
kca.c
kca.lo
kcain.c
kcain.lo
kct.c
kct.lo
kctin.c
kctin.lo
kdr.c
kdr.lo
kdrcb.c
kdrcb.lo
kdrcr.c
kdrcr.lo
kdrin.c
kdrin.lo
libnrnmech.la *
mod_func.c
mod_func.lo
naf.c
naf.lo
nafcb.c
nafcb.lo
nafcr.c
nafcr.lo
nafin.c
nafin.lo
nafx.c
nafx.lo
nap.c
nap.lo
netstim.c
netstim.lo
NMDA.c
NMDA.lo
NMDAIN.c
NMDAIN.lo
sinclamp.c
sinclamp.lo
special
vecstim.c
vecstim.lo
                            
TITLE Ca R-type channel with medium threshold for activation
: used in distal dendritic regions, together with calH.mod, to help
: the generation of Ca++ spikes in these regions
: uses channel conductance (not permeability)
: written by Yiota Poirazi on 11/13/00 poirazi@LNC.usc.edu
:
: updated to use CVode by Carl Gold 08/10/03
: Updated by Maria Markaki  03/12/03
: updated on july 13, 2007 by kiki

NEURON {
	SUFFIX car
	USEION ca READ cai, cao WRITE ica
        RANGE gcabar, m, h,ica
	RANGE inf, fac, tau
}

UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
	(molar) = (1/liter)
	(mM) =	(millimolar)
	FARADAY = (faraday) (coulomb)
	R = (k-mole) (joule/degC)
}


ASSIGNED {               : parameters needed to solve DE
	ica (mA/cm2)
:	iCa (mA/cm2)
        inf[2]
	tau[2]		(ms)
        v               (mV)
        celsius 	(degC)
	ecar    	(mV)      
	cai             (mM)      : initial internal Ca++ concentration
	cao             (mM)      : initial external Ca++ concentration
}


PARAMETER {              : parameters that can be entered when function is called in cell-setup
        gcabar = 0      (mho/cm2) : initialized conductance
}  

STATE {	
	m 
	h 
}            : unknown activation and inactivation parameters to be solved in the DEs  


INITIAL {
	rates(v)
        m = 0    : initial activation parameter value
	h = 1    : initial inactivation parameter value
}

BREAKPOINT {
	SOLVE states METHOD cnexp
	ecar = (1e3) * (R*(celsius+273.15))/(2*FARADAY) * log (cao/cai)
	ica = gcabar*m*m*m*h*(v - ecar)
:	iCa = gcabar*m*m*m*h*(v - ecar)

}


DERIVATIVE states {
	rates(v)
	m' = (inf[0]-m)/tau[0]
	h' = (inf[1]-h)/tau[1]
}

PROCEDURE rates(v(mV)) {LOCAL a, b :rest = -70
	FROM i=0 TO 1 {
		tau[i] = vartau(v,i)
		inf[i] = varss(v,i)
	}
}




FUNCTION varss(v(mV), i) {
	if (i==0) {
	   varss = 1 / (1 + exp((v+43.5)/(-3(mV)))) 
	}
	else if (i==1) {  
	     varss = 1/ (1 + exp((v+50)/(1(mV))))    
	}
}

FUNCTION vartau(v(mV), i) (ms){
	if (i==0) {
           vartau = 70(ms)  : activation variable time constant
        }
	else if (i==1) {
           vartau = 20(ms)   : inactivation variable time constant
       }
	
}	















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