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
                            
: Persistent Na+ channel

NEURON {
	SUFFIX Nap
	USEION na READ ena WRITE ina
	RANGE gnapbar, ina, gna
	RANGE DA_alphamshift,DA_betamshift
	RANGE DA_alphahfactor, DA_betahfactor
}

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

INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}

PARAMETER {
	v (mV)
	dt (ms)
	gnapbar= 0.0022 (mho/cm2) <0,1e9>
	ena = 55 (mV)
	DA_alphamshift=0 : 2 for 100% DA, 0 otherwise
	DA_betamshift=0  : 5 for 100% DA,0 otherwise
	DA_alphahfactor=0: -.8e-5 for DA, 0 otherwise
	DA_betahfactor=0 : 0.014286-0.02 for DA, 0 otherwise
}

STATE {
	m h
}

ASSIGNED {
	ina (mA/cm2)
	minf hinf 
	mtau (ms)
	htau (ms)
	gna (mho/cm2)
	
}

INITIAL {
	rate(v)
	m = minf
	h = hinf
}

BREAKPOINT {
	SOLVE states METHOD cnexp
	gna = gnapbar*m*h
	ina = gna*(v-55)
	
}

DERIVATIVE states {
	rate(v)
	m' = (minf-m)/mtau
	h' = (hinf-h)/htau
}

UNITSOFF

FUNCTION malf( v){ LOCAL va 
	va=v+12+DA_alphamshift
	if (fabs(va)<1e-04){
	 va = va + 0.00001 }
	malf = (-0.2816*va)/(-1+exp(-va/9.3))
	
}


FUNCTION mbet(v(mV))(/ms) { LOCAL vb 
	vb=v-15+DA_betamshift
	if (fabs(vb)<1e-04){
	    vb = vb + 0.00001 }

	mbet = (0.2464*vb)/(-1+exp(vb/6))

}	


FUNCTION half(v(mV))(/ms) { LOCAL vc 
	vc=v+42.8477
	if (fabs(vc)<1e-04){
	   vc=vc+0.00001 }
        half= (2.8e-5+DA_alphahfactor)*(exp(-vc/4.0248))

}


FUNCTION hbet(v(mV))(/ms) { LOCAL vd
	vd=v-413.9284
	if (fabs(vd)<1e-04){
	vd=vd+0.00001 }
        hbet= (0.02+DA_betahfactor)/(1+exp(-vd/148.2589))
 
}




PROCEDURE rate(v (mV)) {LOCAL msum, hsum, ma, mb, ha, hb
	ma=malf(v) mb=mbet(v) ha=half(v) hb=hbet(v)
	
	msum = ma+mb
	minf = ma/msum
	mtau = 1/msum
	
	
	hsum = ha+hb
	hinf = ha/hsum
	htau = 1/hsum
}

	
UNITSON





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