Fast Spiking Basket cells (Tzilivaki et al 2019)

 Download zip file   Auto-launch 
Help downloading and running models
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;
/
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 high threshold calcium current (L-current)

COMMENT
        *********************************************
        reference:      McCormick & Huguenard (1992) 
                        J.Neurophysiology 68(4), 1384-1400
        found in:       hippocampal pyramidal cells
        *********************************************
        Assembled for MyFirstNEURON by Arthur Houweling
ENDCOMMENT

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

NEURON {
        SUFFIX calc
	:SUFFIX cal
        USEION ca READ cai,cao WRITE ica
        RANGE gcabar, m_inf, tau_m, ica
}

UNITS {
        (mA)    = (milliamp)
        (mV)    = (millivolt)
        (mM)    = (milli/liter)
        FARADAY = 96480 (coul)
        R       = 8.314 (volt-coul/degK)
}

PARAMETER {
        v                       (mV)
        celsius = 23            (degC)
        dt                      (ms)
        cai = 50e-6             (mM)
        cao = 2                 (mM)
        gcabar= 0.000276        (cm/s)          
}

STATE {
        m
}

ASSIGNED {
        ica             (mA/cm2)
        tau_m           (ms)
        m_inf 
        tadj
}

BREAKPOINT { 
        SOLVE states :METHOD euler
        ica = gcabar * m*m * ghk(v,cai,cao,2)
}

:DERIVATIVE states {
:       rates(v)
:
:       m'= (m_inf-m) / tau_m 
:}
  
PROCEDURE states() {
        rates(v)
        m= m + (1-exp(-dt/tau_m))*(m_inf-m)
}

UNITSOFF
INITIAL {
        tadj = 3^((celsius-23.5)/10)
        rates(v)
        m = m_inf
:        ica = gcabar * m*m * ghk(v,cai,cao,2)
}

FUNCTION ghk( v(mV), ci(mM), co(mM), z)  (millicoul/cm3) {
        LOCAL e, w
        w = v * (.001) * z*FARADAY / (R*(celsius+273.16))
        if (fabs(w)>1e-4) 
          { e = w / (exp(w)-1) }
        else
        : denominator is small -> Taylor series
          { e = 1-w/2 }
        ghk = - (.001) * z*FARADAY * (co-ci*exp(w)) * e
}
UNITSOFF

PROCEDURE rates(v(mV)) { LOCAL a,b
        a = 1.6 / (1+ exp(-0.072*(v-15)))
        b = 0.02 * vtrap( -(v-1.31), 5.36/2)


         tau_m = 1/(a+b) / (tadj*1.43)

        m_inf = 1/(1+exp((v+9)/-6))
}

FUNCTION vtrap(x,c) { 
        : Traps for 0 in denominator of rate equations
        if (fabs(x/c) < 1e-6) {
          vtrap = c + x/2 }
        else {
          vtrap = x / (1-exp(-x/c)) }
}
UNITSON




Loading data, please wait...