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 *
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 *
                            
TITLE simple NMDA receptors

COMMENT
-----------------------------------------------------------------------------

Essentially the same as /examples/nrniv/netcon/ampa.mod in the NEURON
distribution - i.e. Alain Destexhe's simple AMPA model - but with
different binding and unbinding rates and with a magnesium block.
Modified by Andrew Davison, The Babraham Institute, May 2000


	Simple model for glutamate AMPA receptors
	=========================================

  - FIRST-ORDER KINETICS, FIT TO WHOLE-CELL RECORDINGS

    Whole-cell recorded postsynaptic currents mediated by AMPA/Kainate
    receptors (Xiang et al., J. Neurophysiol. 71: 2552-2556, 1994) were used
    to estimate the parameters of the present model; the fit was performed
    using a simplex algorithm (see Destexhe et al., J. Computational Neurosci.
    1: 195-230, 1994).

  - SHORT PULSES OF TRANSMITTER (0.3 ms, 0.5 mM)

    The simplified model was obtained from a detailed synaptic model that 
    included the release of transmitter in adjacent terminals, its lateral 
    diffusion and uptake, and its binding on postsynaptic receptors (Destexhe
    and Sejnowski, 1995).  Short pulses of transmitter with first-order
    kinetics were found to be the best fast alternative to represent the more
    detailed models.

  - ANALYTIC EXPRESSION

    The first-order model can be solved analytically, leading to a very fast
    mechanism for simulating synapses, since no differential equation must be
    solved (see references below).



References

   Destexhe, A., Mainen, Z.F. and Sejnowski, T.J.  An efficient method for
   computing synaptic conductances based on a kinetic model of receptor binding
   Neural Computation 6: 10-14, 1994.  

   Destexhe, A., Mainen, Z.F. and Sejnowski, T.J. Synthesis of models for
   excitable membranes, synaptic transmission and neuromodulation using a 
   common kinetic formalism, Journal of Computational Neuroscience 1: 
   195-230, 1994.


-----------------------------------------------------------------------------
ENDCOMMENT



NEURON {
	POINT_PROCESS NMDAIN
	RANGE g, Alpha, Beta, e, gmax, ica
	USEION ca WRITE ica
	NONSPECIFIC_CURRENT  iNMDA            
	GLOBAL Cdur, mg, Cmax
}
UNITS {
	(nA) = (nanoamp)
	(mV) = (millivolt)
	(umho) = (micromho)
	(mM) = (milli/liter)
}

PARAMETER {
	Cmax	= 1	 (mM)           : max transmitter concentration
	Cdur	= 1	 (ms)		: transmitter duration (rising phase) 
	Alpha	= 4	 (/ms /mM)	: forward (binding) rate (4)
	Beta	= 0.02	(/ms)		: backward rate
	e	= 0	 (mV)		: reversal potential
        mg      = 1      (mM)           : external magnesium concentration

}


ASSIGNED {
	v		(mV)		: postsynaptic voltage
	iNMDA 		(nA)		: current = g*(v - e)
	g 		(umho)		: conductance
	Rinf				: steady state channels open
	Rtau		(ms)		: time constant of channel binding
	synon
        B 
	gmax                              : magnesium block
	ica
}

STATE {Ron Roff}

INITIAL {
	Rinf = Cmax*Alpha / (Cmax*Alpha + Beta)
	Rtau = 1 / (Cmax*Alpha + Beta)
	synon = 0
}

BREAKPOINT {
	SOLVE release METHOD cnexp
        B = mgblock(v)
	g = (Ron + Roff)*1(umho) * B
	iNMDA = g*(v - e)
        ica = 7*iNMDA/10   :(5-10 times more permeable to Ca++ than Na+ or K+, Ascher and Nowak, 1988)
:       ica = 0
        iNMDA = 3*iNMDA/10

}

DERIVATIVE release {
	Ron' = (synon*Rinf - Ron)/Rtau
	Roff' = -Beta*Roff
}

FUNCTION mgblock(v(mV)) {
        TABLE 
        DEPEND mg
        FROM -140 TO 80 WITH 1000

        : from Jahr & Stevens

      
	 mgblock = 1 / (1 + exp(0.072 (/mV) * -v) * (mg / 3.57 (mM)))  	
}

: following supports both saturation from single input and
: summation from multiple inputs
: if spike occurs during CDur then new off time is t + CDur
: ie. transmitter concatenates but does not summate
: Note: automatic initialization of all reference args to 0 except first

			
NET_RECEIVE(weight, on, nspike, r0, t0 (ms)) {
	: flag is an implicit argument of NET_RECEIVE and  normally 0
        if (flag == 0) { : a spike, so turn on if not already in a Cdur pulse
		nspike = nspike + 1
		if (!on) {
			r0 = r0*exp(-Beta*(t - t0))
			t0 = t
			on = 1
			synon = synon + weight
			state_discontinuity(Ron, Ron + r0)
			state_discontinuity(Roff, Roff - r0)
		}
:		 come again in Cdur with flag = current value of nspike
		net_send(Cdur, nspike)
       }
	if (flag == nspike) { : if this associated with last spike then turn off
		r0 = weight*Rinf + (r0 - weight*Rinf)*exp(-(t - t0)/Rtau)
		t0 = t
		synon = synon - weight
		state_discontinuity(Ron, Ron - r0)
		state_discontinuity(Roff, Roff + r0)
		on = 0
	}
gmax = weight
}



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