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]
Citations  Citation Browser
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 Kct current

COMMENT Equations from 
		  Shao L.R., Halvorsrud R., Borg-Graham L., Storm J.F. The role of BK-type Ca2_-dependent K+ channels in spike broadening during repetitive firing in rat hippocampal pyramidal cells J.Physiology (1999),521:135-146 
		  
		  The Krasnow Institute
		  George Mason University

Copyright	  Maciej Lazarewicz, 2001
		  (mlazarew@gmu.edu)
		  All rights reserved.
ENDCOMMENT

NEURON {
	:SUFFIX  mykca
	SUFFIX kctin
	USEION k READ ek WRITE ik
	USEION ca READ cai   
	RANGE  gkcbar,ik
}

UNITS {
	(molar) = (1/liter)
	(mM)	= (millimolar)
	(S)  	= (siemens)
	(mA) 	= (milliamp)
	(mV) 	= (millivolt)
}

PARAMETER {
        gkcbar	= 0	 (S/cm2)
	:gkbar	= 1.0e-3 (S/cm2)
}

ASSIGNED {
        v       (mV)
        cai	(mM)     :26/10/04   htan se sxolio
	celsius		 (degC)
	ek		(mV)
	ik	(mA/cm2)
	k1	(/ms)
	k2	(/ms)
	k3	(/ms)
	k4	(/ms)
	q10	(1)
}

STATE { cst ost ist }

BREAKPOINT { 
	SOLVE kin METHOD sparse
	ik = gkcbar * ost *( v - ek ) 
}

INITIAL {
	SOLVE kin STEADYSTATE sparse
}

KINETIC kin {
:	rates(v, cani)
	rates(v, cai)
	~cst<->ost  (k3,k4)
	~ost<->ist  (k1,0.0)
	~ist<->cst  (k2,0.0)
	CONSERVE cst+ost+ist=1
}

:change feb8th for pfc
:PROCEDURE rates( v(mV), cani(mM)) {
PROCEDURE rates( v(mV), cai(mM)) {
:	 k1=alp( 0.1, v,  -10.0,   1.0 ) : original
	 k1=alp( 0.01, v,  -10.0,   1.0 ) :increases the current
	 k2=alp( 0.1, v, -120.0, -10.0 ) :original (0.1, -120, -10)
:	 k3=alpha( 0.001, 1.0, v, -20.0, 7.0 ) *1.0e8* ( cai*1.0(/mM) )^3  :original
	 k3=alpha( 0.001, 1.0, v, -20.0, 7.0 ) *1.0e8* (cai*1.0(/mM) )^3
	 :k3 changes the attenuation
	 k4=alp( 0.2, v, -44.0,  -5.0 ) :original
:	 k4=alp( 0.2, v, -44.0,  -5.0 )
}

FUNCTION alpha( tmin(ms), tmax(ms), v(mV), vhalf(mV), k(mV) )(/ms){
        alpha = 1.0 / ( tmin + 1.0 / ( 1.0 / (tmax-tmin) + exp((v-vhalf)/k)*1.0(/ms) ) )
}

FUNCTION alp( tmin(ms), v(mV), vhalf(mV), k(mV) )(/ms){
        alp = 1.0 / ( tmin + exp( -(v-vhalf) / k )*1.0(ms) )
}