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
network_model
data
figs
src
.exrc *
.gitignore
LICENSE *
run_simulations.sh
submit_lamodel.sh
                            
function la_run {
	echo $LAPARAMS

	#qsub -v "LAPARAMS=$LAPARAMS" submit_lamodel.sh
	./lamodel $LAPARAMS 
}


for nltype in 0 1 2 3; do
	for clustered in 0 1; do
		for run in {0..20}  ; do

			# sublinear SOM

			LAPARAMS=" -P 1 -T 180 -S 1980$run -o nlTypePV=${nltype} -o nlTypeSOM=1 -o INClustered=${clustered} -s single_${nltype}_1_${clustered}_G_${run} -G"
			la_run

			# supralinear SOM
			LAPARAMS=" -P 1 -T 180 -S 1980$run -o nlTypePV=${nltype} -o nlTypeSOM=0 -o INClustered=${clustered} -s single_${nltype}_0_${clustered}_G_${run} -G"
			la_run

			# linear SOM
			LAPARAMS=" -P 1 -T 180 -S 1980$run -o nlTypePV=${nltype} -o nlTypeSOM=2 -o INClustered=${clustered} -s single_${nltype}_2_${clustered}_G_${run} -G"
			la_run

			# mixed SOM
			LAPARAMS=" -P 1 -T 180 -S 1980$run -o nlTypePV=${nltype} -o nlTypeSOM=3 -o INClustered=${clustered} -s single_${nltype}_3_${clustered}_G_${run} -G"
			la_run

		done
	done
done


for nltype in 0 1 2 3; do
	for clustered in 0 1; do
		for run in {0..9}  ; do


			LAPARAMS=" -P 2 -T 1440 -S 1980$run -o nlTypePV=${nltype}  -o nlTypeSOM=1 -o INClustered=${clustered} -s two1440_${nltype}_${clustered}_G_${run} -G"
			la_run

			LAPARAMS=" -P 2 -T 60 -S 1980$run -o nlTypePV=${nltype} -o nlTypeSOM=1 -o INClustered=${clustered} -s two_${nltype}_${clustered}_G_${run} -G"
			la_run


		done
	done
done

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