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;
// Background synaptic activity in FS basket cells 2018 around 30 Hz to simulate the presynpatic cell firing in Gap junctions experiment. according to Tamas Somogyi 2000 
//Alexandra Tzilivaki.

inhibitory_synapses=8
excitatory_synapses=20//32

ampaweightin=7.5e-4//*0.9
nmdaweightin=3.2e-4*5//*0.9
gabaaweightin=5.1e-4*14*0
 

//number_dendszero=$1
objref  pevstiminh[excitatory_synapses], devstiminh[excitatory_synapses] ,pivstiminh[inhibitory_synapses], divstiminh[inhibitory_synapses]
objref ran 

objref pampain_back[excitatory_synapses], pnmdain_back[excitatory_synapses], pgabaain_back[inhibitory_synapses]
objref pncampainback[excitatory_synapses], pncnmdainback[excitatory_synapses], pngabaaainback[inhibitory_synapses]


mean=0.02

objref dpool, rdend 

dpool= new Vector(number_dendszero) // !!
j=0
for i= 0, number_dendszero-1{
    dpool.x[j]=i
    j=j+1
}

rdend=new Random(number_dendszero)
rdend.uniform(0,number_dendszero-1)

objref eDendperSyn, iDendperSyn

eDendperSyn= new Vector(excitatory_synapses,0)
iDendperSyn = new Vector(inhibitory_synapses,0)

for g=0,eDendperSyn.size()-1 {
    eDendperSyn.x[g]=dpool.x[(rdend.uniform(0,dpool.size()-1))]
 }   


for f=0,iDendperSyn.size()-1 {
    iDendperSyn.x[f]=dpool.x[(rdend.uniform(0,dpool.size()-1))]
 }   



objref rp
rp = new Random()
rp.poisson(mean)
print"123"
objref stimvectorE[excitatory_synapses]
objref stimvectorI[inhibitory_synapses]


for t=0,excitatory_synapses-1{
	stimvectorE[t]= new Vector()
	for k=0,int(tstop)-1{         // $1 the tstop 
		if(rp.repick()){
			stimvectorE[t].append(k)
		}
	}
}


for j=0,inhibitory_synapses-1{
	stimvectorI[j]= new Vector() 
	for l=0,int(tstop)-1{         // $1 the tstop 
		if(rp.repick()){
			stimvectorI[j].append(l)
		}
	}
}



proc call_vecstim() {

ran = new Random(5)
PIDb = ran.uniform(0, 1)
	
// excitatory synapses in dendrites
for syn=0,excitatory_synapses-1 {
edendritis=eDendperSyn.x[syn]
	       pevstiminh[syn] = new VecStim(0.5)
	       pevstiminh[syn].delay = 0
	       pevstiminh[syn].play(stimvectorE[syn])
	       PIDb=ran.repick()

			FSdetailedtemplate[$1].dend[edendritis] pampain_back[syn]=new CPGLUIN(PIDb)
			FSdetailedtemplate[$1].dend[edendritis] pnmdain_back[syn]=new NMDAIN(PIDb)

			pncampainback[syn] = new NetCon(pevstiminh[syn], pampain_back[syn], -20, 0, ampaweightin)
			pncnmdainback[syn] = new NetCon(pevstiminh[syn], pnmdain_back[syn], -20, 0, nmdaweightin)
	}
	

// inhibitory synapses in dendrites
for syn=0,inhibitory_synapses-1 {
idendritis=iDendperSyn.x[syn]
		pivstiminh[syn] = new VecStim(0.5)
		pivstiminh[syn].delay = 0
		pivstiminh[syn].play(stimvectorI[syn])
		PIDb=ran.repick()

		FSdetailedtemplate[$1].dend[idendritis] pgabaain_back[syn]=new GABAain(PIDb)

		pngabaaainback[syn] = new NetCon(pivstiminh[syn], pgabaain_back[syn], -30, 0, gabaaweightin)

	}


} // procedure









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