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;
// Version: $Id: lamodel.cpp 172 2014-02-12 10:06:07Z gk $
/* 
 
 
lamodel is a network simulator that simulates memory engram formation 
in a population consisting of excitatory and inhibitory neurons with independent
dendritic subunits. 

This is the entry point for the network simulator. 
This file parses the command line parameters 

Basic command line options for the simulator:

Example: ./lamodel -P 2 -T 1400 [-G] [-L] [-s datadir-name] [-S random-seed]

-P <patterns>: number of memories  to enoode
-T <minutes>: Interval between memories (minutes)
-G: Global-only protein synthesis 
-L: Dendritic-only protein synthesis 
-s <dirname>: Name of the directory to store the data (inside the data/ folder)
-S <random-seed>: Intitialize the random generator

-o <param-name>=<param-value>: Set various simulation parameters:

-o nlTypePV=[0,1,2,3]   : Set basket cell dendrite nonlinearity type. 0=supra, 1=sub, 2=linear, 3=mixed supra/sub
-o nlTypeSOM=[0,1,2,3]  : Set SOM+ cell dendrite nonlinearity type. 0=supra, 1=sub, 2=linear, 3=mixed supra/sub
-o INClustered=[0,1]    : Set whether IN synapses  should target all dendrites randomly (dispersed) or only 33% of them (clustered)


*/


#include "constructs.h"
#include <iostream>
#include <cstring>
#include <string>
#include <unistd.h>
#include <getopt.h>


// Parse command line  arguments
int main( int argc, char* argv[])
{
	
	int c;

	// Set defaults
	int nneurons = 500;
	int nbranches = 20;
	int ninputs = 400;
	int nperinput = 1;
	int npatterns = ninputs;
	int nonesperpattern = 1;
	int interstim = 60;
	int rseed = 1980;
	char* suffix = NULL;
	bool storeData = false;
	bool disableCreb = false;
	int patternsOverlapping = -1;

	LANetwork net; 
	net.enablePruning = true; // default

	while ((c = getopt(argc, argv, "M:N:H:B:I:i:P:p:T:S:s:d:w:O:g:l:b:c:o:t:xnLDRJCGhU"))!= -1)
	{
		switch (c)
		{
			case '?':
			case 'h':
			cout <<
			"Basic command line options for the simulator:" << endl << 
			"Example: ./lamodel -P 2 -T 1400 [-G] [-L] [-s datadir-name] [-S random-seed]" << endl << 
			"-P <patterns>: number of memories  to enoode"<< endl << 
			"-T <minutes>: Interval between memories (minutes)"<< endl << 
			"-G: Global-only protein synthesis "<< endl << 
			"-L: Dendritic-only protein synthesis "<< endl << 
			"-s <dirname>: Name of the directory to store the data (inside the data/ folder)"<< endl << 
			"-S <random-seed>: Intitialize the random generator"<< endl << 
			""<< endl << 
			"-o <param-name>=<param-value>: Set various simulation parameters:"<< endl << 
			""<< endl << 
			"-o nlTypePV=[0,1,2,3]   : Set basket cell dendrite nonlinearity type. 0=supra, 1=sub, 2=linear, 3=mixed supra/sub"<< endl << 
			"-o nlTypeSOM=[0,1,2,3]  : Set SOM+ cell dendrite nonlinearity type. 0=supra, 1=sub, 2=linear, 3=mixed supra/sub" << endl << 
			"-o INClustered=[0,1]    : Set whether IN synapses  should target all dendrites randomly (dispersed) or only 33% of them (clustered)" << endl;
			return 1;
			break;

			case 'B': nbranches = atoi(optarg); break;
			//case 'I': ninputs = atoi(optarg); break;
			//case 'i': nperinput = atoi(optarg); break;
			case 'N': nneurons = atoi(optarg); break;
			case 'P': npatterns = atoi(optarg); break;
			case 'p': nonesperpattern = atoi(optarg); break;
			case 'T': interstim = atoi(optarg); break;
			case 'S': rseed = ( atoi(optarg)); break;
			case 's': suffix = strdup(optarg); break;
			case 'd': patternsOverlapping = atoi(optarg); break;

			case 'x': storeData = true; break;
			case 'n': disableCreb = true; break;
			case 'w': net.isWeakMem.push_back(atoi(optarg)-1); break;

			case 'L': net.localProteins = true; break;
			case 'G': net.globalProteins = true; break;
			case 'D': net.debugMode = true; break;
			case 'R': net.repeatedLearning = true; break;
			case 'J': net.pretraining = true; break;
			case 'C': net.altConnectivity = true; break;
			case 'O': net.branchOverlap = atof(optarg); break;
			case 'H': net.homeostasisTime = atof(optarg); break;


			case 'o': 
				char* o = strstr(optarg, "=");
				if (o)
				{
					*o = '\0';
					char* val = o+1;

					if (!strcmp(optarg, "connectivityParam")) net.connectivityParam = atof(val); 
					else if (!strcmp(optarg,  "BSPTimeParam")) net.BSPTimeParam = atof(val); 
					else if (!strcmp(optarg,  "homeostasisTimeParam")) net.homeostasisTimeParam = atof(val); 
					else if (!strcmp(optarg,  "CREBTimeParam")) net.CREBTimeParam = atof(val); 
					else if (!strcmp(optarg,  "inhibitionParam")) net.inhibitionParam = atof(val); 
					else if (!strcmp(optarg,  "globalPRPThresh")) net.globalPRPThresh = atof(val); 
					else if (!strcmp(optarg,  "localPRPThresh")) net.localPRPThresh = atof(val); 
					else if (!strcmp(optarg,  "dendSpikeThresh")) net.dendSpikeThresh = atof(val); 
					else if (!strcmp(optarg,  "initWeight")) net.initWeight*= atof(val); 
					else if (!strcmp(optarg,  "maxWeight")) net.maxWeight*= atof(val); 
					else if (!strcmp(optarg,  "stimDurationParam")) net.stimDurationParam = atof(val); 
					else if (!strcmp(optarg,  "nNeuronsParam")) nneurons *= atof(val); 
					else if (!strcmp(optarg,  "nBranchesParam")) nbranches *= atof(val); 
					else if (!strcmp(optarg,  "nBranchesTurnover")) net.nBranchesTurnover = atoi(val); 
					else if (!strcmp(optarg,  "INClustered")) net.INClustered = atoi(val); 
					else if (!strcmp(optarg,  "nlTypePV")) net.nlTypePV = atoi(val); 
					else if (!strcmp(optarg,  "nlTypeSOM")) net.nlTypeSOM = atoi(val); 

					printf("Param name='%s' value='%f'\n", optarg, atof(val));
				}
			break;
		}
	}

	printf("Params:\n net.nBranchesTurnover=%d\n net.connectivityParam=%f\n net.BSPTimeParam=%f\n net.homeostasisTimeParam=%f\n net.CREBTimeParam=%f\n net.inhibitionParam=%f\n net.globalPRPThresh=%f\n net.localPRPThresh=%f\n net.dendSpikeThresh=%f\n net.initWeight=%f\n net.maxWeight=%f\n net.nlTypePv = %d \nnet.nlTypeSOM=%d INClustered=%d\n-------\n" , 

	net.nBranchesTurnover, net.connectivityParam , net.BSPTimeParam , net.homeostasisTimeParam , net.CREBTimeParam , net.inhibitionParam , net.globalPRPThresh , net.localPRPThresh , net.dendSpikeThresh , net.initWeight, net.maxWeight, net.nlTypePV, net.nlTypeSOM, net.INClustered );

	LANetwork::SetRandomSeed(rseed);
	net.disableCreb = disableCreb;

	ninputs = nonesperpattern * npatterns;
	printf("\nNinputs=%d, nPerInput=%d, patterns=%d\n", ninputs, nperinput, npatterns);

	// Create the network 
	net.CreateFearNet(nneurons, nbranches, ninputs, nperinput);

	char buf[512];
	if (suffix)
		sprintf(buf, "./data/%s", suffix );
	else
		sprintf(buf, "./data/N%d.B%d.I%d.i%d.P%d.p%d.T%d.S%d.w%d_%s", nneurons, nbranches, ninputs, nperinput, npatterns, nonesperpattern, interstim, rseed, (int)net.isWeakMem.size(),  suffix ? suffix : "");

	cout << "Data directory is "<< buf <<  endl;
	net.SetDataDir( buf);

	if (net.pretraining)
	{
		char buf2[1024];
		sprintf(buf2, "%s/%s", buf, "pre-syn.dat");
		net.SaveSynapseState(buf2);
	}

	cout << "Running main simulation..."<< endl;
	net.RunStoreTest(npatterns, nonesperpattern, interstim, 0, patternsOverlapping);

	cout << "Storing data files ..."<< endl;
	cout<<buf << endl;
	net.StoreDataFiles( storeData);

	printf("Done!\n");


	return 0;
}




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