Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018)

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Accession:227087
Model for the following publication: Adam C. Frank, Shan Huang, Miou Zhou, Amos Gdalyahu, George Kastellakis, Panayiota Poirazi, Tawnie K. Silva, Ximiao Wen, Joshua T. Trachtenberg, and Alcino J. Silva Hotspots of Dendritic Spine Turnover Facilitate Learning-related Clustered Spine Addition and Network Sparsity
Reference:
1 . Frank AC, Huang S, Zhou M, Gdalyahu A, Kastellakis G, Silva TK, Lu E, Wen X, Poirazi P, Trachtenberg JT, Silva AJ (2018) Hotspots of dendritic spine turnover facilitate clustered spine addition and learning and memory. Nat Commun 9:422 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Connectionist Network;
Brain Region(s)/Organism:
Cell Type(s): Abstract integrate-and-fire leaky neuron with dendritic subunits;
Channel(s):
Gap Junctions:
Receptor(s): NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: C or C++ program; MATLAB;
Model Concept(s): Active Dendrites; Synaptic Plasticity;
Implementer(s): Kastellakis, George [gkastel at gmail.com];
Search NeuronDB for information about:  NMDA;
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lamodel
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//
// Version: $Id: lamodel.cpp 172 2014-02-12 10:06:07Z gk $
//

/* 
    This program is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    This program is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.

    You should have received a copy of the GNU General Public License
    along with this program.  If not, see <http://www.gnu.org/licenses/>.
*/


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



int main( int argc, char* argv[])
{
	
	int c;


	// Set defaults
	int nneurons = 300;
	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:h:b:c:o:t:xnLDRJCGU"))!= -1)
	{
		switch (c)
		{
			case '?':
			cout << "usage: "<< argv[0] << " -N nneurons -B nbranches   -P npatterns -p ones_per_pattern -d overlappingPatternOffset -T interstim -S random_seed -w weakMemId " << 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,  "turnoverHotspots")) net.turnoverHotspots = 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" , 
	net.nBranchesTurnover, net.connectivityParam , net.BSPTimeParam , net.homeostasisTimeParam , net.CREBTimeParam , net.inhibitionParam , net.globalPRPThresh , net.localPRPThresh , net.dendSpikeThresh , net.initWeight, net.maxWeight);

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

	// Override;
	ninputs = nonesperpattern * npatterns;

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

	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 << "Output dir: "<< buf <<  endl;

	net.SetDataDir( buf);

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

	net.RunStoreTest(npatterns, nonesperpattern, interstim, 0, patternsOverlapping);
	cout << "Storing data files ..."<< endl;
	cout<<buf << endl;
	net.StoreDataFiles( storeData);
	printf("Done!\n");

	char buf2[512];
	sprintf(buf2, "cp constructs.cpp %s/", buf);
	system(buf2);

	return 0;
}




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