Hopfield and Brody model (Hopfield, Brody 2000)

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Accession:2798
NEURON implementation of the Hopfield and Brody model from the papers: JJ Hopfield and CD Brody (2000) JJ Hopfield and CD Brody (2001). Instructions are provided in the below readme.txt file.
References:
1 . Hopfield JJ, Brody CD (2001) What is a moment? Transient synchrony as a collective mechanism for spatiotemporal integration. Proc Natl Acad Sci U S A 98:1282-7 [PubMed]
2 . Hopfield JJ, Brody CD (2000) What is a moment? "Cortical" sensory integration over a brief interval. Proc Natl Acad Sci U S A 97:13919-24 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Olfactory cortex;
Cell Type(s):
Channel(s): I Na,t; I A;
Gap Junctions:
Receptor(s): AMPA; Gaba;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Pattern Recognition; Coincidence Detection; Temporal Pattern Generation; Synchronization; Attractor Neural Network; Olfaction;
Implementer(s): Migliore, Michele [Michele.Migliore at Yale.edu];
Search NeuronDB for information about:  AMPA; Gaba; I Na,t; I A;
load_file("nrngui.hoc")
load_file("model_hb.hoc")

dt=0.05
tstop=1000


//********************** simulation window ******************************
b = new VBox()
b.intercept(1)
gs = new Graph(0)
gr = new Graph(0)
gf = new Graph(0)
gs.view(0,-70,tstop,100,0,0,100,100)
gs.label(0.4,0.9,"Gamma Cell ")
gr.view(0,0,tstop,31,0,200,10,100)
gr.label(0.4,0.92," raster plot ")
gf.view(0,0,tstop,110,0,300,100,100)
gf.label(0.4,0.9," firing rates (Hz) ")
xpanel("",1)
xbutton("before training", "run_u()")
xbutton("after training", "run_c()")
xbutton("random activation", "run_r()")
xpanel()
b.intercept(0)
b.map("Hopfield-Brody model",100,0,300,600)

proc init() {
	elaps=0

	finitialize(v_init)
	fcurrent()

	forall {
	if (ismembrane("pas")) {e_pas=v_init}
	if (ismembrane("nans")) {e_pas=v_init+(ina+ik)/g_pas}
	}
}

proc step() {
	fadvance()
	gs.plot(t)

if (t>elaps) {
	print t
	elaps=elaps+100
	gs.flush()
	doNotify()
	}
}

proc run() {
	init()
	while(t<tstop) { step()}

//*************** raster plot ***************************
	ydisp=1
	for j=0, inchan-1 {
	for i=0, outchan-1 {
		yr.resize(spike[j][i].size())
		yr.fill(ydisp)
		for k=0, dimcon-1 {
		if (j==index[k][0] && i==index[k][1]) {
		yr.mark(gr,spike[j][i],"|",5)
		ydisp=ydisp+1
		gr.flush()
		doNotify()
				}
		}
	}
	}

//************** firing rates plot **********************
for j=0, inchan-1 {
	clr=j+1
	while (clr>9) {
	clr=clr-9
	}
	gf.color(clr)
for i=0, outchan-1 {
	if (spike[j][i].size()>2) {
		yf.resize(spike[j][i].size()-1)
			for k=1, spike[j][i].size()-1 {
			yf.x[k-1]=1e3/(spike[j][i].x[k]-spike[j][i].x[k-1])
						}
		spike[j][i].rotate(-1,0)
		spike[j][i].resize(spike[j][i].size()-1)
			for k=0, dimcon-1 {
				if (j==index[k][0] && i==index[k][1]) {
				yf.line(gf,spike[j][i])
				gf.flush()
				doNotify()
								}
					}
				}
}
}
}

//****run procedure for simulation before training (we=wi=0)*********
proc run_u() {
	for j=0, inchan-1 {
	acell s[j].start=chan[j]
	}
	gs.exec_menu("Keep Lines")
	gs.addvar("gamma.v(0.5)",1,1, 2*tstop,0,2)
	gs.begin()
	gr.exec_menu("Keep Lines")
	gr.color(1)
	gr.brush(1)
	gr.begin()
	gf.exec_menu("Keep Lines")
	gf.brush(1)
	gf.begin()
	we=0.0
	wi=0.0
	setweights()
	run()
}

//****run procedure for simulation after training (we>0,wi>0)*********
proc run_c() {
	for j=0, inchan-1 {
	acell s[j].start=chan[j]
	}
	ropen()
	gs.exec_menu("Keep Lines")
	gs.addvar("gamma.v(0.5)",2,1, 2*tstop,0,2)
	gs.begin()
	gr.exec_menu("Keep Lines")
	gr.color(2)
	gr.brush(2)
	gr.begin()
	gf.exec_menu("Keep Lines")
	gf.brush(2)
	gf.begin()
	we=0.00001
	wi=0.000015
	setweights()
	run()
}

//****procedure for random activation times after training (we>=0, wi>0)*****
proc run_r() {
	r=new Random()
	for j=0, inchan-1 {
	acell s[j].start=r.uniform(200,350)
	}
	gs.exec_menu("Keep Lines")
	gs.addvar("gamma.v(0.5)",3,1, 2*tstop,0,2)
	gs.begin()
	gr.exec_menu("Keep Lines")
	gr.color(3)
	gr.brush(1)
	gr.begin()
	gf.exec_menu("Keep Lines")
	gf.brush(1)
	gf.begin()
	we=0.00001
	wi=0.000015
	setweights()
	run()
}

proc setweights() {
	for i=0, dimcon-1 {
		xx0=index[i][0]
		xx1=index[i][1]
		for j=0, dimcon-1 {
			yy0=index[j][0]
			yy1=index[j][1]
			if (j!=i) {
			netaa[i][j].weight=we
			netbb[i][j].weight=wi
				}
			netba[i][j].weight=wi
			netab[i][j].weight=we
		}
	}
}




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