CA1 pyramidal neuron: schizophrenic behavior (Migliore et al. 2011)

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Accession:138205
NEURON files from the paper: A modeling study suggesting how a reduction in the context-dependent input on CA1 pyramidal neurons could generate schizophrenic behavior. by M. Migliore, I. De Blasi, D. Tegolo, R. Migliore, Neural Networks,(2011), doi:10.1016/j.neunet.2011.01.001. Starting from the experimentally supported assumption on hippocampal neurons we explore an experimentally testable prediction at the single neuron level. The model shows how and to what extent a pathological hypofunction of a contextdependent distal input on a CA1 neuron can generate hallucinations by altering the normal recall of objects on which the neuron has been previously tuned. The results suggest that a change in the context during the recall phase may cause an occasional but very significant change in the set of active dendrites used for features recognition, leading to a distorted perception of objects.
Reference:
1 . Migliore M, De Blasi I, Tegolo D, Migliore R (2011) A modeling study suggesting how a reduction in the context-dependent input on CA1 pyramidal neurons could generate schizophrenic behavior. Neural Netw 24:552-9 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Dendrite;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s): Hippocampus CA1 pyramidal GLU cell;
Channel(s): I Na,t; I A; I K; I h; I Potassium;
Gap Junctions:
Receptor(s): AMPA;
Gene(s):
Transmitter(s): Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Dendritic Action Potentials; Coincidence Detection; Active Dendrites; Influence of Dendritic Geometry; Detailed Neuronal Models; Action Potentials; Synaptic Integration; Schizophrenia; Hallucinations;
Implementer(s): Migliore, Michele [Michele.Migliore at Yale.edu];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; AMPA; I Na,t; I A; I K; I h; I Potassium; Glutamate;
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Schizophr
readme.txt
distr.mod *
Gfluct.mod
h.mod *
kadist.mod *
kaprox.mod *
kdrca1.mod *
na3n.mod *
naxn.mod *
netstims.mod *
average.hoc
c033-all-seeds.txt
condu.txt
fixnseg.hoc *
geo9068802.hoc
mosinit.hoc
schizopr.ses
sim_9068802-test.hoc
                            
objref fil, vec_ob, vec1, vec2, vec3, oblist, g
strdef infile1
fil = new File()
last = 5  // ******  n-ple size */
cont =11  // ******  number of obliques*/
vec_ob = new Vector(27,-1)
vec1 = new Vector(cont,0)
vec2= new Vector(46, 0)
vec3= new Vector(46, 0)
oblist=new Vector()
oblist.append(0, 5, 8, 9, 14, 17, 18, 19, 22, 23, 26)//list of obliques

sprint(infile1,"c033-all-seeds.txt")	
line =0

fil.ropen(infile1)

while(!fil.eof()){	
	for i=0, last-1{
		oblique = fil.scanvar()
		vec_ob.x[oblique] = vec_ob.x[oblique] + 1  	
	}

	dummy = fil.scanvar()   
	dummy = fil.scanvar()   
	dummy = fil.scanvar()   
	line = line +1
}
fil.close()

/*  average presence for each oblique in valid 5-ple */
for i = 0, oblist.size()-1{
	vec1.x[i] = 100*(vec_ob.x[oblist.x[i]] + 1) / line
     }

/* plot */
for (t=0; t<11; t=t+1) {
vec3.x[4*t+2]=vec1.x[t]
vec3.x[4*t+3]=vec1.x[t]
}

for (t=0; t<22; t=t+1) {
vec2.x[2*t+1] = 0.75+0.5*t
vec2.x[2*t+2] = 0.75+0.5*t
}

g = new Graph()
g.size(0,12,0,100)
g.yaxis(3)
g.yaxis(0,100)
g.beginline()
for (t=0; t<46; t=t+1){
     g.line(vec2.x[t],vec3.x[t])
}
g.flush()


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