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 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 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
                            
{load_file("nrngui.hoc")}
objectvar save_window_, rvp_
objectvar scene_vector_[5]
objectvar ocbox_, ocbox_list_, scene_, scene_list_
{ocbox_list_ = new List()  scene_list_ = new List()}
{pwman_place(0,0,0)}
{
save_window_ = new PlotShape(0)
save_window_.size(-420.33,480.579,-180.474,710.514)
save_window_.variable("v")
scene_vector_[2] = save_window_
{save_window_.view(-420.33, -180.474, 900.909, 890.988, 732, 594, 201.6, 199.38)}
fast_flush_list.append(save_window_)
save_window_.save_name("fast_flush_list.")
}
{
save_window_ = new Graph(0)
save_window_.size(0,100,-80,40)
scene_vector_[3] = save_window_
{save_window_.view(0, -80, 100, 120, 201, 696, 299.52, 199.38)}
graphList[0].append(save_window_)
save_window_.save_name("graphList[0].")
save_window_.addexpr("v(.5)", 1, 1, 0.8, 0.9, 2)
}
{
save_window_ = new Graph(0)
save_window_.size(-285.309,821.551,-80,40)
scene_vector_[4] = save_window_
{save_window_.view(-285.309, -80, 1106.86, 120, 558, 156, 299.52, 199.38)}
flush_list.append(save_window_)
save_window_.save_name("flush_list.")
objectvar rvp_
rvp_ = new RangeVarPlot("v")
dendrite[63] rvp_.begin(1)
user5[30] rvp_.end(1)
rvp_.origin(8.037)
save_window_.addobject(rvp_, 2, 1, 0.8, 0.9)
}
objectvar scene_vector_[1]
{doNotify()}

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