Amyloid beta (IA block) effects on a model CA1 pyramidal cell (Morse et al. 2010)

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Accession:87284
The model simulations provide evidence oblique dendrites in CA1 pyramidal neurons are susceptible to hyper-excitability by amyloid beta block of the transient K+ channel, IA. See paper for details.
Reference:
1 . Morse TM, Carnevale NT, Mutalik PG, Migliore M, Shepherd GM (2010) Abnormal excitability of oblique dendrites implicated in early Alzheimer's: a computational study Front. Neural Circuits 4:16 [PubMed]
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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:
Cell Type(s): Hippocampus CA1 pyramidal cell;
Channel(s): I Na,t; I L high threshold; I N; I T low threshold; I A; I K; I h; I K,Ca;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Dendritic Action Potentials; Active Dendrites; Detailed Neuronal Models; Pathophysiology; Aging/Alzheimer`s;
Implementer(s): Carnevale, Ted [Ted.Carnevale at Yale.edu]; Morse, Tom [Tom.Morse at Yale.edu];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal cell; I Na,t; I L high threshold; I N; I T low threshold; I A; I K; I h; I K,Ca;
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CA1_abeta
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readme.html
cacumm.mod
cagk.mod *
cal2.mod *
can2.mod *
cat.mod *
distr.mod *
h.mod
ipulse2.mod *
kadist.mod
kaprox.mod
kdrca1.mod
na3n.mod
naxn.mod *
zcaquant.mod
aBeta.hoc
add_ca.hoc
bAP_peak_vecs.hoc
c91662.ses
C91662_Link.txt
cond_report.hoc
control_boxes.hoc
distribute_currents.hoc
fig1.jpg
fig2.jpg
fig2A_c91662.hoc
fig3.jpg
fig3.ses
fig4.jpg
fig4.ses
fig5.jpg
fig6b.jpg
figs.hoc
find_averages.hoc
fixnseg.hoc
GaspiriniEtAl2007Fig1Stimulation.ses
generate_conc_graph.hoc
gka_averager.hoc
graph_na3_kinetics.hoc
init_and_run_and_graph.hoc
leaky_distal.hoc
maxica.hoc
maxica.ses.20100525
mosinit.hoc
na3_shifter.hoc
ntc_additions.hoc
oblique_application.hoc
oblique_scaled_ka.hoc
obliques_primary_tuft.hoc
paper_fig_buttons.hoc
sectiontest.hoc
shrink_obliques.hoc
SubBranch.hoc
trigger_and_start.hoc
wait_for_go.hoc
                            
// generate_conc_graph.hoc
// updated 20100215 ro remove abeta before each redone abeta graph
load_file("find_averages.hoc")

objref conc_graph, conc_graph_ca
{conc_graph = new Graph(0)}
{conc_graph_ca = new Graph(0)}
proc generate_conc_graph() {
  init()
  // run model in control condition
  runp()
  graph_avg(conc_graph,conc_graph_ca, 1)  // 1 is black for control

  // linear model 0.2 uM aBeta
  aBeta_concentration_factor=0.2
  set_concentration()
  runp()
  graph_avg(conc_graph,conc_graph_ca, 7) // 7 is purple

  // linear model 0.5 uM aBeta
  remove_aBeta()
  aBeta_concentration_factor=0.5
  set_concentration()
  runp()
  graph_avg(conc_graph,conc_graph_ca, 5) // 5 is orange

  // repeat 1 uM aBeta for reference
  remove_aBeta()
  aBeta_concentration_factor=1
  set_concentration()
  runp()
  graph_avg(conc_graph, conc_graph_ca, 2) // red
  conc_graph.exec_menu("View = plot")
}