Two populations of excitatory neurons in the superficial retrosplenial cortex (Brennan et al 2020)

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Accession:260192
Hyperexcitable neurons enable precise and persistent information encoding in the superficial retrosplenial cortex
Reference:
1 . Brennan EKW, Sudhakar SK, Jedrasiak-Cape I, John TT, Ahmed OJ (2020) Hyperexcitable Neurons Enable Precise and Persistent Information Encoding in the Superficial Retrosplenial Cortex. Cell Rep 30:1598-1612.e8 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type:
Brain Region(s)/Organism: Neocortex;
Cell Type(s):
Channel(s): I Sodium; I Potassium;
Gap Junctions:
Receptor(s): AMPA; Gaba;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s):
Implementer(s): Sudhakar, Shyam Kumar [shyamk at umich.edu];
Search NeuronDB for information about:  AMPA; Gaba; I Sodium; I Potassium;
load_file("nrngui.hoc")
xopen("RS_neuron_tuned.hoc") 
xopen("LR_neuron_tuned.hoc") 




n=1
objref cell[100],hines,g,tvec
g = new Graph()
tvec = new Vector()

proc hines1(){
        dt = 0.025
        printf("%d t=%g dt=%g dreal=%g treal=%g\n", \
                0, t, dt, startsw()-hinest2, startsw()-hinest1)
             
        hinest2 = startsw()
        cvode.event(t + 1, "hines1()")
        



}

proc init() {

    finitialize()

    if (cvode_active()) {
        cvode.re_init()
    } else {
        fcurrent()
    }
    frecord_init()
}



for i=0,n-1{
  
cell[i] = new RS_neuron_tuned(1) 

}




tstop = 1500
hinest1 = startsw()
hinest2 = startsw()
hines = new FInitializeHandler(2, "hinest1=startsw() hinest2=startsw() hines1()")
finitialize()



tvec.record(&t)

proc runandplot() {
  g.exec_menu("Erase") 
  g.addexpr("RS_neuron_model",7,1)
  run()
  cell[0].voltagem.plot(g,tvec) 
  g.exec_menu("View = plot") 
}


runandplot()
cell[0].spiketimes.printf
//quit()

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