Linear vs non-linear integration in CA1 oblique dendrites (Gómez González et al. 2011)

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Accession:144450
The hippocampus in well known for its role in learning and memory processes. The CA1 region is the output of the hippocampal formation and pyramidal neurons in this region are the elementary units responsible for the processing and transfer of information to the cortex. Using this detailed single neuron model, it is investigated the conditions under which individual CA1 pyramidal neurons process incoming information in a complex (non-linear) as opposed to a passive (linear) manner. This detailed compartmental model of a CA1 pyramidal neuron is based on one described previously (Poirazi, 2003). The model was adapted to five different reconstructed morphologies for this study, and slightly modified to fit the experimental data of (Losonczy, 2006), and to incorporate evidence in pyramidal neurons for the non-saturation of NMDA receptor-mediated conductances by single glutamate pulses. We first replicate the main findings of (Losonczy, 2006), including the very brief window for nonlinear integration using single-pulse stimuli. We then show that double-pulse stimuli increase a CA1 pyramidal neuron’s tolerance for input asynchrony by at last an order of magnitude. Therefore, it is shown using this model, that the time window for nonlinear integration is extended by more than an order of magnitude when inputs are short bursts as opposed to single spikes.
Reference:
1 . Gómez González JF, Mel BW, Poirazi P (2011) Distinguishing Linear vs. Non-Linear Integration in CA1 Radial Oblique Dendrites: It's about Time. Front Comput Neurosci 5:44 [PubMed]
Citations  Citation Browser
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 GLU cell;
Channel(s): I Na,p; I CAN; I Sodium; I Calcium; I Potassium; I_AHP;
Gap Junctions:
Receptor(s): NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Active Dendrites; Detailed Neuronal Models; Synaptic Integration;
Implementer(s):
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; NMDA; I Na,p; I CAN; I Sodium; I Calcium; I Potassium; I_AHP;
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CA1_Gomez_2011
lib
basic-graphics.hoc *
choose-secs.hoc *
current-balance.hoc
cut-sections.hoc *
deduce-ratio.hoc *
find-gmax.hoc
histographBP_TP02a.hoc
histographBP_TP02b.hoc
histographBP_TP02b_button.hoc
jose.hoc
map-segments-to-3d.hoc *
morphology-lib.hoc
Oblique-lib.hoc *
Oblique-lib2.hoc
salloc.hoc *
spikecount.hoc *
TP-lib.hoc *
tune-epsps.hoc
tune-epspsN128.hoc
tune-epspsSOMA.hoc
vector-distance.hoc
vector-distanceORIGINAL.hoc *
verbose-system.hoc *
                            
// Given a reference point (ie, soma), an apex point, and a point of
// interest, (POI), this function returns the distance from the reference point to
// the POI. These three points are vectors with x,y,z as their values
// written by Terrence Brannon, last modified by Yiota Poirazi, July 2001, poirazi@LNC.usc.edu

objref RP, POI, APEX

proc pvec() {
  printf("%s: \t", $s1)
  $o2.printf("%f ")
}

proc pvecs() {
  pvec("RP", RP)
  pvec("APEX",APEX)
  pvec("POI",POI)
}

proc clear_vecs() {
  RP=new Vector()
  APEX=new Vector()
  POI=new Vector()
}

objref vhold
vhold=new Vector()

func vector_distance() { local adjustment
//  print "func vector_distance() {"

  clear_vecs()
  
  RP=$o1.c
  APEX=$o2.c
  POI=$o3.c
  adjustment = $4

//  pvecs()

  // Subtract Psoma: Qapex = Papex - Psoma. Therefore Qsoma=0,0,0

  APEX.sub(RP)
  POI.sub(RP)
    RP.sub(RP)

//    pvecs()

  // Normalize Qapex, Creating Uapex

  vhold=APEX.c
  vhold.mul(vhold)
  APEX_BAR=sqrt(vhold.sum())

//  printf("APEX_BAR: %f\n", APEX_BAR)

  APEX.div(APEX_BAR)

//  pvec("UAPEX", APEX)

  // Find length of projection of Qdend onto Uapex

  H = POI.dot(APEX) + adjustment
  
  H=abs(H)

  return(H)
}

objref fvd_vec
strdef fvd_str
func find_vector_distance() {

  fvd_vec=new Vector()
  sprint(fvd_str, "access %s", $s1)
  execute1(fvd_str)
  
  vcreate2(fvd_vec,0)
  
  return(vector_distance(vRP,vAPEX,fvd_vec,adjustment))
}

func find_vector_distance_precise() {

  fvd_vec=new Vector()
  sprint(fvd_str, "access %s", $s1)
  execute1(fvd_str)
  
  vcreate3(fvd_vec,$2)
  
  return(vector_distance(vRP,vAPEX,fvd_vec,adjustment))
}

proc vcreate() {
  $o1.append(x3d(0))
  $o1.append(y3d(0))
  $o1.append(z3d(0))
}

proc vcreate2() {
  $o1.append(x3d($2))
  $o1.append(y3d($2))
  $o1.append(z3d($2))
}

proc vcreate3() {
  $o1.append(x_d3($2))
  $o1.append(y_d3($2))
  $o1.append(z_d3($2))
}