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

 Download zip file   Auto-launch 
Help downloading and running models
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.
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]
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,p; I CAN; I Sodium; I Calcium; I Potassium; I_AHP;
Gap Junctions:
Receptor(s): NMDA;
Simulation Environment: NEURON;
Model Concept(s): Active Dendrites; Detailed Neuronal Models; Synaptic Integration;
Search NeuronDB for information about:  Hippocampus CA1 pyramidal cell; NMDA; I Na,p; I CAN; I Sodium; I Calcium; I Potassium; I_AHP;
basic-graphics.hoc *
choose-secs.hoc *
cut-sections.hoc *
deduce-ratio.hoc *
map-segments-to-3d.hoc *
Oblique-lib.hoc *
salloc.hoc *
spikecount.hoc *
TP-lib.hoc *
vector-distanceORIGINAL.hoc *
verbose-system.hoc *
// This function is used to calculate the AMPA coductance values
// such that a single pulse stimulus gives rise to a 5mV local depolarization 
// at every synapse location along the cell (each synapse has both an AMPA and an NMDA mechanism).
// AMPA values for each location tested, along the sections tested are saved in the tune_epsp_list
// written by Terrence Brannon, last modified by Yiota Poirazi, July 2001,
// Modified by Jose Gomez

strdef fast_file
strdef sec_str, recstr, tuning_code
objref epsp_glu, epsp_nmda, vmvec, epsp_ic, tmpvec, ns, nc_glu, nc_nmda

proc tune_epsp_fast () { 
//  printf("proc tune_epsp_fast (%s.v(%g))\n", secname(), $2)

  R = $2                 //  synapse location
  epsp_glu  = new GLU(R)
  epsp_nmda = new NMDA(R)		//before was NMDAb (I don't Know why, jose)
  tuned = 0             //  initial values before tuning
  old_GMAX = 0

  ns = new NetStim(0.5)  
  ns.start = 20    //jose before 20
  ns.number =1
  while (!tuned) {

	nc_glu = new NetCon(ns,epsp_glu)

  	nc_nmda = new NetCon(ns,epsp_nmda)		//before was epsp_glu, I don't know why,jose

   	nc_glu.weight  = GMAX     // previously calculated maximum AMPA conductance
   	nc_nmda.weight = GMAX*NMDA_AMPA_RATIO  // maximum NMDA conductance 

   tmpvec = new Vector()
   sprint(recstr, "tmpvec.record(&%s.v(%g))", secname(), R) // record depolarization at synapse

   vmvec = tmpvec.c
   // test if resulting depolarization is closed to the desired value (5mV)

   if (epsilon_equal(vmvec.max(),desired_voltage,$3)) {
     tuned = 1  // if yes, stop         

   } else {     // if no, update the GMAX value and test again

     diffa = desired_voltage-BASELINE
     diffb = vmvec.max()-BASELINE
     ratio = diffa/diffb
     GMAX = GMAX*ratio
     print "\t\tnew GMAX: ", GMAX

 sprint(tuning_code,"%s tune_epsp_list.append(new EPSPTuning(\"%s\",%f,%f,1))", secname(), secname(), x, GMAX)
 $o4.printf("%s\n", tuning_code)


// test if difference between desired and actual voltage is smaller than epsilon

func epsilon_equal() {
  printf("epsilon_equal(%f,%f,%f)\n", $1,$2,$3)
  diff = ($1-$2)
  if (abs(diff) < $3) {
  } else {

Loading data, please wait...