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 *
                            
// This function is used to deduce the maximum AMPA coductance value 
// at any location along the cell, such that a single current pulse will give
// rise to approximately 5mV local depolarization.
// tune-epsps.hoc is used is advance to calculate maximum AMPA conductance values
// for a few locations along each section (reference list). This function is used to 
// deduce the AMPA conductance for ANY specified location in ANY section by finding 
// the estimated AMPA value of the reference point closest to the specified point.  
// written by Terrence Brannon, last modified by Yiota Poirazi, July 2001, poirazi@LNC.usc.edu
// last modified by Jose Gomez, 1st May 2006, jfcgomez@ull.es

objref find_gmax_vec, find_gmax_dest, find_gmax_index, find_gmax_tmp
func find_gmax() { local range_ref, pre_diff, pre_i, post_gmax, diff, i,R
  
  range_ref = $1 
  find_gmax_vec = new Vector()
  find_gmax_index = new Vector()
  find_gmax_tmp = new Vector()
 
  post_i=-1     

  for i=0,tune_epsp_list.count()-1 {        // for all sections in tune_epsp_list (reference list) 

    if (issection(tune_epsp_list.object(i).section_name)) { 

 //     print "\t\t-- tuning", secname()				//
 //     printf("R=tune_epsp_list.object(%d).range_ref\n", i)	//

      R = tune_epsp_list.object(i).range_ref
 //     printf("R=tune_epsp_list.object(%d).range_ref == %g\n", i,R)	//
      if (R <= range_ref) {
        pre_i=i
        pre_R=R
        }

      if (R >  range_ref) {
        if (post_i < 0) {
           post_i=i
           post_R=R
        }
      }
    }

  }

//print "===================================== calculating ret_gmax, ret_R"
//print "jooooo, post_i", post_i
  if ( (post_i <0) || (abs(pre_R-range_ref) < abs(post_R-range_ref)) ) {
       
       if(pre_R<=0){pre_i=post_i   //I put this if, Jose Gomez,I had wrong
				   //value if the first element if was ret_R=0
		    pre_R=post_R
	}

//        print "pre_i", pre_i					      //
        ret_gmax=tune_epsp_list.object(pre_i).gbar_ampa
        ret_R=pre_R
   } else {
//        print "post_i", post_i, "post_R ", post_R			//
        ret_gmax=tune_epsp_list.object(post_i).gbar_ampa
        ret_R=post_R
   }

//   printf("%g was the closest range_ref to %g for %s\n", ret_R, range_ref, secname()) //

   return(ret_gmax)     
}