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

 Download zip file   Auto-launch 
Help downloading and running models
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]
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;
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 cell; NMDA; I Na,p; I CAN; I Sodium; I Calcium; I Potassium; I_AHP;
/
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 select dendritic sections (branches) to be used
// in the experiments. For each selected section, one synapse candidate is allocated 

objref vtmp,tmpo
objref tipl,cand_tipl
func choose_secs() {  local i, range_posn, copies, resolution, lo, hi

  tipl = $o1    // neuron part (list) from which to select sections  
  lo = $3       // lowest distance from soma for selected sections
  hi = $4       // highest distance from soma for selected sections
  actual_resolution = $5  // obsolete. Used only if more than one synapses are to be placed at a specific location
  desired_resolution = $6 // obsolete. Used only if more than one synapses are to be placed at a specific location
  section_count = 0       

   forsec tipl {
     for (range_posn) {
 
        vtmp=new Vector()
        vcreate2(vtmp,range_posn)
        dist=vector_distance(vRP,vAPEX,vtmp,adjustment,1)
//        print "The vertical distance for ", secname(), " is ", dist
        if ((dist > lo) && (dist < hi)) {
          section_count=section_count+1
//        copies = int( L / (actual_resolution/desired_resolution) ) non used in the present experiments
          copies = 1
//          printf("Adding %d copies of synapse candidate at %s(%g)\n", copies, secname(),range_posn)
          for i=1,copies {
             tmpo = new RangeRef(range_posn,0)
             $o2.append(tmpo)
          } 
        }
     }
   }
  return(section_count) 
}


// Same as the above function, with an additional restriction: sections selected
// are such that their middle (x=0.5) is within [lo high] microns from soma and 
// synapses are allocated only at x=0.5  ==> choosing branches

func choose_secs_branchwise() {  local i, range_posn, copies, resolution, lo, hi

  tipl=$o1      
  lo=$3
  hi=$4
  actual_resolution=$5  
  desired_resolution=$6
  section_count=0       

   forsec tipl {

        range_posn=0.5
        vtmp=new Vector()
        vcreate2(vtmp,range_posn)
        dist=vector_distance(vRP,vAPEX,vtmp,adjustment,1)
//        print "The vector distance for ", secname(), " is ", dist
        if ((dist > lo) && (dist < hi)) {
          section_count=section_count+1
          //      copies = int( L / (actual_resolution/desired_resolution) )
          copies = 1
//          printf("Adding %d copies of synapse candidate at %s(%g)\n", copies, secname(),range_posn)
          for i=1,copies {
             tmpo = new RangeRef(range_posn,0)
             $o2.append(tmpo)
          } 
     }
   }
  return(section_count) 
}














Loading data, please wait...