CA1 pyramidal neuron: as a 2-layer NN and subthreshold synaptic summation (Poirazi et al 2003)

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Accession:20212
We developed a CA1 pyramidal cell model calibrated with a broad spectrum of in vitro data. Using simultaneous dendritic and somatic recordings, and combining results for two different response measures (peak vs. mean EPSP), two different stimulus formats (single shock vs. 50 Hz trains), and two different spatial integration conditions (within vs. between-branch summation), we found the cell's subthreshold responses to paired inputs are best described as a sum of nonlinear subunit responses, where the subunits correspond to different dendritic branches. In addition to suggesting a new type of experiment and providing testable predictions, our model shows how conclusions regarding synaptic arithmetic can be influenced by an array of seemingly innocuous experimental design choices.
References:
1 . Poirazi P, Brannon T, Mel BW (2003) Arithmetic of subthreshold synaptic summation in a model CA1 pyramidal cell. Neuron 37:977-87 [PubMed]
2 . Poirazi P, Brannon T, Mel BW (2003) Pyramidal neuron as two-layer neural network. Neuron 37:989-99 [PubMed]
3 . Poirazi P, Brannon T, Mel BW (2003ab-sup) Online Supplement: About the Model Neuron 37 Online:1-20
4 . Polsky A, Mel BW, Schiller J (2004) Computational subunits in thin dendrites of pyramidal cells. Nat Neurosci 7:621-7 [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 GLU cell;
Channel(s): I Na,p; I Na,t; I L high threshold; I T low threshold; I A; I K; I M; I h; I K,Ca; I Calcium;
Gap Junctions:
Receptor(s): GabaA; GabaB; NMDA; Glutamate;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Action Potential Initiation; Activity Patterns; Dendritic Action Potentials; Active Dendrites; Influence of Dendritic Geometry; Detailed Neuronal Models; Action Potentials; Depression; Delay;
Implementer(s): Poirazi, Panayiota [poirazi at imbb.forth.gr];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; GabaA; GabaB; NMDA; Glutamate; I Na,p; I Na,t; I L high threshold; I T low threshold; I A; I K; I M; I h; I K,Ca; I Calcium;
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CA1_multi
lib
basic_graphics.hoc *
basic-graphics.hoc *
choose-secs.hoc *
current-balance.hoc *
cut-sections.hoc *
deduce-ratio.hoc *
find-gmax.hoc *
GABA_shiftsyn.hoc *
GABA_shiftsyn_bg.hoc *
ken.h *
map-segments-to-3d.hoc *
maxmin.hoc *
mod_func.c *
newshiftsyn.c *
newshiftsyn.exe *
num-rec.h *
salloc.hoc *
shiftsyn-init_bg.hoc *
shiftsyn-initA.hoc *
shiftsyn-initA.hoc~ *
spikecount.hoc *
tune-epsps.hoc *
vector-distance.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) 
}














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