Modelling reduced excitability in aged CA1 neurons as a Ca-dependent process (Markaki et al. 2005)

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Accession:119266
"We use a multi-compartmental model of a CA1 pyramidal cell to study changes in hippocampal excitability that result from aging-induced alterations in calcium-dependent membrane mechanisms. The model incorporates N- and L-type calcium channels which are respectively coupled to fast and slow afterhyperpolarization potassium channels. Model parameters are calibrated using physiological data. Computer simulations reproduce the decreased excitability of aged CA1 cells, which results from increased internal calcium accumulation, subsequently larger postburst slow afterhyperpolarization, and enhanced spike frequency adaptation. We find that aging-induced alterations in CA1 excitability can be modelled with simple coupling mechanisms that selectively link specific types of calcium channels to specific calcium-dependent potassium channels."
Reference:
1 . Markaki M, Orphanoudakis S, Poirazi P (2005) Modelling reduced excitability in aged CA1 neurons as a calcium-dependent process Neurocomputing 65-66:305-314
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: Hippocampus;
Cell Type(s): Hippocampus CA1 pyramidal cell;
Channel(s): I Na,p; I Na,t; I L high threshold; I N; I A; I K; I M; I K,Ca; I R;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Activity Patterns; Aging/Alzheimer`s;
Implementer(s):
Search NeuronDB for information about:  Hippocampus CA1 pyramidal cell; I Na,p; I Na,t; I L high threshold; I N; I A; I K; I M; I K,Ca; I R;
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CA1_Aged
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 *
newshiftsyn.c *
num-rec.h *
salloc.hoc *
shiftsyn-init_bg.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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