Correcting space clamp in dendrites (Schaefer et al. 2003 and 2007)

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Accession:22203
In voltage-clamp experiments, incomplete space clamp distorts the recorded currents, rendering accurate analysis impossible. Here, we present a simple numerical algorithm that corrects such distortions. The method enabled accurate retrieval of the local densities, kinetics, and density gradients of somatic and dendritic channels. The correction method was applied to two-electrode voltage-clamp recordings of K currents from the apical dendrite of layer 5 neocortical pyramidal neurons. The generality and robustness of the algorithm make it a useful tool for voltage-clamp analysis of voltage-gated currents in structures of any morphology that is amenable to the voltage-clamp technique.
Reference:
1 . Schaefer AT, Helmstaedter M, Sakmann B, Korngreen A (2003) Correction of conductance measurements in non-space-clamped structures: 1. Voltage-gated K+ channels. Biophys J 84:3508-28 [PubMed]
2 . Schaefer AT, Helmstaedter M, Schmitt AC, Bar-Yehuda D, Almog M, Ben-Porat H, Sakmann B, Korngreen A (2007) Dendritic voltage-gated K+ conductance gradient in pyramidal neurones of neocortical layer 5B from rats. J Physiol 579:737-52 [PubMed]
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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): Neocortex M1 L5B pyramidal pyramidal tract GLU cell;
Channel(s): I K; I K,leak; I M; I Potassium;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Parameter Fitting; Influence of Dendritic Geometry; Detailed Neuronal Models;
Implementer(s): Schaefer, Andreas T [andreas.schaefer at crick.ac.uk];
Search NeuronDB for information about:  Neocortex M1 L5B pyramidal pyramidal tract GLU cell; I K; I K,leak; I M; I Potassium; 
// routines for reducing cell model


proc SetOrigin() {
  stimSec.sec distance(0,stimPosI)
}

func min() {
  if ($1<$2) return $1 else return $2
}

proc ReduceModel() { local totalnseg
  print "reducing model ......."

  totalnseg=0
  forall totalnseg+=nseg
  print "number of segments = ", totalnseg
  //forall nseg=L/10+2
  SetOrigin()
  forall if (min(distance(0.5), distance(1))>400) { nseg=L/400+1 SetOrigin() }
  forall if (min(distance(0.5), distance(1))>300 && min(distance(0.5), distance(1)) <=400) { nseg=L/300+1 SetOrigin() }
  forall if (min(distance(0.5), distance(1))>100 && min(distance(0.5), distance(1)) <=300) { nseg=L/100+1 SetOrigin()  }
  forall if (min(distance(0.5), distance(1))>50 && min(distance(0.5), distance(1)) <=100) { nseg=L/20+1 SetOrigin()  }
  //forall if (min(distance(0.5), distance(1))>0 && min(distance(0.5), distance(1)) <=50) { nseg=L/10+1 SetOrigin()  }
  //stimSec.sec nseg=L/10+1
  SetOrigin()
  totalnseg=0
  forall totalnseg+=nseg
  print "number of segments in reduced model = ", totalnseg
}