Principles governing the operation of synaptic inhibition in dendrites (Gidon & Segev 2012)

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A simple result of Gidon & Segev 2012 was provided where distal (off-path) inhibition is demonstrated to be more effective than proximal (on-path) inhibition in a ball and stick neuron.
1 . Gidon A, Segev I (2012) Principles governing the operation of synaptic inhibition in dendrites. Neuron 75:330-41 [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):
Gap Junctions:
Simulation Environment: NEURON;
Model Concept(s): Simplified Models; Synaptic Integration;
Implementer(s): Morse, Tom [Tom.Morse at];
// set_t_table.hoc
// loads the t_table in exp2synNMDA.mod

// Create a synapse to load the table?
create dummy
access dummy
objref dummy_synapse
dummy_synapse = new NMDA(0.5)
print "Change tau1 and tau2 in the exp2synNMDA.mod file and "
print "reload the table in its NMDA POINT_PROCESS if desired to change these:"
print " tau1: ",dummy_synapse.tau1
print " tau2: ",dummy_synapse.tau2

// create the vectors that will load the table
// Use same tau1, tau2 that are used in exp2synNMDA.mod
// tau1 = 0.5 // Gidon, personal communication 20131010
// tau2 = 44
// It's OK to change tau1, tau2.  It is critical however
// to make sure that the table that is loaded as generated
// below and the values in the exp2synNMDA.mod file, i.e.
// dummy_synapse.tau1 and .tau2 are set to the same tau1, tau2
tau1 = dummy_synapse.tau1
tau2 = dummy_synapse.tau2

objref t_vec, g_vec
objref g_g

proc set_taus() {
  tau1 = $1
  tau2 = $2
  // time of peak (found by setting derivative of g = 0)
  tp = (tau1*tau2)/(tau2 - tau1) * log(tau2/tau1)
  factor = -exp(-tp/tau1) + exp(-tp/tau2)
  factor = 1/factor

  // num_of_points = 100  // instead of choosing an arbitrary value like 100
                          // make the number of points such that there is
                          // is a point for each time step on the way to the
                          // the peak.
  t_vec = new Vector()

  dt_tmp = 0.025 // make the time step at least as small as the default
  // If dt happens to be smaller than the default use that as the table
  // time step, unless it is even smaller than a hundred times smaller
  // in which case just use a hundred times smaller as the table time step
  if (dt < dt_tmp) {
    dt_tmp = 0.0025
    if (dt > 0.025e-2) {
      dt_tmp = dt
  t_vec.indgen(0, tp, dt_tmp) // make time step the interp table step
                              // In ordinary cases this works well
                              // however this could be optimized for
                              // smaller tables if need be -
                              // especially since the simulation would
                              // likely run similarly at a lower
                              // resolution

  num_of_points = t_vec.size()
  g_vec = new Vector(num_of_points)

  for i = 0, num_of_points - 1 {
    g_vec.x[i] = factor*(-exp(-t_vec.x[i]/tau1)+exp(-t_vec.x[i]/tau2))
  // finally load the table and set the POINT PROCESS with new tau1, tau2

  dummy_synapse.table_t_table(&t_vec.x[0], num_of_points, &g_vec.x[0])
  dummy_synapse.tau1 = tau1
  dummy_synapse.tau2 = tau2
set_taus(tau1, tau2)

// note that since this is the inverse of conductance as a function of
// time as conductance monotonically rises from 0 to the peak of conductance
// the conductance becomes the independent variable.
  // graph the conductance (inverse function)
proc graph_g() {
  g_g = new Graph()
  t_vec.line(g_g, g_vec)
  // g_g.exec_menu("View = plot")
proc graph_new_g() {
  t_vec.line(g_g, g_vec)
  // g_g.exec_menu("View = plot")

xpanel("graph g")
    xbutton("graph conductance","graph_g()")
    xbutton("add new conductance to old graph","graph_new_g()")
    xbutton("load NMDA receptor with above taus","set_taus(tau1, tau2)")

if (graph_cond) {