Dendro-dendritic synaptic circuit (Shepherd Brayton 1979)

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A NEURON simulation has been created to model the passive spread of an EPSP from a mitral cell synapse on a granule cell spine. The EPSP was shown to propagate subthreshold through the dendritic shaft into an adjacent spine with significant amplitude (figure 2B).
1 . Shepherd GM, Brayton RK (1979) Computer simulation of a dendrodendritic synaptic circuit for self- and lateral-inhibition in the olfactory bulb. Brain Res 175:377-82 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Dendrite;
Brain Region(s)/Organism:
Cell Type(s):
Gap Junctions:
Simulation Environment: NEURON;
Model Concept(s): Influence of Dendritic Geometry; Olfaction;
Implementer(s): Morse, Tom [Tom.Morse at];
// electrotonic_length.hoc
// measures the electrotonic length of V given synaptic conductances
// either in the spine head or on the dendritic shaft

// General goals
// play conductance waveforms into the "passive" synapse to study
// the electrotonic length of synaptic inhibition under the conditions 
// 1) Synaptic Inhibition (SI) (vs only the leak current)
// 2) SI with a bAP
// 3) SI with Synaptic Excitation (SE)
// 4) SI with both bAP and SE

// Inhibitory conductance section
// 1) SI with only the leak current
// the passive setup
// Assume a rat RS prefrontal cortical pyramidal neuron has a surface
// area of 1500 um^2 (estimated from Degenetais E et al. 2002) and
// an input resistance of 35 MOhms then the conductance in S/cm2 is

// 1500 micron squared =1.5e-5 centimeter squared

// a constant to set the leak current conductance g_pas to throughout
// a pre-frontal cortex pyy cell (Degenetais et al 2002).
g_pas_pfc= 1/(35e6*1.5e-5) // 0.0019 S/cm2

g_pas_gr = 1/4000
forall { g_pas = g_pas_gr  e_pas = -80 Ra=80 cm=1}

// the -65 was the default however we set it here anyway to show that
// this is the place to change it

// for ease of computations use fixed time step 0.025 ms 
objref t_vec
print "tstop=",tstop,", dt=",dt
t_vec=new Vector()
t_vec.indgen(0, tstop, dt) // start, stop, step
print "t_vec.size()=",t_vec.size()

objref g_vec

g_vec=create_trapezoid(8e-3) // pass peak conductance in nS
//g_vec=create_trapezoid(8e-3) // 8 nS (in uS)
objref graph_conductance
graph_conductance = new Graph()
g_vec.line(graph_conductance, t_vec)
graph_conductance.exec_menu("View = plot")
graph_conductance.label(.3,.8,"Inhibitory conductance per time")

// put excitatory, inhibitory synapses everywhere so they can be used if desired

objref excitatory[1000], inhibitory[1000]
// store references for up to 1000 excitatory and inhibitory synapses each
objref esyn_list, isyn_list
esyn_list = new List()
isyn_list = new List()
isyn=0 // indicies for about to be newly created synaptic point processes
print "excitatory synapses"
forall  {
  for (x,0) {
    excitatory[esyn] = new excite(0.5)
    print x,", ", secname(),", ", esyn
print esyn_list.count," created"

for i=0, esyn_list.count()-1 {
 esyn_list.o(i).g=0 // start out with no excitatory conductance however then below
print "inhibitory synapses"
forall  {
  for (x,0) {
    inhibitory[isyn] = new inhib(0.5)
    print x,", ", secname(),", ", isyn
print isyn_list.count," created"

for i=0, isyn_list.count()-1 {
 isyn_list.o(i).g=0 // start out with no excitatory conductance however then below
//[0].head.g_excite(0.5), t_vec)
//, t_vec)

objref g1,g2,g3,g4
{ g1=g_vec.c }
{ g2=g1.c }
{ g3=g1.c }
{ g4=g1.c }

print "g1, g2, g3, g4 are all available for,t_vec)"

// obsolete:
print "paper excitatory synapse's corresponding to "
print "GR compartments ten, three, eight, one are available"

// params file is now stimulating the GR-1 section (Spine[0].head)
load_file("") // granule cell voltage graph