Computer model of clonazepam`s effect in thalamic slice (Lytton 1997)

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Accession:12631
Demonstration of the effect of a minor pharmacological synaptic change at the network level. Clonazepam, a benzodiazepine, enhances inhibition but is paradoxically useful for certain types of seizures. This simulation shows how inhibition of inhibitory cells (the RE cells) produces this counter-intuitive effect.
Reference:
1 . Lytton WW (1997) Computer model of clonazepam's effect in thalamic slice. Neuroreport 8:3339-43 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Thalamus;
Cell Type(s): Thalamus geniculate nucleus (lateral) principal neuron; Thalamus reticular nucleus cell;
Channel(s): I Na,t; I T low threshold; I K; I CAN;
Gap Junctions:
Receptor(s): GabaA; Gaba;
Gene(s):
Transmitter(s): Gaba;
Simulation Environment: NEURON;
Model Concept(s): Activity Patterns; Bursting; Therapeutics; Epilepsy; Calcium dynamics;
Implementer(s): Lytton, William [billl at neurosim.downstate.edu];
Search NeuronDB for information about:  Thalamus geniculate nucleus (lateral) principal neuron; Thalamus reticular nucleus cell; GabaA; Gaba; I Na,t; I T low threshold; I K; I CAN; Gaba;
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lytton97b
README
AMPA.mod
calciumpump_destexhe.mod *
GABAA.mod
GABAB1.mod
GABALOW.mod
HH_traub.mod *
IAHP_destexhe.mod
ICAN_destexhe.mod
ICAN_voltdep.mod
Ih_old.mod *
IT_wang.mod
IT2_huguenard.mod
NMDA.mod
passiv.mod *
pregen.mod *
presyn.mod *
pulse.mod
rand.mod
bg.inc *
boxes.hoc
ctl.dat
ctlnew.dat
czp.dat
czpnew.dat
declist.hoc *
decvec.hoc *
default.hoc *
disp.hoc
Fig3.gif
Fig4.gif
geom.hoc
grvec.hoc
init.hoc
labels.hoc
local.hoc
mod_func.c
mosinit.hoc
network.hoc
neurrep8
nrnoc.hoc
params.hoc
presyn.inc *
queue.inc *
run.hoc
show.hoc
simctrl.hoc *
sns.inc *
snsarr.inc
snscode.hoc
snsgr.hoc
snshead.inc *
synq.inc *
xtmp
                            
// $Id: show.hoc,v 1.1 2002/07/27 04:08:58 billl Exp $

proc showfiles () { local cnt

  read_vfile(1,$s1)
  panobj.color=1
  panobj.line=1
  rmtray(1)
  read_vfile(2,$s2)
  panobj.color=2
  panobj.line=7

  disptray(1,9)
  boxerl.object(0).name="All cells: top 2 rows RE cells; rest are TC cells"
  grall(2,0,63,1)

  mktray(1,4,1,500,100)
  boxerl.object(1).name="Fig 4: representative TC cells -- 500 ms"
  grall(1,27,54,1,63,1,9)
  grall(2,27,54,1,63,1,9)
  cnt=panobjl.object(1).glist.count
  for ltr(XO,panobjl.object(1).glist) if (i1>cnt-5) XO.size(0,500,-82,-67)

  mktray(1,3,1,500,100)
  boxerl.object(2).name="Fig 3: representative RE cells -- first 100 ms"
  grall(1,11,13,1,67,1,1)
  grall(2,11,13,1,67,1,1)
  setrange(1,-3)
  cnt=panobjl.object(1).glist.count
  for ltr(XO,panobjl.object(1).glist) if (i1>cnt-4) XO.size(0,100,-90,30)
}

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