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Electrostimulation to reduce synaptic scaling driven progression of Alzheimers (Rowan et al. 2014)

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"... As cells die and synapses lose their drive, remaining cells suffer an initial decrease in activity. Neuronal homeostatic synaptic scaling then provides a feedback mechanism to restore activity. ... The scaling mechanism increases the firing rates of remaining cells in the network to compensate for decreases in network activity. However, this effect can itself become a pathology, ... Here, we present a mechanistic explanation of how directed brain stimulation might be expected to slow AD progression based on computational simulations in a 470-neuron biomimetic model of a neocortical column. ... "
1 . Rowan MS, Neymotin SA, Lytton WW (2014) Electrostimulation to reduce synaptic scaling driven progression of Alzheimer's disease. Front Comput Neurosci 8:39 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex L5/6 pyramidal GLU cell; Neocortex L2/3 pyramidal GLU cell; Neocortex V1 interneuron basket PV GABA cell; Neocortex fast spiking (FS) interneuron; Neocortex spiny stellate cell; Neocortex spiking regular (RS) neuron; Neocortex spiking low threshold (LTS) neuron;
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA;
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON; Python;
Model Concept(s): Long-term Synaptic Plasticity; Aging/Alzheimer`s; Deep brain stimulation; Homeostasis;
Implementer(s): Lytton, William [bill.lytton at]; Neymotin, Sam [Samuel.Neymotin at]; Rowan, Mark [m.s.rowan at];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; Neocortex L2/3 pyramidal GLU cell; Neocortex V1 interneuron basket PV GABA cell; GabaA; AMPA; NMDA; Gaba; Glutamate;
autotune.hoc *
basestdp.hoc *
batch.hoc *
batch2.hoc *
checkirreg.hoc * *
col.hoc *
comppowspec.hoc *
condisconcellfig.hoc *
condisconpowfig.hoc *
declist.hoc *
decmat.hoc *
decnqs.hoc *
decvec.hoc *
default.hoc *
drline.hoc *
e2hubsdisconpow.hoc *
e2incconpow.hoc *
filtutils.hoc *
geom.hoc *
graphplug.hoc *
grvec.hoc *
infot.hoc *
init.hoc *
labels.hoc *
load.hoc *
local.hoc *
makepopspikenq.hoc *
matfftpowplug.hoc *
matpmtmplug.hoc *
matpmtmsubpopplug.hoc *
matspecplug.hoc *
network.hoc *
nload.hoc *
nqpplug.hoc *
nqs.hoc *
nqsnet.hoc *
nrnoc.hoc *
powchgtest.hoc *
python.hoc *
pywrap.hoc *
ratlfp.dat *
redE2.hoc *
setup.hoc *
shufmua.hoc *
simctrl.hoc *
spkts.hoc *
stats.hoc *
syncode.hoc *
vsampenplug.hoc *
xgetargs.hoc *
// $Id: graphplug.hoc,v 1.2 2010/09/26 23:02:45 samn Exp $ 

// "plugin" (for batch.hoc) to create/save GraphNQ

//** save graph-theoretic properties of all cells
objref nqg
{nqg.resize("hub") nqg.pad()}
if(SIMTYP>0) { // vhubid[...] only setup for HUB-based sims
  for i=0,numcols-1 { // add id for hubs
    for vtr(&j,vhubid[i]) if(,"col",i,"id",j)) nqg.v[nqg.m-1].x(nqg.ind.x(0))=1
{nqg.resize("freq") nqg.pad()}// frequency
for i=0,numcols-1 {
  {nqsdel(snq[i]) snq[i]=SpikeNQS(vit[i].tvec,vit[i].vec,lcol.o(i))}
  {nqsdel(fnq[i]) fnq[i]=FreqNQS(snq[i],lcol.o(i))}
  for j=0,col[i].allcells-1 { gid=col[i].ce.o(j).gid
    if(fnq[i].select(-1,"gid",gid)) { 
      hz = fnq[i].v[fnq[i].m-1].x(fnq[i].ind.x(0))
      nqg.v[nqg.m-1].x(gid) = hz
  {nqsdel(snq[i]) nqsdel(fnq[i])}
if(FileExists(tstr)) print "saved GraphNQ to ", tstr else print "couldn't save GraphNQ to ", tstr , "!!!!!!"

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