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

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Accession:154096
"... 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. ... "
Reference:
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 V1 L6 pyramidal corticothalamic GLU cell; Neocortex V1 L2/6 pyramidal intratelencephalic 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;
Channel(s):
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA;
Gene(s):
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 downstate.edu]; Neymotin, Sam [Samuel.Neymotin at nki.rfmh.org]; Rowan, Mark [m.s.rowan at cs.bham.ac.uk];
Search NeuronDB for information about:  Neocortex V1 L6 pyramidal corticothalamic GLU cell; Neocortex V1 L2/6 pyramidal intratelencephalic GLU cell; Neocortex V1 interneuron basket PV GABA cell; GabaA; AMPA; NMDA; Gaba; Glutamate;
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RowanEtAl2014
batchscripts
mod
README
alz.hoc
alzinfo.m
autotune.hoc *
basestdp.hoc *
batch.hoc *
batch2.hoc *
batchcommon
checkirreg.hoc *
clusterrun.sh
col.dot *
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 *
flexinput.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 *
mosinit.hoc
network.hoc *
nload.hoc *
nqpplug.hoc *
nqs.hoc *
nqsnet.hoc *
nrnoc.hoc *
params.hoc
plot.py
plotavg.py
plotbatch.sh
plotbatchcluster.sh
plotdeletions.py
plotntes.py
powchgtest.hoc *
pyhoc.py
python.hoc *
pywrap.hoc *
ratlfp.dat *
redE2.hoc *
run.hoc
runsim.sh
setup.hoc *
shufmua.hoc *
sim.hoc
simctrl.hoc *
spkts.hoc *
stats.hoc *
syncode.hoc *
vsampenplug.hoc *
writedata.hoc
xgetargs.hoc *
                            
// $Id: makepopspikenq.hoc,v 1.6 2010/10/11 14:16:47 samn Exp $ 


// uses batch data to make nqs with population spike info

{colW=colH=3 mytstop=1e3}

strdef strrcs
strrcs="nqsnet.hoc,65,network.hoc,125,params.hoc,112,run.hoc,53,nload.hoc,182"
rcsopen(strrcs) // load sim from RCS

mytstop=htmax=tstop=20e3

rcsopen("load.hoc",87)

objref nqspk
nqspk=new NQS("sidx","SIMTYP","DISCONCOL","col","spks","binsz","avgE","spkth")
objref vspkth,vbinsz
{vspkth=new Vector() vspkth.append(0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5)}
vbinsz=new Vector()
vbinsz.append(10,15,20,25,30,35,40,45,50)
objref nqtmp
nqtmp=new NQS(1)
nqtmp.s[0].s="E"
nqtmp.verbose=0
proc myrspks () { local i,j,k,l,Espks,th,spkth
  for i=0,nqbatch.v.size-1 {
    print "sidx " , i
    {myloadone(i) SIMTYP=nqbatch.get("SIMTY",i).x DISCONCOL=nqbatch.get("DISCONCOL",i).x}
    for vtr(&binsz,vbinsz) { 
      print "sidx " , i , " binsz " , binsz
      initAllMyNQs()
      nqtmp.v.resize(nqCTY.v[E2].size)
      for vtr(&spkth,vspkth) { th=int(spkth*col.ecells)
        for j=0,numcols-1 { nqtmp.v.fill(0)
          for col.ctt(&k) if(!ice(k)) nqtmp.v.add(nqCTY[j].v[k])
          Espks=nqtmp.select("E",">=",th)
          if(Espks>0) print Espks
          nqspk.append(i,SIMTYP,DISCONCOL,j,Espks,binsz,nqtmp.v.mean,spkth)
        }
      }
    }
  }
  nqspk.sv("/u/samn/intfcol/data/10oct10_E_SPKS_D.nqs")
}

//* prit - print stats
proc prit () { local i,bsz,th,a localobj vec
  nqspk.verbose=0 bsz=$1 th=$2 a=allocvecs(vec)
  print "totals"
  for case(&SIMTYP,0,E2,I2,-E2,&i) for DISCONCOL=0,1 {
    if(nqspk.select("SIMTYP",SIMTYP,"DISCONCOL",DISCONCOL,"spkth",th,"binsz",bsz)) {
      print "SIMTYP ",SIMTYP,"DISCONCOL ",DISCONCOL,nqspk.getcol("spks").sum
    }
  }
  print "\nper minute:"
  for case(&SIMTYP,0,E2,I2,-E2,-I2,&i) for DISCONCOL=0,1 {
    if(nqspk.select("SIMTYP",SIMTYP,"DISCONCOL",DISCONCOL,"spkth",th,"binsz",bsz)) {
      vec.resize(0) vec.copy(nqspk.getcol("spks"))
      print "SIMTYP ",SIMTYP,"DISCONCOL ",DISCONCOL,"E:",3*vec.mean,"per minute"
    }
  }
  dealloc(a)
  nqspk.verbose=1
}

// time("myrspks()") // 24.090833 m


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