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

 Download zip file   Auto-launch 
Help downloading and running models
"... 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 V1 pyramidal corticothalamic L6 cell; Neocortex V1 pyramidal intratelencephalic L2-5 cell; Neocortex V1 interneuron basket PV 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 [billl at neurosim.downstate.edu]; Neymotin, Sam [samn at neurosim.downstate.edu]; Rowan, Mark [m.s.rowan at cs.bham.ac.uk];
Search NeuronDB for information about:  Neocortex V1 pyramidal corticothalamic L6 cell; Neocortex V1 pyramidal intratelencephalic L2-5 cell; Neocortex V1 interneuron basket PV cell; GabaA; AMPA; NMDA; Gaba; Glutamate;
autotune.hoc *
basestdp.hoc *
batch.hoc *
batch2.hoc *
checkirreg.hoc *
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 *
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: infot.hoc,v 1.43 2009/12/04 01:25:55 samn Exp $ 

print "Loading infot.hoc..."

if(!installed_infot) {install_infot()}

//* tentropsig(v1,v2,nshuf,nbins,twoway,[xpast,ypast,hval])
//significance test of transfer entropy using shuffling
//returns (te - tes) / sds , where te is transfer entropy, tes is transfer entropy
//of shuffled data, sds is std-dev of transfer entrop of shuffled data
//should only accept as significanat values > 4-6
func tentropsig () { local nshuf,i,xp,yp,hv,nbins,te localobj v1,v2,vo
  v1=new Vector() v2=new Vector() vo=new Vector(1)
  v1.copy($o1) v2.copy($o2) nshuf=$3 nbins=$4
  if(numarg()>4) xp=$5 else xp=1
  if(numarg()>5) yp=$6 else yp=2
  if(numarg()>6) hv=$7 else hv=0
  if(1||verbose_infot>2) printf("te=%g,sig=%g\n",te,vo.x(0))
  return vo.x(0)

//** mutinfbshufv(v1,v2,[nshuf,nbins])
//return vector with mutual information from shuffled v1,v2
//used for significance test , i.e. : ((miorig - mishufmean) / mishufstdev) > 2
obfunc mutinfbshufv () { local nshuf,nbins,i localobj v1,v2,ve
  v1=new Vector() v2=new Vector() ve=new Vector()
  v1.copy($o1) v2.copy($o2)
  if(numarg()>2) nshuf=$3 else nshuf=20
  if(numarg()>3) nbins=$4 else nbins=10
  for i=0,nshuf-1 {
    v1.shuffle() v2.shuffle()
  return ve

//** mutinfbsig(v1,v2,[nshuf,nbins])
//get significance of mutual information, should be at least > 2
func mutinfbsig () { local nshuf,nbins,st localobj ve
  if(numarg()>2) nshuf=$3 else nshuf=20
  if(numarg()>3) nbins=$4 else nbins=10
  if(st<=0) st=1
  return ($o1.mutinfb($o2,nbins) - ve.mean) / st

//** tentropspksig(v1,v2,nshuffles)
//get significance of tentropspks using shuffling
//returns (TE - AvgTEShuffle) / StdDevTEShuffle
func tentropspksig () { local nshuf,i,xp,yp,hv,nbins,te,sd localobj v1,v2,ve
  v1=new Vector() v2=new Vector() ve=new Vector()
  v1.copy($o1) v2.copy($o2) nshuf=$3
  for i=0,nshuf-1 {
    v1.shuffle()     ve.append(v1.tentropspks(v2))
  if(verbose_infot>2) printf("te=%g,ve.mean=%g,ve.stdev=%g\n",te,ve.mean,ve.stdev)
  if(verbose_infot>2) ve.printf
  return (te-ve.mean)/sd

//* normte() get normalized transfer entropy using tentropspks in output vector vo
//vo.x(0)=transfer entropy of $o1->$o2
//vo.x(2)=normalized transfer entropy in 0,1 range
//$3==number of shuffles
//$o1,$o2 should both have same size and non-negative values. this func is meant for time-binned spike train data
obfunc normte () { local a localobj ve,vo
  a=allocvecs(ve) vo=new Vector()
  nshuf=$3 vrsz(3+nshuf,vo) 
  if(verbose_infot>2) vo.printf
  if(vo.x(1)<=0 && verbose_infot>0){printf("WARNING H(X2F|X2P)==%g<=0\n",vo.x(1)) vo.x(1)=1 }
  if (nshuf>0) {
    if (ve.mean!=vo.x[2]) printf("normte ERRA\n")
  return vo

//* GetTENQ() get an nqs with useful transfer entropy info
obfunc GetTENQ () { local te01,te10,pf01,pf10 localobj nqte,vo1,vo2
  if(numarg()>3) nqte=$o4
  if(nqte==nil) {
    nqte=new NQS("from","to","TE","NTE","HX2|X2P","prefdir","TEshufavg","TEshufstd","sig")
  } else nqte.clear()
  if(te01>0 || te10>0) {
  } else {
  return nqte

//** prefdte() get preferred direction of transfer entropy
//$o1=vec 1, $o2=vec 2, $3 = # of times to shuffle
func prefdte () { local nshuf,a,te01,te10,pfd localobj v1,v2,vtmp
  v1.copy($o1) v2.copy($o2) nshuf=$3 vtmp.resize(3)
  return pfd

//** mkchist() averages entries in window into disc values and returns in new output vec
//$o1=input vec,$2=win size
obfunc mkchist () { local idx,eidx,wsz localobj vin,vout
  vin=$o1 wsz=$2 vout=new Vector() 
  vout.resize(1+vin.size/wsz) vout.resize(0)
  for(idx=0;idx<=vin.size;idx+=wsz) {
    if(eidx>idx) vout.append( int(vin.mean(idx,eidx)) )
  return vout

//get magnitude of difference in a preferred direction - just abs of diff, but if theyre both neg, return 0
//$1 = nTE_X->Y
//$2 = nTE_Y->X
func prefdmag () { local n1,n2,s
  n1=$1 n2=$2
  if(n1>0 && n2<=0) return n1-n2 //n1 is relatively strong
  if(n2>0 && n1<=0) return n2-n1 //n2 is relatively strong
  if(n2<0 && n1<0) return 0      //both are weak
  return abs(n1-n2)              //both are weak positive

//** simple test for nte
for i=0,1 {
  vb[i]=new Vector()
  vs[i]=new Vector()

//mkspktrain(Random,rate,tmax) -- make a spike train with specified rate,tmax
//Random obj must be initialized
obfunc mkspktrain () { local tmax,rate,t,dt,intt localobj rdp,vs
  rdp=$o1 rate=$2 tmax=$3
  t = 0
  vs=new Vector()
  while(t<=tmax) {
    dt = rdp.poisson(intt)
    t += dt
  return vs

//make random spikes with frequency $1, tmax=$2, offset for spikes=$3, alpha=$4 -- ratio of spikes from
//vs[0] that get placed in vs[1] 
//spikes in vs[0] are randomly picked, spikes in vs[1] are same as in vs[0] but shifted forward by $3 offset
//so vs[0] 'drives' vs[1], or can be used to predict it, but vs[1] cant be used to predict vs[0]
proc mkspks () { local tmax,rate,t,dt,intt,off,i,alpha localobj rdp
  rate=$1 tmax=$2 off=$3 
  if(numarg()>3)alpha=$4 else alpha=1
  rdp=new Random()
  for i=0,1 vs[i].resize(0)
  if(alpha < 1.0) {
    for vtr(&t,vs[0]) if(rdp.uniform(0,1) <= alpha) vs[1].append(t+off)
  } else {
//test nTE : nTE of X0 -> X1 should be much higher than nTE of X1 -> X0
//optional $1=offset == offset to shift spikes by, in ms
//optional $2=rate == rate of spikes, in Hz
//optional $3=bin size , in ms
//optional $4=alpha == ratio of spikes of X0 that get placed in X1 with offset
//optional $5=max time, in ms
func testnte () { local a,i,bisv,maxt,alpha,off,rate,binsz,dur  localobj nqt,nqout
  if(numarg()>0)off=$1 else off=10
  if(numarg()>1)rate=$2 else rate=50
  if(numarg()>2)binsz=$3 else binsz=10
  if(numarg()>3)alpha=$4 else alpha=1
  if(numarg()>4)dur=$5 else dur=10000
  bisv=binmin_infot binmin_infot=0
  print "output should be close to:\n\t0 1 0.6707 0.9975 0.6707 0.9407 0.003435 0.001672 399.1"
  print "\t1 0 0.02183 0.03049 0.6685 -0.9407 0.002828 0.001442 13.17"
  for i=0,1 vb[i].hist(vs[i],0,(maxt+binsz-1)/binsz,binsz)
nqout=new NQS("X1","X2")
  return 1

//get kernel smoothed prob distrib in an nqs
//$o1=input vector
//$2=increment in x , smaller values mean finer resolution
//$3=bandwidth - higher means smoother output
// $4=min value in output, $5=max value in output
obfunc khist () { local min,max,inc,h,x,i,s localobj vx,vy,nq,vin
  if(numarg()>1)inc=$2 else inc=0.1
  if(numarg()>2)h=$3 else h=vin.getbandwidth()
  if(numarg()>3)min=$4 else min=vin.min()
  if(numarg()>4)max=$5 else max=vin.max()
  {vx=new Vector() vy=new Vector()}
  for vtr(&x,vx,&i) vy.x(i) = vin.kprob1D(h,x)
  if(s!=0) vy.div(vy.sum)
  nq=new NQS("x","y")
  return nq

Rowan MS, Neymotin SA, Lytton WW (2014) Electrostimulation to reduce synaptic scaling driven progression of Alzheimer's disease. Front Comput Neurosci 8:39[PubMed]

References and models cited by this paper

References and models that cite this paper

Binzegger T, Douglas RJ, Martin KA (2004) A quantitative map of the circuit of cat primary visual cortex. J Neurosci 24:8441-53 [PubMed]

Busche MA, Eichhoff G, Adelsberger H, Abramowski D, Wiederhold KH, Haass C, Staufenbiel M, Ko (2008) Clusters of hyperactive neurons near amyloid plaques in a mouse model of Alzheimer's disease. Science 321:1686-9

Carnevale NT, Hines ML (2006) The NEURON Book

Chandler B, Grossberg S (2012) Joining distributed pattern processing and homeostatic plasticity in recurrent on-center off-surround shunting networks: noise, saturation, short-term memory, synaptic scaling, and BDNF. Neural Netw 25:21-9

Crumiller M, Knight B, Yu Y, Kaplan E (2011) Estimating the amount of information conveyed by a population of neurons. Front Neurosci 5:90-31 [PubMed]

Demuro A, Parker I, Stutzmann GE (2010) Calcium signaling and amyloid toxicity in Alzheimer disease. J Biol Chem 285:12463-8

Frohlich F, Bazhenov M, Sejnowski TJ (2008) Pathological effect of homeostatic synaptic scaling on network dynamics in diseases of the cortex. J Neurosci 28:1709-20 [PubMed]

Gourevitch B, Eggermont JJ (2007) Evaluating information transfer between auditory cortical neurons. J Neurophysiol 97:2533-43 [PubMed]

Hansen N (2012) Action mechanisms of transcranial direct current stimulation in Alzheimer's disease and memory loss. Front Psychiatry 3:48-8

Kerr CC, Neymotin SA, Chadderdon GL, Fietkiewicz CT, Francis JT, Lytton WW (2012) Electrostimulation as a prosthesis for repair of information flow in a computer model of neocortex IEEE Transactions on Neural Systems & Rehabilitation Engineering 20(2):153-60 [Journal] [PubMed]

   Prosthetic electrostimulation for information flow repair in a neocortical simulation (Kerr 2012) [Model]

Lefort S, Tomm C, Floyd Sarria JC, Petersen CC (2009) The excitatory neuronal network of the C2 barrel column in mouse primary somatosensory cortex. Neuron 61:301-16 [PubMed]

Liebetanz D, Koch R, Mayenfels S, Konig F, Paulus W, Nitsche MA (2009) Safety limits of cathodal transcranial direct current stimulation in rats. Clin Neurophysiol 120:1161-7

Lytton WW, Neymotin SA, Kerr CC (2014) Multiscale modeling for clinical translation in neuropsychiatric disease J Comput Surg 1:7

Lytton WW, Omurtag A, Neymotin SA, Hines ML (2008) Just in time connectivity for large spiking networks Neural Comput 20(11):2745-56 [Journal] [PubMed]

   JitCon: Just in time connectivity for large spiking networks (Lytton et al. 2008) [Model]

Lytton WW, Stewart M (2005) A rule-based firing model for neural networks Int J Bioelectromagn 7:47-50

Lytton WW, Stewart M (2006) Rule-based firing for network simulations. Neurocomputing 69:1160-1164

Modolo J, Beuter A (2009) Linking brain dynamics, neural mechanisms, and deep brain stimulation in Parkinson's disease: an integrated perspective. Med Eng Phys 31:615-23

Neymotin S, Kerr C, Francis J, Lytton W (2011) Training oscillatory dynamics with spike-timing-dependent plasticity in a computer model of neocortex Signal Processing in Medicine and Biology Symposium (SPMB), IEEE :1-6

Neymotin SA, Lee H, Park E, Fenton AA, Lytton WW (2011) Emergence of physiological oscillation frequencies in a computer model of neocortex. Front Comput Neurosci 5:19-75 [Journal] [PubMed]

   Emergence of physiological oscillation frequencies in neocortex simulations (Neymotin et al. 2011) [Model]

Palop JJ, Mucke L (2010) Amyloid-beta-induced neuronal dysfunction in Alzheimer's disease: from synapses toward neural networks. Nat Neurosci 13:812-8 [PubMed]

Qiu S, Anderson CT, Levitt P, Shepherd GM (2011) Circuit-specific intracortical hyperconnectivity in mice with deletion of the autism-associated Met receptor tyrosine kinase. J Neurosci 31:5855-64

Rabey JM, Dobronevsky E, Aichenbaum S, Gonen O, Marton RG, Khaigrekht M (2013) Repetitive transcranial magnetic stimulation combined with cognitive training is a safe and effective modality for the treatment of Alzheimer's disease: a randomized, double-blind study. J Neural Transm 120:813-9

Rowan MS,Neymotin SA (2013) Synaptic Scaling Balances Learning in a Spiking Model of Neocortex Adaptive and Natural Computing Algorithms, Tomassini M, Antonioni A, Daolio F, Buesser P, ed. pp.20 [Journal]

   Synaptic scaling balances learning in a spiking model of neocortex (Rowan & Neymotin 2013) [Model]

Rutherford LC, Nelson SB, Turrigiano GG (1998) BDNF has opposite effects on the quantal amplitude of pyramidal neuron and interneuron excitatory synapses. Neuron 21:521-30 [PubMed]

Savioz A, Leuba G, Vallet PG, Walzer C (2009) Contribution of neural networks to Alzheimer disease's progression. Brain Res Bull 80:309-14

Shepherd GM (2013) Corticostriatal connectivity and its role in disease. Nat Rev Neurosci 14:278-91

Small DH (2008) Network dysfunction in Alzheimer's disease: does synaptic scaling drive disease progression? Trends Mol Med 14:103-8 [PubMed]

Smith GS, Laxton AW, Tang-Wai DF, McAndrews MP, Diaconescu AO, Workman CI, Lozano AM (2012) Increased cerebral metabolism after 1 year of deep brain stimulation in Alzheimer disease. Arch Neurol 69:1141-8 [PubMed]

Trasande CA, Ramirez JM (2007) Activity deprivation leads to seizures in hippocampal slice cultures: is epilepsy the consequence of homeostatic plasticity? J Clin Neurophysiol 24:154-64 [PubMed]

Turrigiano G (2011) Too many cooks? Intrinsic and synaptic homeostatic mechanisms in cortical circuit refinement. Annu Rev Neurosci 34:89-103 [PubMed]

Turrigiano GG (2008) The self-tuning neuron: synaptic scaling of excitatory synapses. Cell 135:422-35 [PubMed]

Turrigiano GG, Leslie KR, Desai NS, Rutherford LC, Nelson SB (1998) Activity-dependent scaling of quantal amplitude in neocortical neurons. Nature 391:892-6 [PubMed]

Utz KS, Dimova V, Oppenlander K, Kerkhoff G (2010) Electrified minds: transcranial direct current stimulation (tDCS) and galvanic vestibular stimulation (GVS) as methods of non-invasive brain stimulation in neuropsychology--a review of current data and future implications. Neuropsychologia 48:2789-810

van Rossum MC, Bi GQ, Turrigiano GG (2000) Stable Hebbian learning from spike timing-dependent plasticity. J Neurosci 20:8812-21 [PubMed]

Weiler N, Wood L, Yu J, Solla SA, Shepherd GM (2008) Top-down laminar organization of the excitatory network in motor cortex. Nat Neurosci 11:360-6 [Journal] [PubMed]

   Laminar connectivity matrix simulation (Weiler et al 2008) [Model]

Yu Y, Crumiller M, Knight B, Kaplan E (2010) Estimating the amount of information carried by a neuronal population. Front Comput Neurosci 4:10-810

Dura-Bernal S, Li K, Neymotin SA, Francis JT, Principe JC, Lytton WW (2016) Restoring behavior via inverse neurocontroller in a lesioned cortical spiking model driving a virtual arm. Front. Neurosci. Neuroprosthetics 10:28 [Journal]

   Cortical model with reinforcement learning drives realistic virtual arm (Dura-Bernal et al 2015) [Model]

Neymotin SA, McDougal RA, Sherif MA, Fall CP, Hines ML, Lytton WW (2015) Neuronal calcium wave propagation varies with changes in endoplasmic reticulum parameters: a computer model Neural Computation 27(4):898-924 [Journal] [PubMed]

   Neuronal dendrite calcium wave model (Neymotin et al, 2015) [Model]

(38 refs)