Motor cortex microcircuit simulation based on brain activity mapping (Chadderdon et al. 2014)

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Accession:146949
"... We developed a computational model based primarily on a unified set of brain activity mapping studies of mouse M1. The simulation consisted of 775 spiking neurons of 10 cell types with detailed population-to-population connectivity. Static analysis of connectivity with graph-theoretic tools revealed that the corticostriatal population showed strong centrality, suggesting that would provide a network hub. ... By demonstrating the effectiveness of combined static and dynamic analysis, our results show how static brain maps can be related to the results of brain activity mapping."
Reference:
1 . Chadderdon GL, Mohan A, Suter BA, Neymotin SA, Kerr CC, Francis JT, Shepherd GM, Lytton WW (2014) Motor cortex microcircuit simulation based on brain activity mapping. Neural Comput 26:1239-62 [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 M1 pyramidal intratelencephalic L2-5 cell; Neocortex fast spiking (FS) interneuron; 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;
Model Concept(s): Oscillations; Laminar Connectivity;
Implementer(s): Lytton, William [billl at neurosim.downstate.edu]; Neymotin, Sam [samn at neurosim.downstate.edu]; Shepherd, Gordon MG [g-shepherd at northwestern.edu]; Chadderdon, George [gchadder3 at gmail.com]; Kerr, Cliff [cliffk at neurosim.downstate.edu];
Search NeuronDB for information about:  Neocortex V1 pyramidal corticothalamic L6 cell; Neocortex M1 pyramidal intratelencephalic L2-5 cell; GabaA; AMPA; NMDA; Gaba; Glutamate;
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README
infot.mod *
intf6.mod *
intfsw.mod *
matrix.mod
misc.mod *
nstim.mod *
staley.mod *
stats.mod *
vecst.mod *
boxes.hoc *
col.hoc
declist.hoc *
decmat.hoc *
decnqs.hoc *
decvec.hoc *
default.hoc *
drline.hoc *
filtutils.hoc *
gcelldata.hoc
gmgs102.nqs
grvec.hoc *
infot.hoc *
init.hoc
intfsw.hoc *
labels.hoc *
load.py
local.hoc *
main.hoc
misc.h *
miscfuncs.py
network.hoc
neuroplot.py *
nload.hoc
nqs.hoc *
nqsnet.hoc
nrnoc.hoc *
params.hoc
run.hoc
samutils.hoc *
saveoutput.hoc
saveweights.hoc
setup.hoc *
simctrl.hoc *
spkts.hoc *
staley.hoc *
stats.hoc *
stdgui.hoc *
syncode.hoc *
updown.hoc *
wdmaps2.nqs
xgetargs.hoc *
                            
// $Id: staley.hoc,v 1.23 2010/01/15 21:07:35 samn Exp $ 

print "Loading staley.hoc..."

{install_staley()}

//* trace analysis
//** outputnqs = getsznq(inputvector)
// gets an nqs with time-bounds of seizures detected with staley code in staley.mod
obfunc getsznq () { local i,ns localobj nqsz,vtmp
  nqsz=new NQS("id","numspikest","startidx","endidx","startms","endms","startmin","endmin")
  vtmp=$o1
  if ((ns=vtmp.getseizures(nqsz.v[1],nqsz.v[2],nqsz.v[3]))<1) return nil // "no seizures found"
  if(ns>1) nqsz.v[0].indgen(0,nqsz.v[1].size-1,1) else nqsz.v[0].append(0)
  for i=2,3 {
    nqsz.v[i+2].copy(nqsz.v[i])
    nqsz.v[i+2].mul(1e3/samprate_staley)
    nqsz.v[i+4].copy(nqsz.v[i+2])
    nqsz.v[i+4].div(60000)
  }
  return nqsz
}

//** getallchsznq(inputnqs with coumns as EEG time-series,vmask - mask of which columns to find seizures for)
// get seizures on all channels of inputnqs($o1) specified in vids($o2)
// in output nqs, "id" has the seizure identifier within a given channel, and "gid"
// has the global seizure identifier across all channel
// vmask.x(i) == 1 iff should use channel i (inputnqs.v[i]) to find seizures, otherwise channel i is skipped
obfunc getallchsznq () { local a,idx,bsz,esz,ii,jj,lend,nid,gid,mii,mxi,changed,done,i,j,st1,st2,et1,et2,tt\
                        localobj nqin,nqout,vmask,nqtmp,vch,vfrag,vb,ve,vdur,v1,v2,v3,\
                        vsmin,vemin,vnsmin,vnemin,vid,vbe,vbi
  nqin=$o1 vmask=$o2
  if (vmask==nil) {vmask=getvmask(nqin) $o2=vmask}
  nqout=new NQS("id","numspikest","startidx","endidx","startms","endms","startmin","endmin","ch")
  nqout.info.set("chans",vch=new Vector(0))  nqout.info.set("frags",vfrag=new Vector(0))
  nqout.info.set("beg",vb=new Vector(0))     nqout.info.set("end",ve=new Vector(0))
  nqout.info.set("dur",vdur=new Vector(0))
  nqout.info.set("begidx",vbi=new Vector(0)) nqout.info.set("endidx",vbe=new Vector(0))
  a=allocvecs(v1,v2,v3,vsmin,vemin,vid,vnsmin,vnemin)
  for idx=0,vmask.size-1 if(vmask.x(idx)!=0) {
    // printf("looking for seizures on column %d\n",idx)
    if((nqtmp=getsznq(nqin.v[idx]))!=nil) {
      nqtmp.resize("ch") nqtmp.pad() nqtmp.v[nqtmp.m-1].fill(idx)
      nqout.append(nqtmp) nqsdel(nqtmp)
    }
  }  
  {nqout.sort("endmin") nqout.sort("startmin")}
  {vid.resize(nqout.v.size) vsmin.copy(nqout.getcol("startmin")) vemin.copy(nqout.getcol("endmin"))}
  for i=0,vsmin.size-1 { //find start/end of seizures overlapping across channels
    {st1=vsmin.x(i) et1=vemin.x(i)}
    for j=0,vsmin.size-1 if(i!=j) {
      {st2=vsmin.x(j) et2=vemin.x(j)}
      if(st2>=st1 && st2<=et1) {
        {st1=MIN(st1,st2)      et1=MAX(et1,et2)}
        {vsmin.x(i)=vsmin.x(j)=st1 vemin.x(i)=vemin.x(j)=et1}
      } else if(st1>=st2 && st1<=et2) {
        {st1=MIN(st1,st2)      et1=MAX(et1,et2)}
        {vsmin.x(i)=vsmin.x(j)=st1 vemin.x(i)=vemin.x(j)=et1}
      }
    }
  }
  {vrsz(0,vnsmin,vnemin) nid=tt=0} //set global ids for seizures
  if(vsmin.size>0) {tt=vemin.x(0)-vsmin.x(0) vnsmin.append(vsmin.x(0)) vnemin.append(vemin.x(0))}
  if(vsmin.size>1) {
    for i=0,vsmin.size-2 {
      vid.x(i)=nid
      if(vsmin.x(i)!=vsmin.x(i+1) || vemin.x(i)!=vemin.x(i+1)) {
        {nid+=1 vnsmin.append(vsmin.x(i+1)) vnemin.append(vemin.x(i+1))}
      }
    }
    if(vsmin.x(i)!=vsmin.x(i-1) || vemin.x(i)!=vemin.x(i-1)) nid+=1 //check last seizure's id
    vid.x(i)=nid
  } else if(vsmin.size==1) vid.x(0)=0
  {nqout.resize("gid") nqout.getcol("gid").copy(vid)}//set the new gids
  {vb.copy(vnsmin)  ve.copy(vnemin)}
  {vbi.copy(vnsmin) vbe.copy(vnemin) vbe.apply("min2ind") vbi.apply("min2ind")}
  {vdur.copy(ve) vdur.sub(vb)}
  nqout.verbose=0
  for ii=0,nid {
    vfrag.append(nqout.select("gid",ii))
    vch.append(nqout.getcol("ch").uniq())
  }
  nqout.verbose=1 nqout.tog("DB")
  dealloc(a)
  return nqout
}

//** stichallsznq(nqs from geallchsznq)
//get nqs with overlapping(in time) seizures combined
obfunc stitchallsznq () { local c1,c2,i,j,a localobj vs,ve,nqo,nqin
  a=allocvecs(vs,ve)
  nqin=$o1
  nqin.tog("DB")
  nqin.verbose=0
  vrsz(nqin.v.size(),vs,ve)
  c1=nqin.fi("startmin")
  c2=nqin.fi("endmin")
  for i=0,vs.size-1 {
    vs.x(i)=nqin.v[c1].x(i)
    ve.x(i)=nqin.v[c2].x(i)    
  }
  nqo=new NQS("id","startidx","endidx","startms","endms","startmin","endmin","numspikest")
  for i=0,vs.size-1 {
    for j=0,vs.size-1 {
    }
  }
  dealloc(a)
  nqin.verbose=1
  return nqo
}

Chadderdon GL, Mohan A, Suter BA, Neymotin SA, Kerr CC, Francis JT, Shepherd GM, Lytton WW (2014) Motor cortex microcircuit simulation based on brain activity mapping. Neural Comput 26:1239-62[PubMed]

References and models cited by this paper

References and models that cite this paper

Ainsworth M, Lee S, Cunningham MO, Roopun AK, Traub RD, Kopell NJ, Whittington MA (2011) Dual gamma rhythm generators control interlaminar synchrony in auditory cortex. J Neurosci 31:17040-51 [PubMed]

Anderson CT, Sheets PL, Kiritani T, Shepherd GM (2010) Sublayer-specific microcircuits of corticospinal and corticostriatal neurons in motor cortex. Nat Neurosci 13:739-44 [PubMed]

Apicella AJ, Wickersham IR, Seung HS, Shepherd GM (2012) Laminarly orthogonal excitation of fast-spiking and low-threshold-spiking interneurons in mouse motor cortex. J Neurosci 32:7021-33 [PubMed]

Bastos AM, Usrey WM, Adams RA, Mangun GR, Fries P, Friston KJ (2012) Canonical microcircuits for predictive coding. Neuron 76:695-711 [PubMed]

Beltramo R, D'Urso G, Dal Maschio M, Farisello P, Bovetti S, Clovis Y, Lassi G, Tucci V, De P (2013) Layer-specific excitatory circuits differentially control recurrent network dynamics in the neocortex. Nat Neurosci 16:227-34 [PubMed]

Bernhardt BC, Chen Z, He Y, Evans AC, Bernasconi N (2011) Graph-theoretical analysis reveals disrupted small-world organization of cortical thickness correlation networks in temporal lobe epilepsy. Cereb Cortex 21:2147-57 [PubMed]

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

Bureau I, Shepherd GM, Svoboda K (2004) Precise development of functional and anatomical columns in the neocortex. Neuron 42:789-801 [PubMed]

Cardin JA, Carlen M, Meletis K, Knoblich U, Zhang F, Deisseroth K, Tsai LH, Moore CI (2009) Driving fast-spiking cells induces gamma rhythm and controls sensory responses. Nature 459:663-7 [PubMed]

Carnevale NT, Hines ML (2006) The NEURON Book

Chadderdon GL, Neymotin SA, Kerr CC, Lytton WW (2012) Reinforcement learning of targeted movement in a spiking neuronal model of motor cortex. PLoS One 7:e47251-57 [PubMed]

Chen D, Fetz EE (2005) Characteristic membrane potential trajectories in primate sensorimotor cortex neurons recorded in vivo. J Neurophysiol 94:2713-25 [PubMed]

Dantzker JL, Callaway EM (2000) Laminar sources of synaptic input to cortical inhibitory interneurons and pyramidal neurons. Nat Neurosci 3:701-7 [PubMed]

Dembrow NC, Chitwood RA, Johnston D (2010) Projection-specific neuromodulation of medial prefrontal cortex neurons. J Neurosci 30:16922-37 [PubMed]

Douglas RJ, Martin KA (2004) Neuronal circuits of the neocortex. Annu Rev Neurosci 27:419-51 [PubMed]

Freeman LC (1977) A set of measures of centrality based on betweenness Sociometry 40:35-41

Hattox AM, Nelson SB (2007) Layer V neurons in mouse cortex projecting to different targets have distinct physiological properties. J Neurophysiol 98:3330-40 [PubMed]

Hooks BM, Hires SA, Zhang YX, Huber D, Petreanu L, Svoboda K, Shepherd GM (2011) Laminar analysis of excitatory local circuits in vibrissal motor and sensory cortical areas. PLoS Biol 9:e1000572 [Journal] [PubMed]

   Laminar analysis of excitatory circuits in vibrissal motor and sensory cortex (Hooks et al. 2011) [Model]

Hooks BM, Mao T, Gutnisky DA, Yamawaki N, Svoboda K, Shepherd GM (2013) Organization of cortical and thalamic input to pyramidal neurons in mouse motor cortex. J Neurosci 33:748-60 [PubMed]

Isomura Y, Harukuni R, Takekawa T, Aizawa H, Fukai T (2009) Microcircuitry coordination of cortical motor information in self-initiation of voluntary movements. Nat Neurosci 12:1586-93 [PubMed]

Kameda H, Hioki H, Tanaka YH, Tanaka T, Sohn J, Sonomura T, Furuta T, Fujiyama F, Kaneko T (2012) Parvalbumin-producing cortical interneurons receive inhibitory inputs on proximal portions and cortical excitatory inputs on distal dendrites. Eur J Neurosci 35:838-54 [PubMed]

Katzel D, Zemelman BV, Buetfering C, Wölfel M, Miesenböck G (2011) The columnar and laminar organization of inhibitory connections to neocortical excitatory cells. Nat Neurosci 14:100-7 [PubMed]

Kerr C, Van_albada S, Neymotin S, Chadderdon G, Robinson P, Lytton W (2012) Effects of basal ganglia on cortical computation: A hybrid network-field model Soc. Neurosci. Abstracts 301.16

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]

Kiritani T, Wickersham IR, Seung HS, Shepherd GM (2012) Hierarchical connectivity and connection-specific dynamics in the corticospinal-corticostriatal microcircuit in mouse motor cortex. J Neurosci 32:4992-5001 [PubMed]

Lang S, Dercksen VJ, Sakmann B, Oberlaender M (2011) Simulation of signal flow in 3D reconstructions of an anatomically realistic neural network in rat vibrissal cortex. Neural Netw : [PubMed]

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]

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

Lytton WW, Neymotin SA, Hines ML (2008) The virtual slice setup. J Neurosci Methods 171:309-15 [Journal] [PubMed]

   The virtual slice setup (Lytton et al. 2008) [Model]

Lytton WW, Omurtag A (2007) Tonic-clonic transitions in computer simulation. J Clin Neurophysiol 24:175-81 [PubMed]

   Tonic-clonic transitions in a seizure simulation (Lytton and Omurtag 2007) [Model]

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

Markram H (2006) The blue brain project. Nat Rev Neurosci 7:153-60 [Journal] [PubMed]

   [241 reconstructed morphologies on NeuroMorpho.Org]

Miller MN, Okaty BW, Nelson SB (2008) Region-specific spike-frequency acceleration in layer 5 pyramidal neurons mediated by Kv1 subunits. J Neurosci 28:13716-26 [PubMed]

Morgan RJ, Soltesz I (2008) Nonrandom connectivity of the epileptic dentate gyrus predicts a major role for neuronal hubs in seizures. Proc Natl Acad Sci U S A 105:6179-84 [Journal] [PubMed]

   Dentate gyrus (Morgan et al. 2007, 2008, Santhakumar et al. 2005, Dyhrfjeld-Johnsen et al. 2007) [Model]

Morishima M, Kawaguchi Y (2006) Recurrent connection patterns of corticostriatal pyramidal cells in frontal cortex. J Neurosci 26:4394-405 [PubMed]

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, Jacobs KM, Fenton AA, Lytton WW (2011) Synaptic information transfer in computer models of neocortical columns. J Comput Neurosci. 30(1):69-84 [Journal] [PubMed]

   Synaptic information transfer in computer models of neocortical columns (Neymotin et al. 2010) [Model]

Neymotin SA, Lazarewicz MT, Sherif M, Contreras D, Finkel LH, Lytton WW (2011) Ketamine disrupts theta modulation of gamma in a computer model of hippocampus Journal of Neuroscience 31(32):11733-11743 [Journal] [PubMed]

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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]

Oviedo HV, Bureau I, Svoboda K, Zador AM (2010) The functional asymmetry of auditory cortex is reflected in the organization of local cortical circuits. Nat Neurosci 13:1413-20 [PubMed]

Packer AM, Yuste R (2011) Dense, unspecific connectivity of neocortical parvalbumin-positive interneurons: a canonical microcircuit for inhibition? J Neurosci 31:13260-71 [PubMed]

   [37 reconstructed morphologies on NeuroMorpho.Org]

Roopun AK, Kramer MA, Carracedo LM, Kaiser M, Davies CH, Traub RD, Kopell NJ, Whittington MA (2008) Period concatenation underlies interactions between gamma and beta rhythms in neocortex. Front Cell Neurosci 2:1-11 [PubMed]

Sakata S, Harris KD (2009) Laminar structure of spontaneous and sensory-evoked population activity in auditory cortex. Neuron 64:404-18 [PubMed]

Schubert D, Staiger JF, Cho N, Kotter R, Zilles K, Luhmann HJ (2001) Layer-specific intracolumnar and transcolumnar functional connectivity of layer V pyramidal cells in rat barrel cortex. J Neurosci 21:3580-92 [PubMed]

Song W, Kerr CC, Lytton WW, Francis JT (2013) Cortical plasticity induced by spike-triggered microstimulation in primate somatosensory cortex. PLoS One 8:e57453-18 [PubMed]

Stepanyants A, Chklovskii DB (2005) Neurogeometry and potential synaptic connectivity. Trends Neurosci 28:387-94 [PubMed]

Stepanyants A, Hirsch JA, Martinez LM, Kisvarday ZF, Ferecskó AS, Chklovskii DB (2008) Local potential connectivity in cat primary visual cortex. Cereb Cortex 18:13-28 [PubMed]

   [10 reconstructed morphologies on NeuroMorpho.Org]

Suter BA, Migliore M, Shepherd GM (2013) Intrinsic electrophysiology of mouse corticospinal neurons: a class-specific triad of spike-related properties. Cereb Cortex 23:1965-77 [PubMed]

Tanaka YH, Tanaka YR, Fujiyama F, Furuta T, Yanagawa Y, Kaneko T (2011) Local connections of layer 5 GABAergic interneurons to corticospinal neurons. Front Neural Circuits 5:12

Thomson AM, Bannister AP (2003) Interlaminar connections in the neocortex. Cereb Cortex 13:5-14 [PubMed]

Tiesinga P, Sejnowski TJ (2009) Cortical enlightenment: are attentional gamma oscillations driven by ING or PING? Neuron 63:727-32 [PubMed]

van Diessen E, Hanemaaijer JI, Otte WM, Zelmann R, Jacobs J, Jansen FE, Dubeau F, Stam CJ, Go (2013) Are high frequency oscillations associated with altered network topology in partial epilepsy? Neuroimage 82:564-73 [PubMed]

Vierling-Claassen D, Cardin JA, Moore CI, Jones SR (2010) Computational modeling of distinct neocortical oscillations driven by cell-type selective optogenetic drive: separable resonant circuits controlled by low-threshold spiking and fast-spiking interneurons. Front Hum Neurosci 4:198 [Journal] [PubMed]

   Engaging distinct oscillatory neocortical circuits (Vierling-Claassen et al. 2010) [Model]

Wang XJ (2010) Neurophysiological and computational principles of cortical rhythms in cognition. Physiol Rev 90:1195-268 [PubMed]

Wang Y, Markram H, Goodman PH, Berger TK, Ma J, Goldman-Rakic PS (2006) Heterogeneity in the pyramidal network of the medial prefrontal cortex. Nat Neurosci 9:534-42 [PubMed]

   [204 reconstructed morphologies on NeuroMorpho.Org]

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]

Wilke C, Worrell G, He B (2011) Graph analysis of epileptogenic networks in human partial epilepsy. Epilepsia 52:84-93 [PubMed]

Wilson HR, Cowan JD (1972) Excitatory and inhibitory interactions in localized populations of model neurons. Biophys J 12:1-24 [Journal] [PubMed]

   Excitatory and inhibitory interactions in populations of model neurons (Wilson and Cowan 1972) [Model]

Yu J, Anderson CT, Kiritani T, Sheets PL, Wokosin DL, Wood L, Shepherd GM (2008) Local-Circuit Phenotypes of Layer 5 Neurons in Motor-Frontal Cortex of YFP-H Mice. Front Neural Circuits 2:6-405 [PubMed]

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]

Dura-Bernal S, Neymotin SA, Kerr CC, Sivagnanam S, Majumdar A, Francis JT, Lytton WW (2017) Evolutionary algorithm optimization of biological learning parameters in a biomimetic neuroprosthesis. IBM Journal of Research and Development (Computational Neuroscience special issue) 61(2/3):6:1-6:14 [Journal]

   Motor system model with reinforcement learning drives virtual arm (Dura-Bernal et al 2017) [Model]

Neymotin SA, Dura-Bernal S, Lakatos P, Sanger TD, Lytton WW (2016) Multitarget Multiscale Simulation for Pharmacological Treatment of Dystonia in Motor Cortex. Front Pharmacol 7:157 [Journal] [PubMed]

   Multitarget pharmacology for Dystonia in M1 (Neymotin et al 2016) [Model]

Neymotin SA, McDougal RA, Bulanova AS, Zeki M, Lakatos P, Terman D, Hines ML, Lytton WW (2016) Calcium regulation of HCN channels supports persistent activity in a multiscale model of neocortex Neuroscience 316:344-366 [Journal] [PubMed]

   Ca+/HCN channel-dependent persistent activity in multiscale model of neocortex (Neymotin et al 2016) [Model]

(64 refs)