Feedforward heteroassociative network with HH dynamics (Lytton 1998)

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Using the original McCulloch-Pitts notion of simple on and off spike coding in lieu of rate coding, an Anderson-Kohonen artificial neural network (ANN) associative memory model was ported to a neuronal network with Hodgkin-Huxley dynamics.
1 . Lytton WW (1998) Adapting a feedforward heteroassociative network to Hodgkin-Huxley dynamics. J Comput Neurosci 5:353-64 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s):
Channel(s): I Na,t; I K;
Gap Junctions:
Receptor(s): GabaA; AMPA;
Simulation Environment: NEURON;
Model Concept(s): Pattern Recognition; Temporal Pattern Generation; Spatio-temporal Activity Patterns; Simplified Models; Attractor Neural Network;
Implementer(s): Lytton, William [billl at neurosim.downstate.edu];
Search NeuronDB for information about:  GabaA; AMPA; I Na,t; I K;
matrix.mod *
naf.mod *
passiv.mod *
pregen.mod *
sinstim.mod *
bg.inc *
boxes.hoc *
declist.hoc *
decvec.hoc *
default.hoc *
loadr.hoc *
local.hoc *
mosinit.hoc *
nrnoc.hoc *
simctrl.hoc *
sns.inc *
snshead.inc *
// $Id: spkts.hoc,v 1.40 2000/04/12 20:26:53 billl Exp $

// ancillary programs for handling vectors

// load_proc("decvec")

//* transfer a file into a list of strings
// usage 'f2slist(list,file)'
proc f2slist() { local i
  if (! tmpfile.ropen($s2)) { print "Can't open ",$s2
    return }
  while (tmpfile.gets(temp_string_)>0) {
    sfunc.head(temp_string_,"\n",temp_string_) // chop
    tmpobj = new String(temp_string_)

//* spkts(attrnum[,flag,min,max]) graph spikes from the vectors

thresh = 0    // threshold for deciding which are spikes
burstlen = 1.4  // duration of spike or burst, don't accept another till after this time

proc spkts_call() {}// callback function stub
proc spkts () { local cnt, attrnum, ii, pstep, jj, num, time0, flag, min, max, tst
  vec1.resize(0) // will store times and indices respectively
  if (numarg()==0) { print "spkts(attrnum[,flag,min,max])\n\tflag 0: graph, flag 1: save vec1,vec to veclist, flag 2: save inds (vec1) and times (vec)" // infomercial
    return }
  attrnum = $1
  panobj = panobjl.object(attrnum)
  if (attrnum==0) { cnt=printlist.count() } else { cnt = panobj.llist.count() }
  pstep = panobj.printStep
  if (numarg()>1) { flag = $2 } else { flag = 0 }
  if (numarg()>2) { min = $3 } else { min = 0 }
  if (numarg()>3) { max = $4 } else { max = cnt-1 }
  if (flag==0){
  for ii=min,max {
    if (attrnum==0) { 
      if (panobj.printStep==-2) tvec = printlist.object(ii).tvec
      if (panobj.printStep==-1) tvec = panobj.tvec
    } else {
      rv_readvec(attrnum,ii,vrtmp)  // pick up vector from file
      if (panobj.printStep==-2) tvec = panobj.tvec
    spkts_call()  // place to reset thresh or do other manipulations
    // should replace indvwhere,truncvec with vecst.mod:vec.xing()
    ind.indvwhere(vrtmp,">",thresh) // this has redund points from spk or burst
    if (panobj.printStep<0) { // a tvec
      ind.index(tvec,ind)  // convert indices to times
    } else {
      ind.mul(pstep)  // convert indices to times
    truncvec(ind,vec0,burstlen) // get rid of times too close to previous one
    if (flag==0) { printf("%d:%d ",ii,ind.size()) }  // how many times this cell spiked
    vec0.resize(ind.size)  // scratch vector stores index
    vec1.copy(vec0,vec1.size())  // add same index for each spk to end of vec1
    vec.copy(ind,vec.size())     // add the times for this to end of vec
  if (panobj.printStep<0) { // a tvec
    tst = vec.max
  } else {
    tst = pstep*vrtmp.size()         // calc the tst

  if (flag==1) { savevec(vec1) savevec(vec) }
  if (flag<=0) {
    vec1.mark(graphItem,vec,"O",panobj.line)  // graph all the times

//** pull the vec and vec1 files from spkts apart and put in alloc'ed vectors
func parse_spkts () { local p,q
  p=allocvecs(vec1.max+2) q=p+1
  for (ii=0;ii<=vec1.max;ii+=1) {
    q += 1
  return p+1

proc line_spkts () { local ii,min,max,xmax,skip
  skip = $1
  if (numarg()==3) { min=$2 max=$3 } else {
    min = int(graphItem.size(3)+1) max = int(graphItem.size(4)) }
  xmax = graphItem.size(2)
  for (ii=min;ii<max;ii=ii+skip) {

burst_maxfreq = 30
calc_ave = 0

//** calcspkts(flag,index)
// run after spkts() so vec contains the times, vec1 contains the
// indices
proc calcspkts () { local ii,jj,flag,index,p1,p2,mn,mx
  p1 = allocvecs(2,1000) p2 = p1+1
  if (numarg()==0) {
    print "To be run after spkts(). \
Assumes times in vec, indices in vec1. \
calcspkts(flag,min,max)\nflags:\t1\tspk times\n\t2\tspk freq \
\t3\tburst times\n\t4\tburst freq\nset calc_ave to get averages for freqs"
  // vec contains the times, vec1 contains the indices
  flag = $1
  mn = $2
  if (numarg()==3) { mx=$3 } else { mx=mn }
  for index=mn,mx {
    if (flag==1) {  
      printf("SPKS for #%d: ",index)
      for jj=0,mso[p1].size()-1 {printf("%g ",mso[p1].x[jj])}
    } else if (flag==2) {  
      printf("FREQ for #%d: ",index)
      for jj=0,mso[p1].size()-2 { 
        pushvec(mso[p2],1000./(mso[p1].x[jj+1]-mso[p1].x[jj])) }
      if (calc_ave) { print mso[p2].mean } else { vlk(mso[p2]) }
    } else if (flag==3) {  
      printf("BTIMES for #%d: ",index)
      burst_time = mso[p1].x[0]
      for jj=1,mso[p1].size()-1 {
        if (1000./(mso[p1].x[jj]-burst_time) < burst_maxfreq) {
          printf("%g ",burst_time)
          burst_time = mso[p1].x[jj]
    } else if (flag==4) {  
      printf("BFREQ for #%d: ",index)
      burst_time = mso[p1].x[0]
      for jj=1,mso[p1].size()-1 {
        // should keep track of spike times in case of very long bursts
        if (1000./(mso[p1].x[jj]-burst_time) < burst_maxfreq) {
          burst_time = mso[p1].x[jj]
      if (calc_ave) { print mso[p2].mean } else { mso[p2].printf }

func rvwheres () { local ii
  if ($1!=0) {
    for ii=0,panobjl.object($1).llist.count()-1 {
      if (sfunc.substr(panobjl.object($1).llist.object(ii).name,$s2)==0) {
        return ii }
    errorMsg("String not found in rvwheres.")
  return -2

supind = 0
//* spkhist assume spk times in vec 
// allows superimposing of graphs
// spkhist(bin_size)
proc spkhist () { local ii,jj,min,max,diff
  if (numarg()==0) { print "spkhist(bin_size)" return }
  if (numarg()==3) { min=$2 max=$3 } else { min=0 max=tstop }
  diff = max-min
  vec0.fill(0) vec1.fill(0)
  for (ii=min;ii<int(diff/$1);ii=ii+1) {
    vec0.x[jj+0] = ii*$1
    vec0.x[jj+1] = ii*$1
    vec0.x[jj+2] = (ii+1)*$1
    vec0.x[jj+3] = (ii+1)*$1
    vec1.x[jj+0] = 0
    vec1.x[jj+1] = vrtmp.x[ii]
    vec1.x[jj+2] = vrtmp.x[ii]
    vec1.x[jj+3] = 0
  if (panobj.super==0) {
  } else { graphItem = panobjl.object(panobj.remote).glist.object(supind) 
    supind = supind+1 }
  sprint(temp_string_,"Hist: %s %d",panobj.filename,$1)

//** truncvec (vec1,vec2,margin) 
// truncate a thresholded time vector so that only one time is given for each spike
// vec1 has thresholded times, vec2 is for scratch use, margin is duration of a spike
proc truncvec () { local ii, num, marg, time0
  marg = $3
  num=0 time0=-10
  for ii=0,$o1.size()-1 {
    if ($o1.x[ii] > time0+marg) { 
      $o2.x[ii] = $o1.x[ii]
      time0 = $o1.x[ii]

//** redundout(vec) eliminates sequential redundent entries
// destructive
proc redundout () { local x,ii
  x = $o1.x[0]
  for ii=1,$o1.size-1 {
    if ($o1.x[ii]==x) { $o1.x[ii]=-1e20 } else { x=$o1.x[ii] }

//** redundkeep(vec) keeps sequential redundent entries
// destructive
proc redundkeep () { local x,ii
  x = $o1.x[0]
  for ii=1,$o1.size-1 {
    if ($o1.x[ii]!=x) { $o1.x[ii-1]=-1e20 x=$o1.x[ii] }

//** after running spkall can see which cells are responsible for spks
// assumes spk times in vec, spk identities in vec1
// uses ind and vec0
proc whichspked () { local ii
  ind.indvwhere(vec,"()",$1,$2) // a range
  vec0 = vec1.ind(ind)
  ind = vec.ind(ind)
  for ii=0,ind.size()-1 { printf("%d %g\n",vec0.x[ii],ind.x[ii]) }

// firebtwn(ind,time,min,max) list of cells that fire between times min and max
proc firebtwn () { local ii,p1,p2,p3
  p1 = allocvecs(3) p2=p1+1 p3=p2+1
  mso[p1].index($o1,mso[p3]) // indices
  mso[p2].index($o2,mso[p3]) // times
  printf("%d hits\n",mso[p3].size)
  for vtr2(&x,&y,mso[p1],mso[p2]) {
      printf("%4d::%6.2f ",x,y)
      if ((ii+1)%5==0) { print "" }
  print ""
//  dealloc(p2) // to save the indexes

// elimind(ind,time,min,max) take out cells with nums between min,max
// destructive
proc elimind () { local ii,p1
  p1 = allocvecs(1)
  vecelim($o1,mso[p1]) vecelim($o2,mso[p1])

// index/time graph
// tigr(ind,vec,size,marker)
proc tigr () { local sz
  if (numarg()==0) { print "tigr(Yvec,Xvec,marker size,marker type)" 
    print "Marker types: \"o\",t,s,O,T,S,+ (circ, tri, square; CAP is filled)"
    return }
  if (numarg()>2) { sz=$3 } else { sz=6 }
  if (numarg()>3) { temp_string_=$s4 } else { temp_string_="O" }

Lytton WW (1998) Adapting a feedforward heteroassociative network to Hodgkin-Huxley dynamics. J Comput Neurosci 5:353-64[PubMed]

References and models cited by this paper

References and models that cite this paper

Anderson JA (1972) A simple neural network generating an interactive memory Math Biosci 14:197-220

Anderson JA, Rosenfeld E (1988) Neurocomputing: Foundations Of Research

Barkai E, Bergman RE, Horwitz G, Hasselmo ME (1994) Modulation of associative memory function in a biophysical simulation of rat piriform cortex. J Neurophysiol 72:659-77 [Journal] [PubMed]

Borg-graham L (1991) Modelling the non-linear conductances of excitable membranes Cellular Neurobiology: A Practical Approach, Wheal CHADJ&HV, ed. pp.247

Buckmaster PS, Schwartzkroin PA (1995) Interneurons and inhibition in the dentate gyrus of the rat in vivo. J Neurosci 15:774-89 [PubMed]

Bullock TH (1993) Integrative systems research on the brain: resurgence and new opportunities. Annu Rev Neurosci 16:1-15 [PubMed]

Bullock TH (1997) Signals and signs in the nervous system: the dynamic anatomy of electrical activity is probably information-rich. Proc Natl Acad Sci U S A 94:1-6 [PubMed]

Celebrini S, Thorpe S, Trotter Y, Imbert M (2001) Dynamics of orientation coding in area V1 of the awake primate. Vis Neurosci 10:811-25 [PubMed]

Damasio AR (1990) Category-related recognition defects as a clue to the neural substrates of knowledge. Trends Neurosci 13:95-8 [PubMed]

deCharms RC, Merzenich MM (1996) Primary cortical representation of sounds by the coordination of action-potential timing. Nature 381:610-3 [PubMed]

Destexhe A, Mainen Z, Sejnowski TJ (1994) An efficient method for computing synaptic conductances based on a kinetic model of receptor binding Neural Comput 6:14-18 [Journal]

   Efficient Method for Computing Synaptic Conductance (Destexhe et al 1994) [Model]
   Kinetic synaptic models applicable to building networks (Destexhe et al 1998) [Model]
   Application of a common kinetic formalism for synaptic models (Destexhe et al 1994) [Model]

Destexhe A, Mainen ZF, Sejnowski TJ (1994) Synthesis of models for excitable membranes, synaptic transmission and neuromodulation using a common kinetic formalism. J Comput Neurosci 1:195-230 [Journal] [PubMed]

   Application of a common kinetic formalism for synaptic models (Destexhe et al 1994) [Model]
   Kinetic synaptic models applicable to building networks (Destexhe et al 1998) [Model]

Essen VANDC, Maunsell JHR (1983) Hierarchical organization and functional streams in the visual cortex Trends Neurosci 6:370-375

Fransen E, Lansner A (1995) Low spiking rates in a population of mutually exciting pyramidal cells Network:Comput Neural Systems 6:271-288

Fricke RA, Prince DA (1984) Electrophysiology of dentate gyrus granule cells. J Neurophysiol 51:195-209 [Journal] [PubMed]

Fukai T (1996) Competition in the temporal domain among neural activities phase-locked to subthreshold oscillations. Biol Cybern 75:453-61 [PubMed]

Gardner-Medwin AR (1976) The recall of events through the learning of associations between their parts. Proc R Soc Lond B Biol Sci 194:375-402 [PubMed]

Gray CM (1994) Synchronous oscillations in neuronal systems: mechanisms and functions. J Comput Neurosci 1:11-38 [Journal] [PubMed]

Gray CM, Konig P, Engel AK, Singer W (1989) Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties. Nature 338:334-7 [PubMed]

Gray CM, Singer W (1989) Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex. Proc Natl Acad Sci U S A 86:1698-702 [PubMed]

Hines M (1993) NEURON--a program for simulation of nerve equations. Neural Systems: Analysis And Modeling, Eeckman F, ed. pp.127

Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci U S A 79:2554-8 [PubMed]

Hopfield JJ (1984) Neurons with graded response have collective computational properties like those of two-state neurons. Proc Natl Acad Sci U S A 81:3088-92 [PubMed]

Hopfield JJ (1995) Pattern recognition computation using action potential timing for stimulus representation. Nature 376:33-6 [PubMed]

Kapur A, Lytton WW, Ketchum KL, Haberly LB (1997) Regulation of the NMDA component of EPSPs by different components of postsynaptic GABAergic inhibition: computer simulation analysis in piriform cortex. J Neurophysiol 78:2546-59 [Journal] [PubMed]

Kapur A, Pearce RA, Lytton WW, Haberly LB (1997) GABAA-mediated IPSCs in piriform cortex have fast and slow components with different properties and locations on pyramidal cells. J Neurophysiol 78:2531-45 [Journal] [PubMed]

Kohonen T (1972) Correlation matrix memories. IEEE Trans. on Computers 21:353-359

Lansner A, Fransen E (1992) Modelling hebbian cell assemblies comprised of cortical neurons Network 3:105-119

Lansner A, Fransen E (1995) Improving the realism of attractor models by using cortical columns as functional units The Neurobiology Of Computation: Proceedings Of The 3rd Annual Computation And Neural Systems Conference, Bower J, ed.

Lisman JE, Idiart MA (1995) Storage of 7 +/- 2 short-term memories in oscillatory subcycles. Science 267:1512-5 [PubMed]

Lytton WW (1997) Brain organization: from molecules to parallel processing Contemporary Behavioral Neurology, Trimble M:Cummings J, ed. pp.5

Lytton WW, Destexhe A, Sejnowski TJ (1996) Control of slow oscillations in the thalamocortical neuron: a computer model. Neuroscience 70:673-84 [PubMed]

Lytton WW, Hellman KM, Sutula TP (1998) Computer models of hippocampal circuit changes of the kindling model of epilepsy. Artif Intell Med 13:81-97 [PubMed]

Lytton WW, Sejnowski TJ (1991) Simulations of cortical pyramidal neurons synchronized by inhibitory interneurons. J Neurophysiol 66:1059-79 [Journal] [PubMed]

Maass W (1997) Fast sigmoidal networks via spiking neurons. Neural Comput 9:279-304 [PubMed]

Marr D (1971) Simple memory: a theory for archicortex. Philos Trans R Soc Lond B Biol Sci 262:23-81 [PubMed]

Marr D (1982) Vision: A Computational Investigation into the Human Representation and Processing of Visual Information

McCulloch WS, Pitts W (1990) A logical calculus of the ideas immanent in nervous activity. 1943. Bull Math Biol 52:99-115; discussion 73-97 [PubMed]

Menschik ED, Finkel LH (1998) Neuromodulatory control of hippocampal function: towards a model of Alzheimer's disease. Artif Intell Med 13:99-121 [PubMed]

Murthy VN, Fetz EE (1992) Coherent 25- to 35-Hz oscillations in the sensorimotor cortex of awake behaving monkeys. Proc Natl Acad Sci U S A 89:5670-4 [PubMed]

O'Reilly RC, McClelland JL (1994) Hippocampal conjunctive encoding, storage, and recall: avoiding a trade-off. Hippocampus 4:661-82 [PubMed]

Parodi O, Combe P, Ducom JC (1996) Temporal coding in vision: coding by the spike arrival times leads to oscillations in the case of moving targets. Biol Cybern 74:497-509 [PubMed]

Pribram KH (1969) The neurophysiology of remembering. Sci Am 220:73-86 [PubMed]

Scharfman HE (1994) Evidence from simultaneous intracellular recordings in rat hippocampal slices that area CA3 pyramidal cells innervate dentate hilar mossy cells. J Neurophysiol 72:2167-80 [Journal] [PubMed]

Stemmler M (1996) A single spike suffices - the simplest form of stochastic resonance in model neurons Network-computation In Neural Systems 7:687-716

Teyler TJ, DiScenna P (1986) The hippocampal memory indexing theory. Behav Neurosci 100:147-54 [PubMed]

Thorpe S, Fize D, Marlot C (1996) Speed of processing in the human visual system. Nature 381:520-2 [PubMed]

Tovee MJ (1994) Neuronal processing. How fast is the speed of thought? Curr Biol 4:1125-7 [PubMed]

Tovee MJ, Rolls ET, Treves A, Bellis RP (1993) Information encoding and the responses of single neurons in the primate temporal visual cortex. J Neurophysiol 70:640-54 [Journal] [PubMed]

Treves A, Rolls ET (1994) Computational analysis of the role of the hippocampus in memory. Hippocampus 4:374-91 [PubMed]

Willshaw DJ, Buneman OP, Longuet-Higgins HC (1969) Non-holographic associative memory. Nature 222:960-2 [PubMed]

Hines ML, Carnevale NT (2003) Personal Communication of NEURON bibliography

Lytton WW, Hellman KM, Sutula TP (1998) Computer models of hippocampal circuit changes of the kindling model of epilepsy. Artif Intell Med 13:81-97 [PubMed]

Lytton WW, Lipton P (1999) Can the hippocampus tell time? The temporo-septal engram shift model. Neuroreport 10:2301-6 [Journal] [PubMed]

   Hippocampus temporo-septal engram shift model (Lytton 1999) [Model]

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]

(55 refs)