Broadening of activity with flow across neural structures (Lytton et al. 2008)

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Accession:116830
"Synfire chains have long been suggested as a substrate for perception and information processing in the nervous system. However, embedding activation chains in a densely connected nervous matrix risks spread of signal that will obscure or obliterate the message. We used computer modeling and physiological measurements in rat hippocampus to assess this problem of activity broadening. We simulated a series of neural modules with feedforward propagation and random connectivity within each module and from one module to the next. ..."
Reference:
1 . Lytton WW, Orman R, Stewart M (2008) Broadening of activity with flow across neural structures. Perception 37:401-7 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Activity Patterns; Temporal Pattern Generation; Spatio-temporal Activity Patterns;
Implementer(s): Lytton, William [billl at neurosim.downstate.edu];
: $Id: stats.mod,v 1.4 2005/08/13 15:37:40 billl Exp $
 
:* COMMENT
COMMENT
randwd   randomly chooses n bits to set to 1
hamming  v.hamming(v1) is hamming distance between 2 vecs
flipbits v.flipbits(scratch,num) flips num rand chosen bits
flipbalbits v.flipbalbits(scratch,num) balanced flipping
vpr      v.vpr prints out vector as 1 (x[i]>0) or 0 (x[i]<=0)
fac      not vec related - returns factorial
logfac   not vec related - returns log factorial
vseed    set some C level randomizer seeds
slope(num) does a linear regression to find the slope, assuming num=timestep of vector
vslope(num) does a linear regression to find the slope, assuming num=timestep of vector
stats(num) does a linear regression, assuming num=timestep of vector
vstats(v2) does a linear regression, using v2 as the x-coords
ENDCOMMENT

NEURON {
  SUFFIX nothing
  GLOBAL  STATS_INSTALLED
}

PARAMETER {
  : BVBASE = -1. : defined in vecst.mod
  STATS_INSTALLED=0
}

ASSIGNED { }

VERBATIM
#include <stdlib.h>
#include <math.h>
#include <limits.h> /* contains MAXLONG */
#include <sys/time.h> 
extern double BVBASE;
extern double* hoc_pgetarg();
extern double hoc_call_func(Symbol*, int narg);
extern FILE* hoc_obj_file_arg(int narg);
extern Object** hoc_objgetarg();
extern void vector_resize();
extern int vector_instance_px();
extern void* vector_arg();
extern double* vector_vec();
extern double hoc_epsilon;
extern void set_seed();
extern int ivoc_list_count(Object*);
extern Object* ivoc_list_item(Object*, int);
extern int list_vector_px2();
int list_vector_px();
int list_vector_resize();

typedef struct BVEC {
 int size;
 int bufsize;
 short *x;
 Object* o;
} bvec;
ENDVERBATIM
 
:* v1.slope(num) does a linear regression to find the slope, assuming num=timestep of vector

VERBATIM
static double slope(void* vv) {
	int i, n;
	double *x, *y;
        double timestep, sigxy, sigx, sigy, sigx2;
	/* how to get the instance data */
	n = vector_instance_px(vv, &y);

        if(ifarg(1)) { 
          timestep = *getarg(1); 
        } else { printf("You must supply a timestep\n"); return 0; }

        sigxy= sigx= sigy= sigx2=0; // initialize these

        x = (double *) malloc(sizeof(double)*n);
        for(i=0; i<n; i++) {
          x[i] = timestep*i;
          sigxy += x[i] * y[i];
          sigx  += x[i];
          sigy  += y[i];
          sigx2 += x[i]*x[i];
        }
        return (n*sigxy - sigx*sigy)/(n*sigx2 - sigx*sigx);
}
ENDVERBATIM
 
:* v1.vslope(v2) does a linear regression, using v2 as the x-coords

VERBATIM
static double vslope(void* vv) {
	int i, n;
	double *x, *y;
        double timestep, sigxy, sigx, sigy, sigx2;
	/* how to get the instance data */
	n = vector_instance_px(vv, &y);

        if(ifarg(1)) {
          if(vector_arg_px(1, &x) != n ) {
            hoc_execerror("Vector size doesn't match.", 0); 
          }
          sigxy= sigx= sigy= sigx2=0; // initialize these

          for(i=0; i<n; i++) {
            sigxy += x[i] * y[i];
            sigx  += x[i];
            sigy  += y[i];
            sigx2 += x[i]*x[i];
          }
        }         
        return (n*sigxy - sigx*sigy)/(n*sigx2 - sigx*sigx);
}
ENDVERBATIM
 
:* v1.stats(num) does a linear regression, assuming num=timestep of vector

VERBATIM
static double stats(void* vv) {
	int i, n;
	double *x, *y;
        double timestep, sigxy, sigx, sigy, sigx2, sigy2;
        double r, m, b;
	/* how to get the instance data */
	n = vector_instance_px(vv, &y);

        if(ifarg(1)) { 
          timestep = *getarg(1); 
        } else { printf("You must supply a timestep\n"); return 0; }

        sigxy= sigx= sigy= sigx2=sigy2= 0; // initialize these

        x = (double *) malloc(sizeof(double)*n);
        for(i=0; i<n; i++) {
          x[i] = timestep*i;
          sigxy += x[i] * y[i];
          sigx  += x[i];
          sigy  += y[i];
          sigx2 += x[i]*x[i];
          sigy2 += y[i]*y[i];
        }
        m = (n*sigxy - sigx*sigy)/(n*sigx2 - sigx*sigx);
        b = (sigy*sigx2 - sigx*sigxy)/(n*sigx2 - sigx*sigx);
        r = (n*sigxy - sigx*sigy)/(sqrt(n*sigx2-sigx*sigx) * sqrt(n*sigy2-sigy*sigy));

        printf("Examined %d data points\n", n);
        printf("slope     = %f\n", m);
        printf("intercept = %f\n", b);
        printf("R         = %f\n", r);
        printf("R-squared = %f\n", r*r);
        return 1;
}
ENDVERBATIM
 
:* v1.vstats(v2) does a linear regression, using v2 as the x-coords

VERBATIM
static double vstats(void* vv) {
	int i, n;
	double *x, *y;
        double timestep, sigxy, sigx, sigy, sigx2, sigy2;
        double r, m, b;
	/* how to get the instance data */
	n = vector_instance_px(vv, &y);

        if(ifarg(1)) {
          if(vector_arg_px(1, &x) != n ) {
            hoc_execerror("Vector size doesn't match.", 0); 
          }
          sigxy= sigx= sigy= sigx2=sigy2=0; // initialize these

          for(i=0; i<n; i++) {
            sigxy += x[i] * y[i];
            sigx  += x[i];
            sigy  += y[i];
            sigx2 += x[i]*x[i];
            sigy2 += y[i]*y[i];
          }
          m = (n*sigxy - sigx*sigy)/(n*sigx2 - sigx*sigx);
          b = (sigy*sigx2 - sigx*sigxy)/(n*sigx2 - sigx*sigx);
          r = (n*sigxy - sigx*sigy)/(sqrt(n*sigx2-sigx*sigx) * sqrt(n*sigy2-sigy*sigy));

          printf("Examined %d data points\n", n);
          printf("slope     = %f\n", m);
          printf("intercept = %f\n", b);
          printf("R         = %f\n", r);
          printf("R-squared = %f\n", r*r);
          return 1;
        } else {
          printf("You must supply an x vector!\n");
          return 0;
        }
}
ENDVERBATIM
 
:* v1.randwd(num[,v2]) will randomly flip num bits from BVBASE to 1
: does v1.fill(BVBASE); optionally fill v2 with the indices
VERBATIM
static double randwd(void* vv) {
	int i, ii, jj, nx, ny, flip, flag;
	double* x, *y;
	/* how to get the instance data */
	nx = vector_instance_px(vv, &x);
        flip = (int) *getarg(1);
        if (ifarg(2)) { /* write a diff vector to z */
          flag = 1; ny = vector_arg_px(2, &y);
          if (ny!=flip) { hoc_execerror("Opt vector must be size for # of flips", 0); }
        } else { flag = 0; }
        if (flip>=nx) { hoc_execerror("# of flips exceeds (or ==) vector size", 0); }
	for (i=0; i < nx; i++) { x[i] = BVBASE; }
	for (i=0,jj=0; i < flip; i++) { /* flip these bits */
	  ii = (int) ((nx+1)*drand48());
	  if (x[ii]==BVBASE) {
	    x[ii] = 1.; 
            if (flag) { y[jj] = ii; jj++; }
	  } else {
	    i--;
	  }
	}
	return flip;
}
ENDVERBATIM
 
:* v1.hamming(v2[,v3]) compares v1 and v2 for matches, v3 gives diff vector
VERBATIM
static double hamming(void* vv) {
	int i, nx, ny, nz, prflag;
	double* x, *y, *z,sum;
	sum = 0.;
	nx = vector_instance_px(vv, &x);
	ny = vector_arg_px(1, &y);
        if (ifarg(2)) { /* write a diff vector to z */
          prflag = 1; nz = vector_arg_px(2, &z);
        } else { prflag = 0; }
	if (nx!=ny || (prflag && nx!=nz)) {
	  hoc_execerror("Vectors must be same size", 0);
	}
	for (i=0; i < nx; ++i) {
	  if (x[i] != y[i]) { sum++; 
            if (prflag) { z[i] = 1.; }
          } else if (prflag) { z[i] = 0.; }
        }
	return sum;
}
ENDVERBATIM

:* v1.flipbits(scratch,num) flips num bits
: uses scratch vector of same size as v1 to make sure doesn't flip same bit twice
VERBATIM
static double flipbits(void* vv) {	
	int i, nx, ny, flip, ii;
	double* x, *y;

	nx = vector_instance_px(vv, &x);
	ny = vector_arg_px(1, &y);
        flip = (int)*getarg(2);
	if (nx != ny) {
	  hoc_execerror("Scratch vector must be same size", 0);
	}
	for (i=0; i<nx; i++) { y[i]=x[i]; } /* copy */
	for (i=0; i < flip; i++) { /* flip these bits */
	  ii = (int) ((nx+1)*drand48());
	  if (x[ii]==y[ii]) { /* hasn't been touched */
	    x[ii]=((x[ii]==1.)?BVBASE:1.);
	  } else {
	    i--; /* do it again */
	  }
	}
	return flip;
}
ENDVERBATIM
 
:* v1.flipbalbits(scratch,num) flips num bits making sure to balance every 1
: flip with a 0 flip to preserve initial power
: uses scratch vector of same size as v1 to make sure doesn't flip same bit twice
VERBATIM
static double flipbalbits(void* vv) {	
	int i, nx, ny, flip, ii, next;
	double* x, *y;

	nx = vector_instance_px(vv, &x);
	ny = vector_arg_px(1, &y);
        flip = (int)*getarg(2);
	if (nx != ny) {
	  hoc_execerror("Scratch vector must be same size", 0);
	}
	for (i=0; i<nx; i++) { y[i]=x[i]; } /* copy */
        next = 1; /* start with 1 */
	for (i=0; i < flip;) { /* flip these bits */
	  ii = (int) ((nx+1)*drand48());
	  if (x[ii]==y[ii] && y[ii]==next) { /* hasn't been touched */
	    next=x[ii]=((x[ii]==1.)?BVBASE:1.);
            i++;
	  }
	}
	return flip;
}
ENDVERBATIM
 
:* v1.vpr() prints out neatly
VERBATIM
static double vpr(void* vv) {
  int i, nx;
  double* x;
  FILE* f;
  nx = vector_instance_px(vv, &x);
  if (ifarg(1)) { 
    f = hoc_obj_file_arg(1);
    for (i=0; i<nx; i++) {
      if (x[i]>BVBASE) { fprintf(f,"%d",1); 
      } else { fprintf(f,"%d",0); }
    }
    fprintf(f,"\n");
  } else {
    for (i=0; i<nx; i++) {
      if (x[i]>BVBASE) { printf("%d",1); 
      } else { printf("%d",0); }
    }
    printf("\n");
  }
  return 1.;
}
ENDVERBATIM

:* v1.bin(targ,invl) place counts for each interval
VERBATIM
static double bin(void* vv) {	
  int i, j, nx, ny, next, maxsz;
  double* x, *y, invl, loc;

  nx = vector_instance_px(vv, &x);
  ny = vector_arg_px(1, &y);
  invl = (int)*getarg(2);

  vv=vector_arg(1);
  maxsz=vector_buffer_size(vv);
  vector_resize(vv, maxsz);
  if (x[nx-1]/invl>(double)(maxsz-1)) {
    printf("Need size %d in target vector (%d)\n",(int)(x[nx-1]/invl+1),maxsz); 
    hoc_execerror("",0); }
  for (j=0; j<maxsz; j++) y[j]=0.;
  for (i=0,j=0,loc=invl; i<nx && j<maxsz; i++) {
    if (x[i]<loc) y[j]++; else {
      while (x[i]>loc) { loc+=invl; j++; }
      y[j]++;
    }
  }
  vector_resize(vv, j);
  return (double)j;
}
ENDVERBATIM

:* PROCEDURE install_stats()
PROCEDURE install_stats () {
  STATS_INSTALLED=1
VERBATIM
  install_vector_method("slope", slope);
  install_vector_method("vslope", vslope);
  install_vector_method("stats", stats);
  install_vector_method("vstats", vstats);
  install_vector_method("randwd", randwd);
  install_vector_method("hamming", hamming);
  install_vector_method("flipbits", flipbits);
  install_vector_method("flipbalbits", flipbalbits);
  install_vector_method("vpr", vpr);
  install_vector_method("bin", bin);
ENDVERBATIM
}

:* fac (n) 
: from numerical recipes p.214
FUNCTION fac (n) {
VERBATIM {
    static int ntop=4;
    static double a[101]={1.,1.,2.,6.,24.};
    static double cof[6]={76.18009173,-86.50532033,24.01409822,
      -1.231739516,0.120858003e-2,-0.536382e-5};
    int j,n;
    n = (int)_ln;
    if (n<0) { hoc_execerror("No negative numbers ", 0); }
    if (n>100) { /* gamma function */
      double x,tmp,ser;
      x = _ln;
      tmp=x+5.5;
      tmp -= (x+0.5)*log(tmp);
      ser=1.0;
      for (j=0;j<=5;j++) {
        x += 1.0;
        ser += cof[j]/x;
      }
      return exp(-tmp+log(2.50662827465*ser));
    } else {
      while (ntop<n) {
        j=ntop++;
        a[ntop]=a[j]*ntop;
      }
    return a[n];
    }
}
ENDVERBATIM
}
 
:* logfac (n)
: from numerical recipes p.214
FUNCTION logfac (n) {
VERBATIM {
    static int ntop=4;
    static double a[101]={1.,1.,2.,6.,24.};
    static double cof[6]={76.18009173,-86.50532033,24.01409822,
      -1.231739516,0.120858003e-2,-0.536382e-5};
    int j,n;
    n = (int)_ln;
    if (n<0) { hoc_execerror("No negative numbers ", 0); }
    if (n>100) { /* gamma function */
      double x,tmp,ser;
      x = _ln;
      tmp=x+5.5;
      tmp -= (x+0.5)*log(tmp);
      ser=1.0;
      for (j=0;j<=5;j++) {
        x += 1.0;
        ser += cof[j]/x;
      }
      return (-tmp+log(2.50662827465*ser));
    } else {
      while (ntop<n) {
        j=ntop++;
        a[ntop]=a[j]*ntop;
      }
    return log(a[n]);
    }
}
ENDVERBATIM
}

: unable to get the drand here to recognize the same fseed used in rand
PROCEDURE vseed (seed) {
  VERBATIM
  srand48((unsigned)_lseed);
  set_seed(_lseed);
  srandom(_lseed);
  ENDVERBATIM
}

Lytton WW, Orman R, Stewart M (2008) Broadening of activity with flow across neural structures. Perception 37:401-7[PubMed]

References and models cited by this paper

References and models that cite this paper

Abeles M (1991) Corticonics: Neural Circuits of the Cerebral Cortex.

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   Networks of spiking neurons: a review of tools and strategies (Brette et al. 2007) [Model]

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