RandomInhibitoryConnectionsEffect.pde

int[][] neurons;
float[] activity;
int testCount = 1000, neuronCount = 100, inhibited = 10;
void setup()
{
  background(255);
  size(805,400);
  frameRate(100);
  neurons = GetNeurons(neuronCount, inhibited);
  activity = new float[neuronCount];
  RunTest(testCount);
}

void draw()
{
}

void keyPressed()
{
  if(key == 'a')
  {
    neurons = GetNeurons(neuronCount, inhibited);
    activity = new float[neuronCount];
    RunTest(testCount);
  }
}

void RunTest(int count)
{
  background(0);
  for(int c = 0; c < count; c++)
  {
    activity = AddScores(ActivateNeurons(), activity);
  }

  activity = Grade(activity,count);

  int rW = floor(width/activity.length);

  for(int a = 0; a < activity.length; a++)
  {
    color col = color(255*activity[a]);
    stroke(col);
    fill(col);
    rect(rW*a,0,rW,height);
    print(activity[a] + " ");
  }
}

int[][] GetNeurons(int count, int inhibited)
{
  int[][] neuroSample = new int[count][inhibited];

  for(int n = 0; n < count; n++)
  {
    for(int i = 0; i < inhibited; i++)
    {
      neuroSample[n][i] = round(random(count-1));
    }
  }

  return neuroSample;
}

float[] AddScores(int[][] activations, float[] scores)
{
  for(int a = 0; a < activations.length; a++)
  {
    if(activations[a][0] >= 0)
    {
      scores[a] += 1.0f;
    }
  }
  return scores;
}

float[] Grade(float[] scores, int totalRuns)
{
  for(int s = 0; s < scores.length; s++)
  {
    scores[s] = scores[s]/totalRuns;
  }

  return scores;
}

int[][] ActivateNeurons()
{
  int[][] activations = neurons;

  for(int n = 0; n < neurons.length; n++)
  {
    int neuron = round(random(neurons.length-1));

    if(activations[neuron][0] != -1)
    {
      for(int i = 0; i < activations[neuron].length; i++)
      {
        activations[neurons[neuron][i]][0] = -1;
      }
    }
  }

  return activations;
}