{
 "metadata": {
  "name": "",
  "signature": "sha256:cdfdd01c407b97999606dd9e154f0204e3f491d83416885d18b5e3cce5e14947"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Firstly we import all the require libraries**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import io\n",
      "from IPython.nbformat import current\n",
      "\n",
      "def execute_notebook(nbfile):\n",
      "    \n",
      "    with io.open(nbfile) as f:\n",
      "        nb = current.read(f, 'json')\n",
      "    \n",
      "    ip = get_ipython()\n",
      "    \n",
      "    for cell in nb.worksheets[0].cells:\n",
      "        if cell.cell_type != 'code':\n",
      "            continue\n",
      "        ip.run_cell(cell.input)\n",
      "        \n",
      "execute_notebook(\"common.ipynb\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Sun Dec 21 14:43:04 2014\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/usr/local/lib/python2.7/dist-packages/brian/utils/sparse_patch/__init__.py:39: UserWarning: Couldn't find matching sparse matrix patch for scipy version 0.14.0, but in most cases this shouldn't be a problem.\n",
        "  warnings.warn(\"Couldn't find matching sparse matrix patch for scipy version %s, but in most cases this shouldn't be a problem.\" % scipy.__version__)\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**In the gupta paper the first layer consist of 15 leaky integrate and fire nuerons.The input for those neurons were a constant current in case the pixel was \"on\" or a null current in case the pixel was \"off\".**\n",
      "**If the neuron receives a constant current,it will fire around 3 times for a normal exposing of a character.**\n",
      "\n",
      "**In this implementation, instead of simulating a leaky integrate and fire neurons, we directly use the class SpikeGeneratorGroup, to generate spikes at spicified times. \n",
      "The adavantage of this implementacion is that we have more control over the neurons  we are trying to train ('second layer').\n",
      "The \"dictionary\" class is responsable for generating the array which contains the firing time for the first layer**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "epochs = 100 \n",
      "spikeMiliseconds = 100\n",
      "spikeInterval = spikeMiliseconds * ms\n",
      "spikeIntervalUnformatted = spikeMiliseconds * .001\n",
      "dictionaryLongitude = 4\n",
      "spikesPerChar=3\n",
      "firstLayerSize = 15\n",
      "\n",
      "dictionary = dictionary()\n",
      "spiketimes = dictionary.spikeTimes(dictionaryLongitude, spikeInterval, spikesPerChar, epochs)\n",
      "LIK = SpikeGeneratorGroup(firstLayerSize, spiketimes)\n",
      "spikeMonitor = SpikeMonitor(LIK)\n",
      "\n",
      "run(spikesPerChar * dictionaryLongitude * spikeInterval , report='text')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "100% complete, 0s elapsed, approximately 0s remaining.\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "** We will now plot the images which will be used to train the network**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig=plt.figure(figsize=(12,4))\n",
      "for i in range(0,dictionaryLongitude):\n",
      "    ax = fig.add_subplot(1,dictionaryLongitude,i+1)\n",
      "    ax.imshow(dictionary.dictionary[i][1], interpolation=\"nearest\", cmap = plt.cm.Greys_r)\n",
      "    plt.xticks(np.arange(0,3, 1.0))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ZiSNHjnQ9zliQ3fbJbTtkt32y2zy5HY1OszuSb89aoY8//rjevHlzvXHjxvqVV17pepyB\nDh48WN999931LbfcUk9NTdVvvfVW1yMN9Nlnn9VVVdU7duyoZ2dn69nZ2fqTTz7peqziyW675LY9\nstsu2W2H3Lavy+z6tagAAKST7u1+AABQUgEASEdJBQAgHSUVAIB0lFQAANJRUgEASEdJBQAgHSUV\nAIB0/gvvZ3D4pqCYQAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f3fb16279d0>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Here we plot the spikes of the first layer, the first character is letter A, as you can see, the neuron 0,2,4,10,13 spikes at 100,200,300 miliseconds**\n",
      "\n",
      "**Rembember you can zoon and pam clicking on the icons on the botton-left of the plot**\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**A nicer looking version of the raster_plot for low number of neurons**\n",
      "\n",
      "**As you can check, the black pixels of the images above correspondes to the neurons which fired on the scatter plot below.**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "neuronArray = np.empty(0, dtype=int)\n",
      "firetimeArray = np.empty(0, dtype=double)\n",
      "neuronColor = np.random.rand(firstLayerSize)\n",
      "colors = np.empty(0, dtype=float)\n",
      "\n",
      "fig=plt.figure(figsize=(12,4))\n",
      "for neuron in range(0,15):\n",
      "    for fire in range(0,spikeMonitor.spiketimes[neuron].size):\n",
      "       neuronArray = np.append(neuronArray, neuron)\n",
      "       firetimeArray = np.append(firetimeArray,spikeMonitor.spiketimes[neuron][fire])\n",
      "       colors = np.append(colors,neuronColor[neuron])\n",
      "\n",
      "plt.scatter(firetimeArray,neuronArray, s=100, c=colors)\n",
      "plt.xticks(np.arange(0,max(firetimeArray)+0.1, 0.1))\n",
      "plt.yticks(np.arange(0,firstLayerSize, 1.0))\n",
      "ylabel(\"Neuron number\")\n",
      "xlabel(\"Time(seconds)\")\n",
      "grid(True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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IIaZNmyamTZtWNMqFBslNmrQTbm4fm73JdQK23P+9SvWZCAtroXRmsbKzs0Vg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\nmRBCCCGEkBpokEwIIYQQQkgN/w9kuVWJWMdVnwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f3fb15606d0>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Let's now show how the second layer should look like"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**We will now, build an artificial raster plot to show how an idealy trained network should look like, make sure to zoom on it, to see that only one neuron fire when a given character is presented**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "neuronArray = np.empty(0, dtype=int)\n",
      "firetimeArray = np.empty(0, dtype=double)\n",
      "neuronColor = np.random.rand(4)\n",
      "colors = np.empty(0, dtype=float)\n",
      "\n",
      "#Each neuron learn to represent a random character\n",
      "neuronList=list(range(4))\n",
      "np.random.shuffle(neuronList)\n",
      "\n",
      "\n",
      "neuronIndex = 0\n",
      "time = 0\n",
      "for charNumber in range(0 ,dictionaryLongitude ):\n",
      "    neuronIndex = (neuronIndex + 1)%4\n",
      "    neuron = neuronList[neuronIndex]\n",
      "    for charRepetetion in range(0,spikesPerChar):\n",
      "         time = time + spikeInterval / ms\n",
      "            \n",
      "         neuronArray = np.append(neuronArray, neuron)\n",
      "         firetimeArray = np.append(firetimeArray,time)\n",
      "         colors = np.append(colors,neuronColor[neuron])\n",
      "\n",
      "fig, ax = plt.subplots(subplot_kw=dict(axisbg='#EEEEEE'),figsize=(13,4))\n",
      "points = plt.scatter(firetimeArray * ms,neuronArray, s=100, c=colors)\n",
      "plt.yticks(np.arange(0,4, 1.0))\n",
      "ylabel(\"Neuron number\")\n",
      "xlabel(\"Time(seconds)\")\n",
      "grid(True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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iEYBdrAU0F9mYjXzMRj7mIhuzkY/voREAAAAA/BAzAgAAAICXY0YAAAAAgFNo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\nCACA4U79vCY1NVX9+/eXJD3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       "text": [
        "<matplotlib.figure.Figure at 0x7f3fb1169b10>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "If you want to see a real simulation of the second layer click [here](simulation.ipynb)"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}