{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## SYDE 556/750: Simulating Neurobiological Systems\n",
    "\n",
    "Accompanying Readings: Chapter 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_ben": {
     "show_hide_input": false
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAf/Z\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"720\"\n",
       "            height=\"400\"\n",
       "            src=\"https://www.youtube.com/embed/U_Q6Xjz9QHg?loop=1&autoplay=0&playlist=U_Q6Xjz9QHg\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x106028dd8>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('U_Q6Xjz9QHg', width=720, height=400, loop=1, autoplay=0, playlist='U_Q6Xjz9QHg') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Overall Goal\n",
    "<img src=\"files/lecture1/book_cover.png\" width=\"200\" style=\"float:right\"> \n",
    "- Building brains!\n",
    "- Why? \n",
    "    - To figure out how brains work (health applications)\n",
    "    - To apply this knowledge to building systems (AI applications)\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Administration\n",
    "\n",
    "- Course website: [http://compneuro.uwaterloo.ca/courses/syde-750.html](http://compneuro.uwaterloo.ca/courses/syde-750.html)\n",
    "- Contact information\n",
    "  - Chris Eliasmith: <a href=\"mailto:celiasmith@uwaterloo.ca\">celiasmith@uwaterloo.ca</a>\n",
    "  - Course times: Mon & Wed. 9:00a-10:20a (10:30a-11:20p Wed for 750)\n",
    "  - Location: Mon: E5-6004, Wed: E7-5343 \n",
    "  - Office hours: by appointment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Coursework\n",
    "\n",
    " - Four assignments (60%)\n",
    "     - 20%, 20%, 10%, 10%\n",
    "     - About two weeks for each assignment\n",
    "     - Everyone writes their own code, generates their own graphs, writes their own answers\n",
    "  \n",
    " - Final project (40%)\n",
    "     - Make a model of some neural system\n",
    "     - For 556 students, this can be an extension of something seen in class\n",
    "     - For 750 students, this must be more of a research project with more novelty\n",
    "     - [ideas](http://compneuro.uwaterloo.ca/courses/syde-750/syde-556-possible-projects.html)\n",
    "     - Get your idea approved via email before Reading Week (Feb 18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Schedule\n",
    "\n",
    "<table style=\"border: 1px solid black;\" cellspacing=\"10\">\n",
    "<tr><th>Week</th><th>Reading</th><th>Topic</th><th>Assignments</th></tr>\n",
    "\n",
    "<tr><td style=\"padding:0 15px 0 15px;\">Jan 7</td><td style=\"padding:0 15px 0 15px;\">Chpt 1</td><td>Introduction</td><td style=\"padding:0 15px 0 15px;\"></td></tr>\n",
    "\n",
    "<tr><td style=\"padding:0 15px 0 15px;\">Jan 9, 14</td><td style=\"padding:0 15px 0 15px;\">Chpt 2,4</td><td>Neurons, Population Representation</td><td style=\"padding:0 15px 0 15px;\">#1 posted</td></tr>\n",
    "\n",
    "<tr><td style=\"padding:0 15px 0 15px;\">Jan 16, 21</td><td style=\"padding:0 15px 0 15px;\">Chpt 4</td><td>Temporal Representation</td><td style=\"padding:0 15px 0 15px;\"></td></tr>\n",
    "\n",
    "<tr><td style=\"padding:0 15px 0 15px;\">Jan 23, 28, 30</td><td style=\"padding:0 15px 0 15px;\">Chpt 5,6</td><td>Feedforward Transformations</td><td style=\"padding:0 15px 0 15px;\">#1 due (23rd at midnight); #2 posted</td></tr>\n",
    "\n",
    "<tr><td style=\"padding:0 15px 0 15px;\">Feb 4, 6, 11</td><td style=\"padding:0 15px 0 15px;\">Chpt 6,8</td><td>Dynamics</td><td style=\"padding:0 15px 0 15px;\"></td></tr>\n",
    "\n",
    "<tr><td style=\"padding:0 15px 0 15px;\">Feb 13, 25</td><td style=\"padding:0 15px 0 15px;\">Chpt 7</td><td>Analysis of Representations</td><td style=\"padding:0 15px 0 15px;\">#2 due (15th at midnight); #3 posted</td></tr>\n",
    "\n",
    "<tr><td style=\"padding:0 15px 0 15px;\">Feb 18, 20</td><td></td><td>*Reading Week*</td><td></td></tr>\n",
    "\n",
    "<tr><td style=\"padding:0 15px 0 15px;\">Feb 27, Mar 4</td><td style=\"padding:0 15px 0 15px;\">Provided</td><td>Symbols</td><td style=\"padding:0 15px 0 15px;\"></td></tr>\n",
    "\n",
    "<tr><td style=\"padding:0 15px 0 15px;\">Mar 6, 11</td><td style=\"padding:0 15px 0 15px;\">Chpt 8</td><td>Memory</td><td style=\"padding:0 15px 0 15px;\">#3 due (6th at midnight)</td></tr>\n",
    "\n",
    "<tr><td style=\"padding:0 15px 0 15px;\">Mar 13, 18</td><td style=\"padding:0 15px 0 15px;\">Provided</td><td>Action Selection</td><td style=\"padding:0 15px 0 15px;\">#4 due (20th at midnight)</td></tr>\n",
    "\n",
    "<tr><td style=\"padding:0 15px 0 15px;\">Mar 20, 25</td><td style=\"padding:0 15px 0 15px;\">Chpt 9</td><td>Learning</td><td style=\"padding:0 15px 0 15px;\"></td></tr>\n",
    "\n",
    "<tr><td style=\"padding:0 15px 0 15px;\">Mar 27</td><td style=\"padding:0 15px 0 15px;\"></td><td>Conclusion</td><td style=\"padding:0 15px 0 15px;\"></td></tr>\n",
    "\n",
    "<tr><td style=\"padding:0 15px 0 15px;\">Apr 1, Apr 3</td><td style=\"padding:0 15px 0 15px;\"></td><td>Project Presentations</td><td style=\"padding:0 15px 0 15px;\"></td></tr>\n",
    "\n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## To Do:\n",
    "- Get textbook (Eliasmith & Anderson, 2003, Neural Engineering), start reading.\n",
    "- Be able to run [Juypter](http://jupyter.org/) (old: ipython) notebooks\n",
    "    - [Anaconda](http://continuum.io/downloads) is probably simplest\n",
    "- Decide what language you'll do your assignments in (Python highly recommended, need permission for others)\n",
    "- Start thinking about a project... already!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Focus of the Course\n",
    "## Theoretical Neuroscience\n",
    "<img src=\"files/lecture1/neuro_levels.png\" width=\"600\" style=\"float:right\"> \n",
    "- How does the mind work?\n",
    "- Most complex and most interesting system humanity has ever studied\n",
    "    - Why study anything else?\n",
    "- How should we go about studying it?\n",
    "    - What techniques/tools?\n",
    "    - How do we know if we're making progress?\n",
    "    - How do we deal with the complexity?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## A Useful Analogy\n",
    "<img src=\"files/lecture1/physics_analogy.png\" width=\"500\" style=\"float:right\"> \n",
    "- What is Theoretical Neuroscience?\n",
    "- A useful analogy is to theoretical physics\n",
    "    - Similarities    \n",
    "        - Methods are similar\n",
    "        - Goals are similar (quantification)\n",
    "    - Differences\n",
    "        - Central question \"What exists? vs Who are we?\"\n",
    "        - More simulation (because of nonlinearities) in biology    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Neural Modelling\n",
    "\n",
    "- Let's build it <img src=\"files/lecture1/understand.gif\" style=\"float:right\">\n",
    "    - Specify theory in enough detail that this is possible\n",
    "    - Tends to get complex, so need computer simulation\n",
    "- Bring together levels and modeling methods\n",
    "    - Single neuron models (levels of detail; e.g. spikes, spatial structure, various ion channels, etc.)\n",
    "    - Small network models (levels of detail; e.g. spiking neurons, rate neurons, mean fields, etc.)\n",
    "    - Large network/cognitive models (levels of detail; e.g. biophysics, pure computation, anatomy, etc.)\n",
    "    - Ideally allow all levels of detail below any higher level to be included as desired.\n",
    "    - 'Correct' level depends on questions being asked."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Problems with current approaches\n",
    "\n",
    "### Large-scale neural models (e.g. [Human Brain Project](https://www.humanbrainproject.eu/), [Synapse Project](http://www.research.ibm.com/cognitive-computing/neurosynaptic-chips.shtml#fbid=qMgN8YTZy5Y), etc.)\n",
    "- Lack of function or behaviour\n",
    "    - Can't compare to psychological data\n",
    "- Assumes canonical algorithm repeats\n",
    "    - e.g., Measurements from one small part (hippocampus) are valid everywhere\n",
    "    - But, different parts of the brain are very different (connectivity, cell types, inputs/outputs)\n",
    "    \n",
    "- Expects intelligence to 'emerge'\n",
    "    - Unclear what 'emergence' means, how it will work, or what it explains\n",
    "    - Wishful thinking?\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Cognitve models (e.g. [ACT-R](http://act-r.psy.cmu.edu/), [Soar](http://soar.eecs.umich.edu/), etc.)\n",
    "- Disconnected from neuroscience, can't compare to neural data\n",
    "    - Trying to map components of the model to brain areas\n",
    "    - When a component is active, maybe neurons in that area are more active?\n",
    "- No \"bridging laws\"\n",
    "    - Like having rules of chemistry that never mention that it's all built out of atoms and electrons\n",
    "- No constraints on the equations\n",
    "    - Just anything that can be written down\n",
    "    - Many possibilities; hard to figure out what matches human data best\n",
    "- Maybe that's okay\n",
    "    - Do we understand the brain enough to make this connection and constrain theories?\n",
    "    - When understanding a word processor, do we worry about transistors?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# The Brain\n",
    "\n",
    "- 2 kg (2% of body weight)\n",
    "- 20 Watts (25% of power consumption)\n",
    "- Area: 4 sheets of paper\n",
    "- Neurons: 100 billion (150,000 per $mm^2$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
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\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"500\"\n",
       "            height=\"400\"\n",
       "            src=\"https://www.youtube.com/embed/jHxyP-nUhUY?autoplay=0&start=60\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x1060142b0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('jHxyP-nUhUY', width=500, height=400, autoplay=0, start=60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Brain structures\n",
    "- Lots of visually obvious structure\n",
    "- Lots of greek and latin names to remember\n",
    "    - locus coeruleus, thalamus, amygdala, hypothalamus, substantia nigra, etc etc\n",
    "\n",
    "<img src=\"files/lecture1/brain1.jpg\" width=\"300\" style=\"float:left\">\n",
    "\n",
    "<img src=\"files/lecture1/brain2.png\" width=\"300\" float:right>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<H2>A Neuron</H2><p><iframe src=\"https://rawgit.com/celiasmith/syde556/master/lecture1/neuron.html\" width=825 height=475></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "HTML('<H2>A Neuron</H2><p><iframe src=\"https://rawgit.com/celiasmith/syde556/master/lecture1/neuron.html\" width=825 height=475></iframe>')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Neurons in the brain\n",
    "\n",
    "- 100 billion\n",
    "- 100's or 1000's of distinct types (distinguished via anatomy and/or physiology)\n",
    "- Axon length: from $10^{-4}$ to $5$ m\n",
    "- Each neuron: 500-200,000 inputs and outputs\n",
    "    - 72km of axons\n",
    "- Communication: 100's of different neurotransmitters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Neuron communication: Synapses\n",
    "\n",
    "<img src=\"files/lecture1/NeuronStructure.jpg\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## What it really looks like\n",
    "\n",
    "<img src=\"files/lecture1/brainbow2.jpg\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## What it really really looks like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"720\"\n",
       "            height=\"500\"\n",
       "            src=\"https://www.youtube.com/embed/F37kuXObIBU?start=515\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x106028ac8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('F37kuXObIBU', width=720, height=500, start=8*60+35)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Kinds of data from the brain\n",
    "\n",
    "## Lesion studies\n",
    "- What are the effects of damaging different parts of the brain?\n",
    "    - Occipital cortex: blindness (really blindsight)\n",
    "    - Inferior frontal gyrus: can't speak (Broca's area)\n",
    "    - Posterior superior temporal gyrus: can't understand speech (Wernicke's area)\n",
    "    - Fusiform gyrus: can't recognize faces (and other visually complex objects)\n",
    "    - Ventral medial prefrontal cortex: moral judgement??? (Phineas Gage)\n",
    "    - etc, etc, etc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## fMRI\n",
    "\n",
    "- Functional Magnetic Resonance Imaging <img src=\"files/lecture1/fMRI.jpg\" style=\"float:right\">\n",
    "- Measure blood oxygenation levels in the brain\n",
    "     - show the difference between two tasks\n",
    "     - averaged over many trials and patients\n",
    "- Measured while performing tasks\n",
    "     - ~4 second between scans\n",
    "     - some attempts at going faster, but blood vessels don't change much faster than this\n",
    "- Shows where energy is being used in the brain\n",
    "     - equivalent to figuring out how a CPU works by measuring temperature\n",
    "     - a bit more fine-grained than lesion studies\n",
    "- Good spatial resolution, low temporal resolution\n",
    "- [Neurosynth](http://neurosynth.org/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## EEG\n",
    "\n",
    "- Electrical activity at the scalp\n",
    "- Large-scale communication between areas\n",
    "- High time resolution, low spatial resolution\n",
    "\n",
    "<img src=\"files/lecture1/eeg-person.jpg\" width=\"200\" style=\"float:left\"> \n",
    "<img src=\"files/lecture1/eeg-data.jpg\" width=\"200\" style=\"float:left\"> \n",
    "<img src=\"files/lecture1/p300.jpg\" width=\"200\" style=\"float:left\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Single cell recording\n",
    "- Place electrodes (one or many) into the brain, record from it\n",
    "    - not necessarily right at a neuron\n",
    "- Pick up local electrical potentials\n",
    "    - You can hear neural 'spikes'\n",
    "- High temporal resolution only one (or a few) cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
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\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"640\"\n",
       "            height=\"390\"\n",
       "            src=\"https://www.youtube.com/embed/KE952yueVLA\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x105feef28>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('KE952yueVLA', width=640, height=390)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<img src=\"files/lecture1/catgratings.gif\" width=\"400\" style=\"float:left\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Multielectrode recordings\n",
    "- Put 'tetrodes' or multi-electrode arrays into the brain\n",
    "- Post-processing: \n",
    "    - \"Spike sorting\" \n",
    "    - Local field potentials (LFPs)\n",
    "- High temporal resolution, max ~100 cells\n",
    "\n",
    "<img src=\"files/lecture1/lfps.png\" width=\"400\" style=\"float:left\"><img src=\"files/lecture1/multielectrode.jpg\" width=\"300\" style=\"float:right\">\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"640\"\n",
       "            height=\"390\"\n",
       "            src=\"https://www.youtube.com/embed/lfNVv0A8QvI\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x105db0320>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('lfNVv0A8QvI', width=640, height=390)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Calcium Imaging\n",
    "- Use calcium to glow when Ca2+ ions bond\n",
    "    - Happens a lot during neural activity and spike generation\n",
    "- Good spatial and good temporal resolution\n",
    "\n",
    "- E.g. In a fish embryo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"640\"\n",
       "            height=\"360\"\n",
       "            src=\"https://www.youtube.com/embed/DGBy-BGiZIM\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x106028b38>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('DGBy-BGiZIM', width=640, height=360)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- In a stalking fish"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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       "<IPython.lib.display.YouTubeVideo at 0x106028da0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('CpejbZ-XEyM', width=640, height=360)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Optogenetics\n",
    "- Allows stimulation and recording from select parts of the brain\n",
    "    - Just those parts expressing a light sensitive proteins that are stimulated\n",
    "- High spatial and temporal resolution (but local)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"640\"\n",
       "            height=\"390\"\n",
       "            src=\"https://www.youtube.com/embed/v7uRFVR9BPU\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x106028cc0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('v7uRFVR9BPU', width=640, height=390)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# What do we know so far?\n",
    "\n",
    "- Lots of details\n",
    "    - Data: \"The proportion of type A neurons in area X is Y\" \n",
    "    - Conclusion: \"Therefore, the proportion of type A neurons in area X is Y\".\n",
    "- Hard to get a big picture\n",
    "    - No good methods for generalizing from data \n",
    "  \n",
    "- \"Data-rich and theory-poor\" (Churchland & Sejnowski, 1994; still true)\n",
    "    - Need some way to connect these details\n",
    "    - Need unifying theory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Recall: Neural Modeling\n",
    "\n",
    "- What I cannot create, I do not understand   <img src=\"files/lecture1/understand.gif\" style=\"float:right\">\n",
    "- Build a computer simulation\n",
    "    - Do to neuroscience what Newton did to physics\n",
    "    - Too complex to be analytically tractable, so use computer simulation\n",
    "- Can we use this to connect the levels?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Single neuron simulation\n",
    "\n",
    "- Hodgkin & Huxley, 1952\n",
    "\n",
    "<img src=\"files/lecture1/hh-neuron1.png\" width=\"600\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Single neuron simulation\n",
    "\n",
    "- Hodgkin & Huxley, 1952\n",
    "\n",
    "<img src=\"files/lecture1/hh-neuron2.png\" width=\"600\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Single neuron simulation\n",
    "\n",
    "<img src=\"files/lecture1/hh-circuit2.jpg\" width=\"400\" style=\"float:left\">\n",
    "<img src=\"files/lecture1/hh-circuit.png\" width=\"400\" style=\"float:left\">\n",
    "\n",
    "<img src=\"files/lecture1/hh-circuit3.gif\" width=\"400\" style=\"float:left\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Millions of neurons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"600\"\n",
       "            height=\"400\"\n",
       "            src=\"https://www.youtube.com/embed/_UFOSHZ22q4?start=60\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x1060372e8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('_UFOSHZ22q4', width=600, height=400, start=60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Billions of neurons\n",
    "\n",
    "- Simplify the neuron model and you can run more of them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"600\"\n",
       "            height=\"400\"\n",
       "            src=\"https://www.youtube.com/embed/WmChhExovzY\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x105feee80>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('WmChhExovzY', width=600, height=400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# The controversy\n",
    "\n",
    "- What level of detail for the neurons?  How should they be connected?\n",
    "- IBM SyNAPSE project (Dharmendra Modha)\n",
    "    - Billions of neurons, but very simple models\n",
    "    - Randomly connected\n",
    "    - 2009: \"Cat\"-scale brain (1 billion neurons)\n",
    "        - 2012: \"Human\"-scale brain (500 billion neurons; 5x human!)\n",
    "    - Called a [\"hoax and PR stunt\"](http://spectrum.ieee.org/tech-talk/semiconductors/devices/blue-brain-project-leader-angry-about-cat-brain) by:    \n",
    "- Blue Brain (Henry Markram)\n",
    "    - Much more detailed neurons\n",
    "    - Statistically connected (i.e. similar to hippocampus)\n",
    "- How much detail is enough?\n",
    "    - How could we know?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## What actually matters...\n",
    "- Connecting brain models to *behaviour*\n",
    "    - How can we build models that actually do something?\n",
    "    - How should we connect realistic neurons so they work together?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# The Neural Engineering Framework\n",
    "<img src=\"files/lecture1/nef_book.png\" width=\"200\" style=\"border:1px solid black; float:right\">\n",
    "\n",
    "- Our attempt\n",
    "    - Probably wrong, but got to start somewhere\n",
    "- Three principles\n",
    "    - Representation\n",
    "    - Transformation\n",
    "    - Dynamics\n",
    "- Building behaviour out of detailed low-level components"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Representation\n",
    "\n",
    "- How do neurons represent information? (What is the neural code?)\n",
    "- What is the mapping between a value to be stored and the activity of a group of neurons?\n",
    "- Examples:\n",
    "    - Edge detection in retina\n",
    "    - Place cells\n",
    "- Every group of neurons can be thought of as representing something\n",
    "    - Each neuron has some preferred value(s)\n",
    "    - Neurons fire more strongly the closer the value is to that preferred value\n",
    "    - Values are *vectors*\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Transformation\n",
    "\n",
    "- Connections compute functions on those vectors\n",
    "- Activity of one group of neurons causes another group to fire\n",
    "    - One group may represent $x$, connected to another group representing $y$\n",
    "    - Whatever firing pattern we get in $y$ due to $x$ is a function $y = f(x)$\n",
    "- Can find what class of functions are well approximated this way\n",
    "- Puts limits on the algorithms we can implement with neurons"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Dynamics\n",
    "\n",
    "- Recurrent connections (feedback)\n",
    "- Turns out to allow us to compute functions of this form:\n",
    "    - ${dx \\over dt} = f(x, u)$\n",
    "    - $x$ is what the neurons represent, $u$ is the input neurons and $f()$ is the transformation\n",
    "- Great for implementing all of control theory (i.e., dynamical systems)\n",
    "    - Example:\n",
    "        - memory: (${dx \\over dt} = u$)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Examples\n",
    "\n",
    "- This approach gives us a neural compiler\n",
    "- Given a quantitative description of a behaviour (e.g. an algorithm), you can solve for the connections between neurons that will approximate that behaviour\n",
    "    - Works for a wide variety of neuron models\n",
    "    - Number of neurons affects accuracy\n",
    "    - Neuron properties influence timing and computation\n",
    "    - Can make predictions (e.g. rats head direction and path integration)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Vision: character recognition\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"640\"\n",
       "            height=\"390\"\n",
       "            src=\"https://www.youtube.com/embed/2j9rRHChtXk\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x106037588>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('2j9rRHChtXk', width=640, height=390)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Vision: >1000 categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"640\"\n",
       "            height=\"390\"\n",
       "            src=\"https://www.youtube.com/embed/VWUhCzUDZ70\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x106037668>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('VWUhCzUDZ70', width=640, height=390)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Problem solving: Tower of Hanoi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"640\"\n",
       "            height=\"360\"\n",
       "            src=\"https://www.youtube.com/embed/sUvHCs5y0o8\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x106028b70>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('sUvHCs5y0o8', width=640, height=360)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Spaun: digit recognition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"640\"\n",
       "            height=\"360\"\n",
       "            src=\"https://www.youtube.com/embed/f6Ul5TYK5-o\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x106037748>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('f6Ul5TYK5-o', width=640, height=360)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Spaun: copy drawing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"640\"\n",
       "            height=\"390\"\n",
       "            src=\"https://www.youtube.com/embed/WNnMhF7rnYo\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x106037160>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('WNnMhF7rnYo', width=640, height=390)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Spaun: addition by counting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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4N7nHc7qHatu4492eXL5O0db9VIWUGrGNY0NY0NaNwC2REBEVTi+NRUAMcZD5uI4N9KCymnip2F8zwxo4lVk2Ng+DSwOkPAv0C8zUVs1STLK5xPN37LlAaguLryEdTyP0QegfJidWDnn2LTwZp8d64yQtpGPnMjyQPK1KijumRtsjRc+Tf9VqMLkm1cPWg40ssMcrZ5JiZg4PBAJAKssRxE4lSAWa2IeE7Kbknr5LRuEvAFmj0KDiVJNSNDwS1j9HdaCPHSNk1A3rs2gvoWfkptFTu2FmtBc1dY4KmoqdnECyUDMS7QAIOOHumw+paCXbF5s5vLrC3xbFHVNQaWledm3x3NPjHkOpYnndeSmqGNZUQm5DdzusfBR6am2UTn5dSL6cF7WNzpzadRtxZTRi4LFLjgiYC6ncQ8auiducOpVrqg7Q5QbX8pS2nOyx0PA33FXyYfWNkS41IdWTtihBDQLuv+iNpJ4NWPc1vEaq2wuNojzkam9z1qXLlvfJf0KdccyzZPKjHOphExPPh8UBwyulc529pfnBFvGtwXKn6R18bwKiKORvOxaV1qhLDh0tdkgyRutl1BOtvWqqDERM4ZqdzmnquqU8e14ma/S1cm43MPWU+KxysDnxvYDx8YfBTmPbI3MxwcOYXjmRAkmMSRk8tFIp6mpo5QZC5zTx/mHao2pNZ1Ku3q0Ualq2ztbqDmF2uG5ykEgC5NguRlFhZQcTTRknxspNy3Npf0Lo5wa0uO4C5Wywg47MyvD5QLAaN5Krqq2V2Jy0FBTxPkghEsjpXEDXc0W46KSHOhJa+RzTfxSdPyVTNDV0ONVNbBTyVLKqFrSIyLtc29r3O7VBb4TU0+KYdDWRx5RINWn+UjQj1qZsI/JCpsBopsNwmGmldeQXc/KdLk3P6qxzP8o+tBI2EfkhaTU7TE4MYC4jTW1iuJLi3V1wetZgZNI2+0LWX0N7khBLjdnY1w4jiqHF/wDOYPvR/Mr9rQ1oa0WA0CgV1BHNM2qc5+ZhabDdoboLBFy28XlfBNvF5XwQdUXLbxeV8E28XlfBB1RctvF5XwTbxeV8EHVFy28XlfBNvF5XwQdVVdJf8irP+RL/AOJ6sNvF5XwVb0ika/A6wNN7U8p3f/rcgt0REBaSyCKMvNzbgOJ5LdcJnNM0EZcLl18t9TYH97INRTvk8KeV4cf5Y3Fob6t6R07mVGd0hkYG2bn3t7f7KSiDSSNkrcr2gi4Nj1G62WUQYa0NaGtFgBYBcJxIyVs0TA+wIe29iR1fFSEQase17A5pu0i4Wy5QAAPA3B5t+q6oC41Li2BxBI3XI4C+p9S7LCCNlZHLEIXG7jqMxN223/3UpcwyKEOcGsYN5IACpK/pC5t2UEO0N7bV/i/kOKCRjWIyU47nptJXNuXn+Qdq89T4e+plEkpJZvJP8xVhHBNWy901zgSRbKBZoA6lpUYllzR0LM9v/sPi/lzQbVdIwRxZsrWB2vqWGy0MPg7WPTgHXVdJTVNU7NNIXHr4KwosFDWZnAE3v1INnYg06U8TpDwNrBabeskOjhH1NCmZY2aRNDzuvwQQODr7yd5QcA6qG+qcf+kHX1LlWS1E0D4pGtfcaEaFT2xDdcX3gKPNE+2vPmg50D6juUCNobIGgPLuB6h+Sz/8iGXasleJLWznW/V8FFZK+mqxtNGSeCSd3UVeOphNFa2tkHmqwObKZpS4yB1y/wApa4hK5/gNLWRDgDvVvV0drsc31qlljcw7Fx03NIF9Fo8fXvy8lAa4NlABaTcDep7DIx7pLBzd/oWmO01HQ1VI6iqNqx2rySCAdLa+tWdJJC5uXaNN9N6+jm9b4otWO0LXmttQkUtE58NnTeC45m5DYWWopJKRxyGUtJ3ZtF2w+TYTmkdqD4UR5jkrGqqKamaBI7w/JGrl8quS1eFb0reNWVVRQMrIHRytLmP36/FQabC6ygIbDWuyA3yObdWss88ouxuyZz4reGMuZcnVVr5FqxqOmX/lisa9uP7chUSNsbB1tLFdm7Kt8BzSx++xC2ljBHgjwt1lXP7oLmmGN4kZucdLKNrbUxYop1LeoopongxSPjN7+C4gLpBibqeZrcSzSxtb4Lst7E812FdOAO6oWPbxMfD1repgp5qfMHCzhZpC4aVhh08c0B2ZsMxOTiwcApi87hjjKzKHltVBoHD+ZvC/NXlNPtmG4yvbo5qDsiIgw5oc0hwBB3grh3Kz+V72jkDf9VzcIDNL3TlzA6Z+DbDd+a7U2bYNzX42zb7X0v8AlZBzNJb6uQi+/NqgpXO0kkGXk0WuundMGfJto83LMFrtpnjNFC0x8C59ifQLINxTwi1omadS6KO1084zxuETeAcy5Pp1WGmqlbmzMhtpbLmv1+hBKRc4XueCHgB7TY23LogIiICIiAiIgIiICr+kH2exL8LL8hVgq/pB9nsS/Cy/IUFgiIg1c4NaXHcBdcaeJoY2RwBld4Tnda7kAgg7io1NOPqHh2eM5L5TZ3XdBKREQEREBYN7G29YcCWkNNjwK1ieXtOYWe02cBzQc6ZmydJFmLrEPuevf8QVIXGI55pXjdowfl/7+C7ICIsIPL9JsWd3R3DBbK2xkvuJ5ehQoayAPD5zmfawDW6BVsrHPnlmeb55HWPPmV0ggL3i58FBcOkfihEcWZkFvzd/Zdw6ipAY2+G8b2tF7LiJDFAIIT4bxqeQXanpRHHu1QcJK+NrheGXKeJA0U2GqjrCyCJ99MzhuusOg2hAymy6U1FSwTPdPeNwHgEEj026+pBMjpQ0buC3NPm5KvficpLaenbnnAG0c/QNPo5rV0OIuGZ1XIDvs0ABBMlg2Zz3Nhclc42iSLNz4KG6WthaY5XGVjhqbahTqQAsyoK+upQ+NwsDouVDi76al2T4zNLfLHrv9KtallmO61T0UH/ywCOJvogl7SqmdmqMoHJu4KoxNuWS4uCDdejfDa+iqMXgaWFwCtgt63eTzDz2JObNRRubc2eP7rvhDJHtzjMQN+5YEDXUD47eF4/rKscEjDGvbbS44r7Fsn5UxDLXdInX8puIuy0kU7SQ+Ihw110U6goWOD3v8Jxde5ULFLCjyDfyVjDiVLDTxsdINrlGZgFyDbqXwp7a21ZHsmXYBb0KrpaqaR0jWZTroCN6nyVrptGwSOaeJsP3UQRsa52dskRJvci49atjtXqU713DpKajLdjW5uS1pqgSUUszpmMljfl2J3k8vzUlkDxZ2fMCOI3qoxXC2SSGWJwZONWADVWiKW/DM6/t8+tppaYuttpG9mrh6Co0TWR1Nr+A/TfuKhQDFrNEhiJHHmrOEsbLG6rjzMANxa9jwNuKllxxXqdrYctomImVZjFI+OQSNzBvMcFikxGeilZLmMsQ0dffbkrwCnqKWUAAROP8MHgLDhw1uqltGA8taMzb2Wdu3t6qGVk0TJYzdjxcFbrzNFUy4TUiKS5pXu1ufEJ4helQCAd4BXGocCWxF2UOuXegcF23alRQ2WeQSnK1hHgjj6Sg6E07o9mQ0stbLl0WWyxMYGh2gFtVrsHeUE2DvKCDoJo/K+CbaPylz2DvKC0ykG10GZXsa/bMecwAzN4EdqkqDMXCF1gXEiwA4qawhzGkagjRBsiLR8jYy3ObZjlHpQboiICIiAiIgKv6QfZ7EvwsvyFWCr+kH2exL8LL8hQWCIiAuNNbZuHEPdf/ALiuyjyQPzmSCTZud4wLbtP5c0EhFxpZDJFZ5/is0eORXRzg21+Jsg2RFyqJDFA97RdwHgjmeAQdVFe2V9S9kTwxuUZnWueO5O7WHwWxTGTyNmR8Tp8V1gY5jSZCDI85nW3ehBvGxsbAxgs0blsiIOc80dPC+aVwaxguSV5KsxyfEnFjD3NS7jr4R9PYpvSutY6nFDES55cC+3DqVRQQNkqWROsGsGZ19EG9RCHQtlDCyMHJGz9ytW+DYM8ZTqudtcGU1JqGuuZANOWi3OHCmp3SW8K1hfmg50cZe7M8kuVsyO40B1Uakhs1o4jRWWXLCXW3BBiCJrbuedOF1tUzsgppJ7ghjSVqxwezqUKvo3yMcGE5X2u3hoUG+EUmzpw9/hSPOZzjxJVk5zWkA8VpCzIwDkLJOLsKDZ0TH7wubKYRuJZoDwUbbvh8U3HEHgujcQYdLElB3kZdtrKuoqN7Kt73Ns3h+a7S4mxjiHAgXtc7vWuffWBpA2rBfcMwVIx2np5MxCc5g0VbicJkgIAHJdRikTgPCB/MLo+qgeLXH/cF78d6/Rt5wwCIPa5rddN+5daFoiOrW68ir19HFI+2axPALR9AIhcG/FaPnj11Ln1hUVswLsoIzN1F+K5wPbTgkkZzqSTvU5lOJKxwc29rLtWUMczQHZ224sU8XpM/iStl9J1LlSYnDL4AttOV1MmmY+OxGpG7eVSjB4jNnjmna5p3hw7FaQmamkD2Ozuylvhi/wCnoVMuOndGS3lRGSOeHGKqkpZhGHB0EgIbfXKeFupdqKEOkL3m7idSVFkDi0sdlLr3uNNVtT4hHE/JKckvG40PoKlOKfp3j8ib9xtaSU4G4KO+PQ3upUU7JGi7muB4hcpQGgk7hvUZiYnTZWtZjcQoqaHWRzifGcLjjqssqZqaTwXbRvEPF/is1UrGPc1ote5I0UVrH1AJZaw52V64NxuXU2iva3NXDiELmObkfxaf1urLDKvNh4DrukiOzI4k8P2XnKTDqyonL4Q3+GQXXNvyVph4La4xlxaJ23bzDm69qjesROoexO1u98jgYpIwwyNIaWuuL29AXWJ7ZImubut6upatikMjXyva7L4oa2wvz3rhBTAjaAAOeSXH81w9TFghcdgeagVE9Q19UGbMOib/AA2OBvJ4N735X0/JBagc1h9mtLst7cANVV0c9TPPEXMEbCXhzXMNxlIG+/WrZBo0McA8HQ63WKf/APHj+6Fxs+Qvha3LEHHM++8b7D1qUgyuM4Dnwgi4Lzp/0ldlyl+sg++flKACYiGuN2HRrjw6iuqwQHAgi4O8Fc2kxENebsPiuPDqKDqiIgIiICr+kH2exL8LL8hVgq/pB9nsS/Cy/IUFgiIgIiIOM1OyVwfdzHjQPYbFcjQteP40ssvLM7cedhxUtEEXbVEIyyU75rbnx219IJFlljZZpGyTN2bG6tjvc35nsUhZQYRZRBq57WeM4NvzKjYnVGjw+aceM0Wb6ToFlxjZUSGcDwgMpcLi1t3rVVidQNnFRtFg5+ctP8rb6Dq9CCshpdnH3TPeR7iMrb7yVwnp5e7SZTlbI3c3iBwVnWPJroWAWDWZrDrKk18THUYkdYPYQWk+ooONHaliysY025Det5ZpKmdsLo9mG+Fe+9bUkZNm77KVW0ueJk0Y/iRagD+YcQg2hiyWvuW8ldBGcheHHyW6n1KJcVbQ5r9DuXWKjjjJ2bQCepBxoZi3aRkENa6zb77cFPEgO9QKhuxkEttdxB4hSYXtlaC3cUEpr223rSofZgsVtl3I5mYa6r2B5ufFTFO9gaTY2KrKnFp2ytdEXRtI0uN6t6/AJZqiSSKVrGvNySNQtRgAs0PbtbDe7WyvjnH+6HExztWux58uH9ySmLLnzF3E63/VVMpM85kjm0G4DWy9MejEcjy4tDeoN/uo0nRV7XnZF46wP7rfiyYadJ6tM9s0uGOdCwjEBqBw/upLmRtaXCvY4gXsuUuHYgyJmSJz3NFjkdlJ/VV0cLGSmOoM8L72s5o0/O6yza2+cn+Q9r7/ALl7h8s8tWxz72OnoCvXgFeaoauOnnyTSWIOjrEA+tXbKyNzbhwI53WbJaLTwrDYRCOZzgPG3rWskayFzjbcuM2IRtOjrnkoFdUiaPIDq425qZMRPadh0GenY5wsXDNr1qW+lBBBtY7xbesU5AYAuu0B4rqLTCVsFJ+lTJgMbnEteW9Vtyw3BbG5Id6QFYzVTIm3J1UdlRPMfBAYw7rjUqnz3/lSKxDMGHtgN2gD0LFaQxllLbmjju9xPpUCf+K1zhw/VeVmbWjb15rEqjZuNxfTqU/DaeYYP3Ychje7Twtd9v1UDEI3ufJ6ef8AZTsCjyULI3k6uLmi+m/kvp5Y1ijUsnk3itOY26wVdRTTXY90cb9H2F/z1U+KxrKUR6ua+/PS2pXCqDGRuaGm9t5UroyYpIJHEXnY7K4k38Hhb/fBfLyd9OvH3NY2vFFlZNDmfA5pYTmcwtueu2qlIptTVj2vaHMILTuIQODr24GxXF9HE5xcC9hO/I8tB9S5vpXReFSEA/zNcdHdd+aCU9wYwuO4C5UdtXdoaIpTLbxSwjX07lrHDUSOHdBa1gN8rTfN6SpaDSFhZGA43cdSesrosLKAuUv1kH3z8pXVcpfrIPvn5Sg6rBAcCCLg7wVlcaucU1LLMRfZsLrc7C6DIJiIa83YdGuPDqK6rzeCdKqPFKk0Ujg2V19nm3PHk+lXwJiIa83YdGuPDqKDsiwsoCr+kH2exL8LL8hVgq/pB9nsS/Cy/IUFgiIgIiICIiAiIgIiIMLzMjQcTqpHEWbIbkncvTLx1YdvUywX02rnPtx1Ngg3fUx1WI7SInI1gaSeOpWldI+d8YuckdrNWtDDepLQQAdBfVd6+GejJfLCdmNc7NQgsMP1sde1Wpe1o1K81S4lHEAN3LRSZKmWsBjjLmMd4zrW0QaVFRkri6haXt/nA8W/Up0GIMewOIc3gQRqCsw0cbIAxgsoFTSGKUvzuAeNAeapSlbxynbLWs9rR0rKl0bA/IxzrZra+gXUINdQzy7OTawNda/FptfVQKKu2MxZIA5jtCHK1M7QLMY0DkFDyazgt664aMdJvG4ToKhkrAWneu4twVEXPikzQ6N4s7FIgxEu4dW9Trk2WxzCzIujGgcVClrmBvP0KLDjMQk2crizlcK6a461qX2dru4qM2vhcDleCFznqGhhIfY9Z0C8mYeTOk09Si1dLHO3w2XPOy2hLmSvidK2SzQbgc79ZXUlYsvlfHb1dRG4eZnw+VrtnGQWcncOpbRYZLbRxZpwcVfWizHwm69a6ANIu0g8N6rTPM9w84nqXnO8kma+3fc7uKw7CathJE5dyvpqvS5epYyN5LSPPR4i+mtHUsLSNCeBUkYq19gw53bg0byptTSMmaQ8XCqTAIK1jprZMoawgWAHXZBYQQvmdtJvCdwbwHp5qyjjEYudXLixzYmgAi/psm2IOqDaocS2w3qtZLdksRPhNd6N6munYASVQVlXHHibS1wAIIdqvYnU7Gk8bn5j18/7LrQveJIo2kZYwSf9/motRiTTpG6/ALeCtipIrBwc92riOa1W8j2jUoZMfvxKbXSEtDSblxAUrorTua+rqCLMeQxv5X7VVYW04vimyke5kYaXeDv4L2cEMdPC2KFoaxosAFmtb2lSlPWNOiIi5diIiAiIgIiIC5S/WQffPyldVyl+sg++flKDqoOMsL8Iqw0EnYvsB90qctJGCSNzDoHAhB8TwqjkrqsBhc17buJb4wIBNx6l9J6IY3LitG+mrgHVEJLC7/UGmp69Vxw3o86h6Sd1AAC5LuTgQ4XCrK0uwDp1G+nAihqxd7D4rgeXI3AQe5BMRDXElh0a48Oo9v8As9lqLSRi4uHDcVzBMRDXElh0a48Oo9v+yHZV/SD7PYl+Fl+QqwVf0g+z2JfhZfkKCwREQFCqMUpqd+RznPeN4YL2/Pcu1WX7EtjNnvIaDy/2LqCMMIboxt/SgxU47AyEGBr3yuNg0tIt1qBHjtaH3kha5vFoaR6lIe+KGR0bgbs3+CP98VqaiIcHajyQghT4rXTTZwZGNO5rdAO1H4tiErGsJcyw1LW2J9P9lZwxNrL5G3DDx0XQYa7KPAb60ELC8WnbOIqskxuNs7/5Tw1XoVVuw8tBJYLXubFd6C8ZdDrkAu0HhzCCaTYErxNKTK6WUk+G4vJtzXtiLgjmvJ4LHke6NwuWEtNwg1w9zTXA204KzxqQSRxUnlnM4W4D+6rquBuHYjHKyzYZDu8k9izLUCpxGSQnwW2a30f+0FvRUrWRCzQ1RJHl2JzC/gts34X/AHU+ieC0BQ+43Zair2rSNo45COAPPhuQSGSbMWtcKPXwmogLWPIJ1BB3FbVF4Rme3wfKG5c2zNc27XBTrktincPbePTLqelNHh8k8jY7PExOnX8VZthMUZZK/LKzQtdoT8Vsc2fOx+VwNw4HUFc4wZZbTOzkm5ceJUfL8u2akRMdNWDBOK0zFuG8Yc8aNcRuJXaOFoJGQ3cssYaecsLmhr92q6TyMgYXuBOXi1c+NSuXjfLP5flXx31FdxP2zHTRF13M16yurqdkkltiwsHDLooDMRZK8GN1geBsrOnmu217jgt9sM44SjJNu400dQU4GlOwfd0/RcnUkQdfLb81NEoO9ZLGu13KF8cWdbQBF3PfubKAd7OC37oIHhscPyU3ZMtustHQcQbLHbwtzt17K4zQt1y3XSmqWEkWyj0711dCRvN1CkiEz3MBsG/zAajqWmMdp/VKVMdKfphbNeCLghL6aqp2VVBYxTNcBwdcdq5ifEpTkZC0XO8vFld2tto24Djv61FqoWSa3B6rrelong56mYyO8los0Lu4Mj0a0BBSzRz0YOz8OPySd3oXJlVPK4NZlc5wuAy5P6K0qXB0ZBHD1qvweZsQmjaAA2Q2PPRBHkjrXuLJbMA0t1KVTU7Ba8bDzNhqrGSSOVgDg13I8VHcGs3EEc0HGqwajq23yCKUbnRjf6RxVb3ndA45g1zRqHcCrNtSHuDG+E47mgaqdT4fJM4S1XgN37Ibz6SgidHMPMcslW4WaRkZ18yvQLAAAAAsBwCIMouNTVU9JGZKmaOFnlPcAFGoMVgxJ7u42ySQt0M+WzCeQvv/AC0QT0REBERAREQFyl+sg++flK6rlL9ZB98/KUHVERBiwXif8SqTNQwVbXeE14Zb1nRe3Xl/8Q2h3RzUXtM0/AoLXo7WCvwOlqASczbHNv0Nv2U6o+rH32/MF5n/AA9qjNgexJDhEdDx1c4r01R9WPvt+YIOqr+kH2exL8LL8hVgq/pB9nsS/Cy/IUFgsLKwgjQxicMqJCST4TBewaOGnoUlVT6uammdCyzomHK08R1flcLJxGQcR6kEySla55dmIvvVViMGLtnIw+GldC1oOaaQguPKwCkGonccwD/yuq3HZsSfQiCjLmPmcGOeX5creJF+KCX0TxQYvhz6jZGJ7ZCx7b3FxyP5q9VFQhuFUjKanj2UTBo22/r9KkHEJeYH5ILORuZhGmvNRYo3Q1eYluWQZQBwtr2/BQxXzh5YXZgbkG2voUmkmkqpszhZkROvN3L1XQTlWw4dAauqlu8PMm4Otl0Cs1xnpoahrhI2+ZuUkEgkfkgpqSJlRJKJnbYFxbmPEA2CgYlRR4ZIySN38OQ5chOoPYsxTHC8RdSSnxTZpP8AM3gVwxGbu2uLibRx+DH18ygs6KocGX4LtHHGXE8zmOul/RzVPTS7LwCQBwKlGeQizDa+9xG5BaTVYts2auURlDBJmIDmuvq9htf8tyxTi7RlvbeSeKnxSRR3bcXXkxt7E6Vz6aoiIDLStvvOi3hhl3nK0nlcqdLNEWHwhvXE1MW5pCz2wbh1OW3Gmj6YSACWR5PVYJ3GA0sErwDzsV0FU2y1fVwNP8RxFuSjTFanTqZraPW3SpfhlRFKWioA5HKpcUNexgtPEbbrtKlsxKmOl+F/CC6x1kT/ABbFfS+WbxzCUwhMp695zNqIs3oNj+akMrHRO2dWwxO4H+U+gqS2rjLeQWxkikbrax4HiuBq2pa4aOHpusOqmDTMLrQ0FM83DA0f0OLf0Wow+mFxs7335nE/ugy6oDzbUrvThjGcNTclQDh81Oc1K7O3/TedbdR7VvFO86SRuYRwI4oJzmscNQFAq2GFzHsuLOBUkSjKDw5KFXVTMuS4udEEyKoa9wsbXXKd5cdy40Yb3MRJob3HUtZ5RYjXTigrsSrXwNt+QFuKssKwWKXDoZpXytllGd2Ujju3j0KpMBxDEYoYnW1u9w4N4r14kiiY1gNmtFgANwQQu8cH+vUf947FuzB6YDw3Syfef2WXZ9fCzyj6Aok2NNZ9XCT942QT4KaGnFoY2svvtvK6kgC5NgvPvxPE6i4pYLdbWX+J0UZ+DYtiDr1U+Rp4Pff4DRBbVvSDDqK4dOJHj+WPwj2Lztb0qxGtLo8MpzG0C5cG53Ac+QVxTdFKGKxnL5zyJyj1DtVq6mhjo5IIY2RMcwizRYbkHiujeDxY/LJW4lUy1Do3ZTG4nXiLnl1Be7iiZDG2OJjWMaLNa0WAC8Z/h4Tta8X0sw2/7l7ZAREQEREBERAXKX6yD75+UrquUv1kH3z8pQdURYQV2F4xDiEs8A8CeB5a5nMXtcKl/wARJ2swMR3BJlbp1EOXnaHE3YV0nrKmaN7qRkzhI9ovkuXAH0KP0zxfu+qDIyHMaG3tqDv1HVqgvP8ADI/wK4fyjJb1uXtaj6sffb8wXjv8NYslHUyWsH5bfkXL2NR9WPvt+YIOqr+kH2exL8LL8hVgq/pB9nsS/Cy/IUFgiIgqKthgne59xG45g62g6j+f6rgwxva1zXgg8VeqBU0Di90lOWguNyx26/MckCOCR8bHNk8EjqXGswaKu2PdVpBE8Pbc7jz0WvcNXI0tIjjv/Nmv8LLjIdgSyobkO653H0FBJqYjG5m0k3315KP4Bsc2n++wpFmqXZaduYk6v4N67qeMLpg0NG0A5B51QRKWBlVUWN3MjuXWNtdwCt2MbG0NY0NaNwCxFEyFgZG0NaOAW6AiIgrsVwiDE2DOSyVvivCpvozWsJDamItHig317F6pEHkZMGrIYny1MsMUTBdzsxPwsq6k7un1ZJkbv1XqOlDS7BZS3N4LmkgcRfivN0EzbNJ1Fran4IJ0UFV/xAGm8BbChqL6TPIvq64CzFNmJu6x335qZG8F1gXfl/vegi9wT65ppCCOoLBw0O1zzXvw/vZWjXDeXNHDfw6lJY5oGhAug847C6poAiklv16/suE2H1I1kExDeGb+y9XmzcL/AAWkrW214oPLtpontyljA7+p7gf1WxpQxwyukYRvDXf3Ks56Vma7WkH+kqDLmjabFx9aDk6Z8QIM0ptpZwC60MNXWRSOil8V1m3H++aiFslSTHGNfSdPSrdjXU1NFDTSCPK3U2vm5lBGkNZDCJBIH8HNPgkH91tBiOYjMXDhYqNUPllfHSw3e+Q8d5PEleopqCGChjpXMbI1o1zC9zxKCtjrvBBzEjndYqKw5SWjMQNCpsuC0T75WOjJ4scR8NygzdHHE3hrHDqewH9EESOGeoaXyTbME3DWWuPzXLvVKJC6KfOb7pB+6mswXEmPt3VEW3363t6FLhwaYAbatcbcGMA/W6CrEdY1hLzCxo/mc82/Ratw/EK9waw5Iv8AUIsPyvqV6SKhp4rEMzOH8z9SpKCvwzCIMNhLWEvld48jt5/su0tO4+KQVKRBUSRSxOzGHMOsXCxFiNPGbSUzWf1NCt1xmpIJxaWNp69xQIKqCoH8GVrjy4+pd1Q1eAvB2lHMQ4bg42P5FRIsbrsMl2OIROe3mdHfkeKD1C+a9JpqzvzP3Q57Sx52epADeFl9AjxSiko+6hUMEI3lxtY8j1ryPSHGzjDm4fhsLnh7rF9tX9Q6kHT/AA8jcDXyfynI389V7VVmAYWMJwyOnJBkPhSEcXFWaAiIgIiICIiAuUv1kH3z8pXVcpfrIPvn5Sg6oiwg8NgdAK+p6QQEC7pBa+4+E/Rear+jlVDW7KJhy3sM38h5Hq617XojG+PF8bDwReUEdYLnr0r4InyZ3MBdlyk8wgquitAKDCY2ZSHEG99/jE6+tWtR9WPvt+YLo0BoAG4LnUfVj77fmCDqq/pB9nsS/Cy/IVYKv6QfZ7EvwsvyFBYIiICIiDCEX3rKIMLKIgIiICIiAiIg0ljbLE+N4u17S0+grwT4JaGskpZRbKdORHAr6Aq7F8KixOEAnJKzxH23dXoQebbKC64APHUqXG9rxwB3a6qqngqqGbZ1MZYeBGod6CukckjPCGhvvugu2PLLHMQDzbZSGSv0uT1aqniqRZoc05r6uuT+ymMmjI0yk8i3tQWbPCANyP8AqCy/IW2f8XqAKlrTbZR8tA1burLAC7W6aWFv9+tBILY8pBAseKqJ2NexzSSWXsDzHNdJqth0dJ+59X/tZp6eetJIGzi4udv9SCvoHCEGME2LtbjepLpJJHiGIOfI7c1o/wB6KxrGGlp4qamjEj5Tka08+d1ZUNDHRxWABkPjPtqUEXCMLFHeeY5ql41PkjkFZrKICIiDCyiICIiAiIgIiIC41VLDVwmKeMPYefBdkQeExvA5MMdtW5paMu1sdR6e1eowOiw+GjjnoIgBK2+c6uPUSrGRjZWOZI0OY4WII0IULCqHvdHNTtN4RIXRdQPD13QT0UWojc2Ta3kcy3hNa8i3WLLdsMb2hzXyEHUESu7UHdFy7nZzk9o7tTudnOT2ju1B1Rcu52c5PaO7U7nZzk9o7tQdUXLudnOT2ju1O52c5PaO7UHVcpfrIPvn5Snc7OcntHdq0lpbtBjke2Rpu0l5cL+glBIXOaTZQySWvkaXW9C0p59rdj25JWeMz9x1LXEml+G1TWgkuheAB6CgiYLJR1O3qqJ92yOs9nFjgTcFWi+T9DK6Wk6StiEha2ZzmyNO51gfjdfWEBcqj6sffb8wXVcqj6sffb8wQdVX9IPs9iX4WX5CrBV/SD7PYl+Fl+QoLBERAREQEREBERAREQEREBERAREQaSRRytyyMa9vJwuoEuBUEoNojGSb3Y4hWSIKb6N0fCScf9f9liTo5TlloqidjudwVdIg84/o3IRYVGY773t+y4VOCyUsLHvmzuLsobqR+y9UqrE5Wur6enOuUF5/QfugrqakipP4pZmfwvvJ6lPbPsI80jg0k3cSbC51XZjIg7M6126C/BaU0fdlRtHawxHwR5Tv7IJkEPhiZ+r8thfgFIREBERAREQEREBERAREQEREBERAREQYXBzTTuL4wTGdXMHDrHYpCwgw1we0OaQQdQQtlHc007i+MExnVzBw6x2Ls1we0OaQQdQQg2REQEWFWDHqLu99K9+QtNg8+KTyugtEWAQRcG4WUHCog2tnsdklZ4rv2PUkE+2DmPGSVuj2fuOpd1wqINrZ7HZJmeK79j1IPHVXRp8PSeGrjbvkLjbc7Q/Fe4XCCYTXa9uWWM+E08OsdS7oC5VH1Y++35guq5VH1Y++35gg6qv6QfZ7EvwsvyFWCr+kH2exL8LL8hQWCIiAiIgIiICIiAiIgIiICIiAiIgIiICIiDB0FyvPMPdNQa1hDnuefBOgy7h8F6B7c7HNva4tdVYwmSNmyimbs731bY/3QR3tnllbCSNo8203BXUMTYYmxsHgtFguNJRinJc52eQ/zW3ehSkBERAREQEREBERAREQEREBERAREQEREBERAUdzTTuL4wTGdXsHDrHYpCwgw1we0OaQQdQQtlHc007i+MExnVzBw6x2LqZWCIylwyAZs19LIK7H8S730JyH+NJ4LOrmV4QBz3AC5c4+sqZi+IOxGufLrsx4LByCs+iuGbec1krf4cRsy/F39kHocGo30OHRwyOLn73XN7HkFPWFlAREQcKiDaWew5JmeK79j1JTz7W7XtySt8ZnLr9C7rhUQbS0kZyTM8V37HqQd1yqPqx99vzBa08+1Ba4ZJW+Mzl/ZbVH1Y++35gg6qv6QfZ7EvwsvyFWCr+kH2exL8LL8hQWCIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgLy3Sit2F6OnfbaeFK0cP/a9BiFYyho5KiTc0aDmeAXzyonfUzvmlN3vNyUG1HSyVlVHBEPCebegc19EpKaOkpo4IhZrBb0qj6M4XJT0/dZLWySjQObezfXxV7ln/wBSP2Z7UHVFyyz/AOpH7M9qZZ/9SP2Z7UHVFyyz/wCpH7M9qZZ/9SP2Z7UHVFyyz/6kfsz2pln/ANSP2Z7UGtRBtLSRnJM3xXfsepc9vtYsrhkla9oc08PCC7ZZ/wDUj9me1cZaV8skb3SMBY4G7WEEgG9t6CWq/pB9nsS/Cy/IVYKv6QfZ7EvwsvyFBYIiICIiAiIgIiICIiAiIgIiICIiAiIg4VUzoYwW2uTbVRG1soOtnDlZd8Q+qb6VAQWDcQpcwY6ZjHkeK51ipIIcLggjqXiukeW8Hla+rRVMVRPAbwzSR/dcQg+mIvBwdIcSh/8Av2g5PaCrCDpdKPr6VjutjrIPWIqODpTQSaSCSI/1NuPgrGDE6GotsqqIk8M1j6iglosAgi41CygIiICIiAiIgIiICIiAiIgIiICIiAsLKqekOJdwUJaw2ml8FvUOJQef6TYl3ZWbCN38GE204u4lR8Bw44jXAOH8GPwnn9lXNa57w1oJc42A5lfQMGw9uHULYtNo7wpD1oJwAAAAsAsoiAiIgIiICIiAiIgKv6QfZ7EvwsvyFWCr+kH2exL8LL8hQWCIiAiIgIiICIiAiIgIiICIiAiIgIiII9XE6aMBlrg3UMUc5/lA9JVoiDxeJ4Pis05ldAHjcBG4GwVRNTTwH+NDJH95pC+lrBAIsRcIPmCL6JPhVBUX2tLESeIbY+sKvn6K0En1bpYj1OuPig8Wi9HP0RmF9hUsf1PaW9qr5+j+JQ3/APj5xzYQf7oIMNVUQH+DPIz7riFYQdI8Sh3zCQcntBVbLBNCbTRPjP8AU0haIPTQdLniwnpQetjrfAqwg6T4dL45kiP9Tb/ovEog+jQYjR1H1VTE48swv6lJXzBd4a2qp/qaiVnU1xsg+kovDQdJcSi8aRko/rb2Kwg6X7hPSeksd+xQepRU0HSbDZfGe+I/1t7FYw11JUfU1ETzyDhdBIRYWUBERAREQEREGkkjYo3SPIa1ouSeAXz7Fa92I1z5naN3MHIK96WYlZooYnanwpCPgF52gpJK6rjp497jqeQ4lBd9FMN2sprZW+Aw2jvxPNeuXKmgZS07IYhZjBYLqgIiICIiAiIgIiICIiAq/pB9nsS/Cy/IVYKv6QfZ7EvwsvyFBYIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiIMOaHCzgCORUKfCMPqL7SkjueLRlPwU5EFDP0Uon6xSSxHle4+Krp+iVQ36iojf94FvavXog8BPgOJQXvTOeBxYQ79FAkikidlljew8nCy+nLVzGvFntDhyIug+YovoE+C4dPfPSRg82eD+ir5+idG+5hlljPI2cEHj0XoJ+iVU36meKQdd2n91Xz4HiUF81K9wHFnhfogjwV9XT22NTKwDgHG3qVhB0nxGLx3Ryj+tvYqh8b43ZZGOaeThZYQepg6Xt3T0hHWx37FWEHSTDZbXldEeT2n9l4ZEH0mGspqj6meOT7rgV3Xy9SoMSrae2yqpWgcM1x6kH0ZRMSrWYfRSTv3gWaOZ4BeZw/H8VmlEQEUx4l7bWH5KVjNPU4m2M7RjAwaRgGxPO6DzM0r55nyyHM95uSvYdF8N7lpO6ZW2lmGl+DVRYJhTqvEyyZhEcBvID8AvcjQWCDKIiAiIgIiICIiAiIgIiICr+kH2exL8LL8hVgq/pB9nsS/Cy/IUHP6RYL50pPahPpFgvnSk9qF8URB9r+kWC+dKT2oT6RYL50pPahfFEQfa/pFgvnSk9qE+kWC+dKT2oXxREH2v6RYL50pPahPpFgvnSk9qF8URB9r+kWC+dKT2oT6RYL50pPahfFEQfa/pFgvnSk9qE+kWC+dKT2oXxREH2v6RYL50pPahPpFgvnSk9qF8URB9r+kWC+dKT2oT6RYL50pPahfFEQfa/pFgvnSk9qE+kWC+dKT2oXxREH2v6RYL50pPahPpFgvnSk9qF8URB9r+kWC+dKT2oT6RYL50pPahfFEQfa/pFgvnSk9qE+kWC+dKT2oXxREH2v6RYL50pPahPpFgvnSk9qF8URB9r+kWC+dKT2oT6RYL50pPahfFEQfa/pFgvnSk9qE+kWC+dKT2oXxREH2v6RYL50pPahPpFgvnSk9qF8URB9r+kWC+dKT2oT6RYL50pPahfFEQfa/pFgvnSk9qE+kWC+dKT2oXxREH2l+PYFI3LJiNE4cnSNKgzzdE5/GqqFp5slDf0K+SIg+lT0nRx19hjkMZ/qla4fsq+emoWfU4zh8g65g0rwqIPVvngY7KamnP3ZWkfArHdNP8A8RD7QLyqIPoGBYlQU+221XTsJtYmQK2794X5wpvahfKUQfX8PxrBoTM92JUjXPIv/FHAKZ9IsF86UntQviiIPtf0iwXzpSe1CfSLBfOlJ7UL4oiD7X9IsF86UntQn0iwXzpSe1C+KIg+1/SLBfOlJ7UJ9IsF86UntQviiIPtf0iwXzpSe1CfSLBfOlJ7UL4oiD7X9IsF86UntQn0iwXzpSe1C+KIg+1/SLBfOlJ7UJ9IsF86UntQviiIPtf0iwXzpSe1ChY3j2EzYHiEUWJUr5H00jWtbICSS02C+QogIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIg/9k=\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"640\"\n",
       "            height=\"390\"\n",
       "            src=\"https://www.youtube.com/embed/mP7DX6x9PX8\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x1060378d0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('mP7DX6x9PX8', width=640, height=390)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Spaun: pattern completion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"640\"\n",
       "            height=\"390\"\n",
       "            src=\"https://www.youtube.com/embed/Q_LRvnwnYp8\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x106037518>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('Q_LRvnwnYp8', width=640, height=390)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Benefits\n",
    "\n",
    "- No one else can do this\n",
    "- New ways to test theories (neurological constraints)\n",
    "- Suggests different types of algorithms\n",
    "- Potential medical applications\n",
    "- New ways of understanding the mind and who we are\n",
    "\n",
    "<img src=\"files/lecture1/compare.png\">"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  },
  "livereveal": {
   "scroll": true,
   "start_slideshow_at": "selected"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}