{
 "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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      "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 0x1c9101cb588>"
      ]
     },
     "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": {},
   "source": [
    "# About Me\n",
    "\n",
    "- Undergrad: Systems (Waterloo)\n",
    "  - option in Cognitive Science\n",
    "- Masters of Philosophy in Computer Science and AI (Sussex)\n",
    "  - experimental psychology on robots\n",
    "- PhD in Cognitive Science (Carleton)\n",
    "  - philosophy of computational modelling\n",
    "- Postdoc: Systems and Philosophy (Waterloo)\n",
    "  - simulating neurobiological systems\n"
   ]
  },
  {
   "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",
    "- How does the mind work?\n",
    "- Do the neural details matter?\n",
    "- Two advantages\n",
    "    - Predictions\n",
    "    - Algorithms\n",
    "- Why is this useful? \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/research/syde-750.html](http://compneuro.uwaterloo.ca/courses/syde-750.html)\n",
    "- Contact information\n",
    "  - Terry Stewart: <a href=\"mailto:tcstewar@uwaterloo.ca\">tcstewar@uwaterloo.ca</a>\n",
    "  - Course times: Mon 12:00pm-1:20pm & Thurs. 12:30p-1:50p (plus 2:00p-2:50p Thurs for SYDE 750)\n",
    "  - Location: E5 6127 \n",
    "  - Office hours: hour after each lecture"
   ]
  },
  {
   "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\n",
    "     - Make a novel 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\n",
    "     - [ideas](http://compneuro.uwaterloo.ca/research/syde-750/syde-556-possible-projects.html)\n",
    "     - Get your idea approved via email before Reading Week"
   ]
  },
  {
   "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 4</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 8, 11</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 15, 18</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 22, 25, 29</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 (22rd at midnight); #2 posted</td></tr>\n",
    "\n",
    "<tr><td style=\"padding:0 15px 0 15px;\">Feb 1, 5, 8</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 12, 15</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 (17th at midnight); #3 posted</td></tr>\n",
    "\n",
    "<tr><td style=\"padding:0 15px 0 15px;\">Feb 19, 22</td><td></td><td>*Reading Week*</td><td></td></tr>\n",
    "\n",
    "<tr><td style=\"padding:0 15px 0 15px;\">Feb 26, Mar 1</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 5</td><td style=\"padding:0 15px 0 15px;\">Chpt 8</td><td>Memory</td><td style=\"padding:0 15px 0 15px;\">#3 due (5th at midnight)</td></tr>\n",
    "\n",
    "<tr><td style=\"padding:0 15px 0 15px;\">Mar 8, 12, 15</td><td style=\"padding:0 15px 0 15px;\">Provided</td><td>Ongoing Research</td><td style=\"padding:0 15px 0 15px;\">#4 due (12th at midnight)</td></tr>\n",
    "\n",
    "<tr><td style=\"padding:0 15px 0 15px;\">Mar 19, 22</td><td style=\"padding:0 15px 0 15px;\">Chpt 9</td><td>Action Selection and Learning</td><td style=\"padding:0 15px 0 15px;\"></td></tr>\n",
    "\n",
    "<tr><td style=\"padding:0 15px 0 15px;\">Mar 26</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;\">Mar 29, Apr 2</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 (Matlab or Python; see webpage)\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": [
    "# 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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iX5iIsCjJMzdf/ESUZTTje48/KSxdty69KVLOwUD1Mwu0P4kSsFNN4m9fKee1\nev1Grcta5Ps8oqYkLTOr7Q1OrYm2w49IngyRM6FzKygZzylhQCcP3SJcfRJGCIMSwncuYsDlZfWc\n1z1xhKsYz7pZf47EAHJMrXhhLqT9upPQczz/AGnbv86X1KhNSAOkaW3uxhv7CGuUHdb69DK6tO9/\n2hOAOk818e2f9HxU2orGME5yfZM/x6XUEN0z1mhpSdNSec5PMq1LNqkDnhRwo9ZiV3ym8XBq1QA9\nW9J0Nq2+07plHlG+z9FXSoawBrj6/hmgba14cj3SWxMZ12r0Nhu0+5wQehzM3+Irad1Qqr2svU+s\n0LdXVWnhYTzXa15vuG3pGPpaco1p2KuY1VqHst2kEpiYukrss4wZppu0uDnOes1ZHf55W4tDV6Dv\nVWtHCswyQfKefWk06p6d27acZEdt7Qu06s9fDH8R5lHZ1bX2va5yTyYnjNt3qnaVor0xYnNp4wfK\nLWakKrLmatFOFZdq+LqSORIa3s2gVb8czO41f8ec3l34kHbxjMbtoFX3OkgtSlCzDmbjy/SduUqM\nvz5RihK10Qt2AMLOvrxFORVY4PMliyvT1hjww3/9S0x8ruquYMqg9eTJohC8+YzIPRju2JyW5jVf\nj6joJK38peT0PYLLZWq4zgcgznbmlrJZdvUZxKextTXpKGP4zwIdpa3KMztkkYEw1lLPpv8AHkb6\nzXYSvrOd+2MS+0izOZnsCr9Z2jnlvDLcbnZhW1cMxz75ztJ2XwknHlFez2KsCDGdf413Z5mNduly\n3ENBcazwOTL7tYS5LcRHSE7wBHrNH3hLFhwJdRz3bOij61ieBxIfSDkEcR5OzGavcOPZFNRpTUTn\nmWWOGUyQe17mHUy0WhXBK4x6SunCnB4zLthdsDmU+e5dnadaCAo84zqq0v0jBQAVGRMhqzVanE1q\nSRWA3QiYsk8deXL1iq5SwAH3y5tST4VHMm2kDWWEP41PCbeo98hWoS4bhjHWacbudGKFs9hxLw1d\nlb1HoQRAXrwqiV2jLZA5mf16JrRLR1Gm9kbkf8x3WVi7Skc+EgzraX6QlYrYC7JHJlKajJXTseGO\nC01+s6tmoTt4TPtlEa1CFUweoMWM7x8z6ehZ2RHWTmnNyEJ2B9AInNwUZJwJLImD2x2gSTTUfeRO\nTqj2t2sXJpoOF8yPOYucmS2k9YbRCIzu0mSGJ2BHaBJCchA40jJmRgUHwuRL6D1EhYm4cdRCpsMD\nPThltmr28JBltf3sj0lb8yxPCDJ9fG/n67qD9jgnMs02oWnSDPLekUuYueOmI7o6ae6VrPP1nis6\ne+d0tZq27sjoW6CN6AM4BPRegk9PoKLdQ1hIKj4S3VmvTuuzhcTO54603p0LFiTg56xfWaG0tvqc\nn2GRTWBcjPE6O0BuwXzg8TPGxJnLGNcbEco+RiWafRm472Hhl9yrqtdtH95ffcunQKeB6Te28MZf\nXX2UoErAAHpFrbM9TxFX1ZLnA4kbWscfcOOvSSR0yzmPjmtu3hUHSaXZVlVVYDdTMnS0HUasK2Qo\n6+6eh1Wk0yaYEDGBLlrxymVt2aovo73xkYPqYr2h2lW1pRFAC8ceczRS4AJYjJ4HsktfRv1AFLFj\njJJMzxjcvey1lu8MZSXzTjMqud08GMGU7LUUA9JuRwyy5VfWDYEr6bjzLVXvGA9Tge4SWnARbHbk\nqmB7zxK0fu71B8hDp5iZ1NedQiVjoI2ukC1AO2M+coqfdqtw9Ok0lps1I2qeR6zFb+d/SlabGYDO\n1ehiOrve6wVKCeZt3afuQRkMceUz+yytfaTWPWzkAhABnxekRvO78K2dmsiorv8AatyUA6CNH+HE\navctpyemZo6HSG3c9rhXzli0ZW1FYoGDARcrEkmtPK6jQ36FhuB2/mHSQL70IJnrLjVdWUcBgZ5f\nW6f6LqGC8qek1jduWXRQMVcEeU1dLqFswGOJmV1NbkqpIHUjyjNeltGCqt8JqueO20L8em2Ka65D\nSQcZMWS16+Gz7jI2r3u5j6TOnSdpaalblKsPcZJK309pVPHkSGjbACmNvncHX70hZC9rHALrhsxm\nu7wjMT1lhOMDp1lTWsu3HQib04cv7NLSupssJ85V2hVx3q/3i2mv4Mcdi9LA9CvESaq53cJae4L9\n7rJ3akDp1MqpQEnIlOtTY648xNajFz60c0tj537jkcysahyxQHK7t39/WK1tay7M4HsluO6AEsh/\nJYjdYbHJPrK4HrAzrHgt3UZLykTJL0lQTs5CEeq7V7Q7oGqs+I9TMIsWOT1MHdnYsxyTIzk6OyJn\nZwwCdzIwgShOCdgchAzhOOsAkHwOScSq7VKvCcmLB2ttUMepmsdxK2E4rBPU9JxzgESa9fcJxMG0\nE9Os6/TxvD1elYo07FhliOZDT5amvpg8S6pTfS/ti+ltCIVbqrGeOd7e6/100NMyJWVzyTzFu0Ll\nsGPMRW3U/abagWY9AJM9naw1G2zAGM4kmOi53LwpULtRZsTMYbTnTkd4cmM9hgC593Bm3qdBRcm5\nl5AjLLVTDF5/s6xRq29s3Tp9LeAXUEzzp0OrqtLipto/4jdOvx4TxiSzfjrjbPWwnZOhJyahGdRo\ntKy7q18ft9cYmfRrfs8k5j2n1gVTkZBmN2NXG3uPNajvNN2kTYQQRgYGBiTGo3ZZzkL0Ed7WCXaW\n0Ig7xTuyB0ExdM28gMDzN+zbXlPPm1d2c8eUKWWtCxGDJVUFbMpwg6585XrSp46Yhq5dFLtttu/y\nzI3EGtffKFtLeFfLmBc7E95M08uSxd7MUA8J5+En3HeVvZ0IPENNZznORjjiX6esvXjdhSeYal2h\noAzXez1mzRZ9Gs7zcMEecTrqUfcOAJXae8BBOMTPtd/njqdtXUa/vwoxXzx4TM+vUWaM2mlgpZuW\n85muzU4YMcCQNjWWBwGIAycCWRM7rqN7vz3e9zknngTPe995YHGJND3iLt5GJX9H8RDHOZnTpjel\na69g33sxbVXm7rDV6dqvGF8Mr09D6lyB0VSzH2CdJI8+eV8bOg0xfs2iulS722F3x5AdBNC0Fa1C\np04nOxL00ug2Abnck/8AqJdqH4AE55XtcOp2zNVRvToMzPsRkPPE1tQ6oM45itw73T7yMSzbOWUL\nfR9oV+eRmSawisdc+kuR1alVP4YnfZnUKo6DrNyM3LaT6crWXfgtzDTqrWBW6SV9++sJnmVZ2WAw\nddGdRpEULZUu0EeISaEdwR6CSe3/AMXEqV8VHPnLi553soj7HOZTe3fXDjpHL9ldO/Hi8otSmOT1\nM0xjE6Qa/LOYHFhZvIdJy9mRAo6tJAbagvrLjEzykhYicMtZZDbOrxKzOr1ksSJEDpEJIDIhiAzC\ndnJydBCE4SB1gcMMyLWIOrCQN6DzgW5ndw8zFG1B/CJWzs/Uy6Ta+7VKvCcmKPa9nUzu0TmJqRNo\nASS+FwfQwhNI1kfw59RO1N9sIrpLdybD1HSWBttysPXmay7jphdVs6RgnHAHWZV9RGrZU/GekdSx\nRknriL1P/wCbvznjiePHqvf9NWNDs7SVaQ95Z4rP+Jsd4r0np0mA95AyTJ6fWGx9pPEzljb2uGUn\nSPGk7TyvCOMia6a0FMmYXaTl9UuDgqJWmux1PSW47iTOY5PU02pYB0mf212X9Jps1dZVWRc4HHSK\naDUlxknHM0zcpTbYfsyMGYnVdrOU3Hm9FqSfAx6GbOkszkGYWv7mjtEnTE936mOafU7MTeU2YZa6\nOds2ju6tNQR9pzcR1I8hF69IzUrZWBknAB9JVZYGbcec/wC81dERXpgXxuPWZ3qOv9ZC1uj1VNYs\nVlIPlMvVFyW3AjImzrLmKZU+ETB1upZ7QF6CXHtzy8cWpaKifMxZrdvIlupu36cYGCYgLAWwTN6e\nbK6pure74rj1FhrrKk89JXoFCbQercmd1691bkDwtIs/07pr07rk8gSQWvepsJGTiZOls22gHpNb\nC6krkEKDJrT1YZSnTotK58Wcjy8jGR2dUtIsXwkfdZfKZttvdsKqcl84GZo0swrCM3MzbVvbNwab\nzWawm7kY6f2kjp88ljmc7Sv3lTwCvQyltV4VJMutsZXh0ZakPWVs5GJjoO6vZfKPpqGsPsies8N+\n4ec1I5XPfdanZ9yhMH3S++w4yOgmGmoIYFYyNYNuCY49sXNzUXMLSfwyYvD0EA+Uq71X8usg6FAB\njBm9OdqaOFrOTKqnyxJHWUgs94rPhHnNJ6kFfAHsi9N4TfdZtrbbz74Wvuxg8yd9ebCB1EoFZV+s\n1053Kw3Xa1gCkdJbnjBPTmV1beAP7yGofJPt/wCIiWoPYbrR+RekkHGdq/GL8nwjwrOqQhAHTrmR\nfxeBut8R4HSWEZidNj7tx85pU1K9ib7QFfr57Z1xjzZzZUrIlYzYgV2A5APB9ZXtlclO2c2y/bOb\nYFSiBWWbcSW2BEuF6sJBtQg6cxSdmeLW1jaiw9MASoszdWMlicxLo2hidxJYhtgQhJbYbYEZEyzb\nObTAhOSzaZwrA4jFGBHURsMLk8P3vSJ4llHF6H2iXeietAttGSZSjkMrQ1AJYgHAnNvg46Tjp7N2\n9GLbN6YXnMd0Omaps2DnGYr2bWATZZyFOAPbH7bwLEYHjzmMq6fOdlu0dPa7b0B+ExnrdW5E9sr1\n7QTggiYHai1jUkV9Opkwy/F+uG7uLNMGrpXAHSXm7IwT0iyv4Bg4ieqvYWYBk47dccpOleuUWOWB\n6GVpaQg5neWyTO6albCQ7YUec1Oms5+xbXbm1WPRZoreW6SD9m11dm7yx70nPuElpdDZXWpPiJ5M\nl1XPvZutRZUSfMTE1FIa1io88TVLsoYKh8PXyEybLwu7nkmSRrybpZ1LWBccCLfRiGLeSmPlht3e\nsimHqOPMzW3G9pVMRbWY5eRYwA5IityFKw35ZWNQd2QccSN462kaWazpj0xNPQnKhCCCIpptz4w2\nPFzmaS8XjuyGAGDFrtqa6Wt3NTgjlvWJ63WkOD+GW2pYgJCk+kydR31j4dcCSJ3Fr29505E5ZQ5A\nO7j0k9Po7XXcRiStqsAz6Te45Z7tM6Wte4yMZiev8Tqo6+cNPe1bEMODIPZus3NJIzTNHZ7GsN0D\nRpOxwy5wcy3RXblXpwI+btizFyvjpxmi+n7PppAYjxCIdsgPYnd9RHX1QI6xCw9/qwo8pcd+1iyX\npmLlbPF1Ed70WAEHAEs7U0gVRbX/AHmejFR7Jud9p50ZrQ5Zz0lV6beR1MYquV69oi97/aZ/CvE1\ni5/S76cyK1x0J6xC7VZtO3pI6i5rHIWFGmLEcZmpNuVulqXZwMcSNrZsKY49Zq6LRNtdUrQ2AEnf\ngYH95n6igh+8H3W49xl4ly3AhP4RmMI5UZMoSxKxgiWqTcQqL1nRzaVBW6oNjMkdMp6cTmmqFNQS\nXiWOF9KnTkdJA1EdRNACdKK3lLpNswpOqseakekgaMciTS7YzadvKc7gzWNa+k53S+kml2y+5PpO\n9y3pNE1j0nNsmjZHuDD6OY6VnNsLsp9H4nO4jpXiRKwFO5kTWBG9sgyiQKlZWwjTKJSwEBcyPnLH\nErgPqe9qVvPzkGbBxmV6dz9zPumqNFW1RP4sTnl09nztyiKkV1qvsllKBjk/2lVqb0yPKcpswB7J\nj10l1TltvdIeYhpazq3Zm4ENXccYHnxL9OTRWFHvMzrTpjntVapqBU+UzmbdbgmaOsuUJu9ZlFSz\nhhnH/M1izevDJ6YBx7Y72XpB3wsc5rXkf6jM8Nl8OpUZxzNH6UcAJgKB5SWOkzmjfaF/eNVSOjNk\n+4Tp1gTw5xiZ4v7zUq2PuictOGJIzmZkNrddaHO4HyiNlS91ubg9Z2vdY+0kdZO4d4QAeB1mvEy8\nI3v3VZ9o4kNHfgBW9ZzWnNoErdcKCOsuunH9bNtiWIEz1g+hJqDVr7z6SvsTTHU2Frvur0mrqWWl\nT3WMAc8zP/p3wx32yF309eR1l9OoIbIMirh3JI4M5bSqAtX8JWrNNXTOHbJbOYxdXSyfdGZgU6og\n4yQZopqC1ePMzNxTkmNUEyh/tFfpOSVHUmV2B2csRxFQ4FufbNTFzzy7aQ0wfG7qZ2zQKUb1AyJG\nq/cF9nnGHs+ybB8pO1llZ+jsZbdhYgTWOGH3yJhMSrBx6zTo+0pyeYsN/irJruKE59JJFNOsRm4D\nCdcj6Qj46DENYS9YYdVMvqW67XamwGt18iJjLYoUiX2WMydYlyWIxNyajlMu902u1Ki/pzE7Li3i\nxx5CX2OUqCdWPl7JTt82m8cduWWXaFa+ZHMaryuCOIuLFDgRkMpE6xyphbT5nM7ddu0r17VI68Lz\nFt4Euq5GT5y7S9F00neEM/E0KVStQFGJXJLxM6c7laaUiTUxZWlgaVk0pk8RZXMYR8iVEsZnCvEk\nJMYxzKEuBOEiDSMwoIgFhmSHMKiV4kcCTMj5QKyJHEmTIGBEiVtLDzIkQKWEqYRhhK2EKXYSlljL\nLK2WRVVSk2qB1zNiu0nI9ZmachL1J6R1DssE5ZvR8eu1tL7WZG6iUWE12nPQy1xm3f7JK6tbav8A\nV5Tm9HpK5slcc45jAtygI5iRVlOJWNSanII8M16zvjVtzsW8QyPSafZumrwLH5I6TLaxGG4HiNLq\nQEXDYksdOUa2sp0t2nCd3tvA8JUfe98x7uz7qfwsP7TT0l6n7SzribFGvruxXYq2LjoR0nPdi5Yf\n48S730Hjn3yVfaBY7bU+E9ld2NpNWhalgrn8Jnme0OxrKLCcbeeZqZOWqoCbrV2jgnk+yaFtWlOl\n+xJF3mJT4K1CqOMct5mQSxa+SeZfXo649sfU5bVEekmi7iFkrmW3XuyjAIl2lCh8mac8Zs5pbhTW\nQBweBJ2FrEJzyYnWdlhLdPKP6cqVyfOZ063LUIb8OFj1VRcDmK6yvZYHXpnmN0N4BzLXLmhfovCW\nHWL1WsnhPlHrLwFwTELLEydnLGIm9pHWgI6kc+UlToXv05sziUJpzuDuCTmaGm1Xdbq24HlJb/jU\nw3NkFNunbnkRoajfXgdZHWFGHhHwiTPsOEOTNM9GNRmuoBvPpGdJawrEzlL32DeeBHFsWrjMU2Ys\nbwZ85F7wtJB6mK9+1jcTtqkAExIzlavrqL0ljF0Copduo6RpLFWokngCZr3hm9nlN4zbnlqJ7i7F\nj1M4wklHEhaxHSd/I8/qAQFgcRhQScKOBOV1llBPEZUBRgSbS1FKRnLdZbIzohi1ISchOwiYk8yA\nkhAmrS1HlAEsWVEtRqr6a81ViwjrFtN2vc9wrtqxuOPSMrkEktn2ekuUISCygzNlrpjlhOrNqusi\n0MwmtOW0ROg4hCTSgmc8oEZnAI0rmJwiSIkTAgZEyRnDAhIMJbiQIgUsJUwjDCVssilm4jKapWXx\njDDz9ZSyyphM2bbxyuPjQS3emVnDaV65lGkPgZc485y53x4cmc8p29OGW4pu1BUHA5PnKaqze3MY\nyuwBgcTtL+M4GMyNa7cOmwO7z1khowvU9JavNoJ4k7Ld42r/AHMz26Za0truCLtZeT5y2vVd1zuA\niVu41jHlIBWsXk8S6jPKt7T9pcdf7yzV3pqKChJywPImAlLV8qY1W7DBJ6THGNQrcXVth92ZQfE+\nM8DrG77QCx4J6xZD3mWIxmbhlSWcXFvWMVNggkdJXaubSoguVOP+pdMY5aPYS1chgDL08CAekzVb\nGSuM4xIGyxOhaNFtrQtsVvCZGmw1DDDw+RiSXMW8QzGltVsDoINGSEtGRiAWtOiyr7M//khtX8x+\nMiQwbUVeSBE7tQG+6M+2SZa1HP8AvKLbUAwgyYkb3pWWYnkmSVTIrkj2xnTMFPjGRKz72irBeB1g\n1buc+UfWjTWnkDHqJP6JVWuFtOPbJtrZGshJZbYXTaF5Mgyp35CnIH+8ue2utMVjLkSxMspooWJX\nBJwJD7zDC5Msrqbq5/tLVAXoJ2nUeTLLdCqccyaqo8pwTsrnanOiRBkoR2SEiJISjs6JydEImJKc\nE7A6OssXmQAkpUWASePSRTpLV6cwFoCS2wIlZRnJLE5CuQhO9JASDCTzONzCq5wiSnIFZhJNImBB\nhIES0yDTKqGEpYRhhKmEiq6iFcg9DH1VGQcTOaXVagKu1pjLHbv885OqvurVrMfhAiN52uNnlLbb\ncjwnJkKay/iYzGtPRylHed4vi8LTldTA5Uy16c9JFS9R9kCwWMngYcesnUQcgGdytyZxKSrVtlek\nIYBw2TDVkpXkdSeJWlgfqRmddS2NzcDpJpvfShaz59ZaRtpPrLPCBKnJ2k44hL4UbKsLD64MLX2N\n7DLdgsrIPnKnGG2+k3j65Z60qDjdweZaNx68Tm32TuPbOmo48kWbu8M0sW2qwcEZlbqxB5BHoRF+\n5ZGBA+BmcpPxvHPR1FBbrLxSp84ovI5XE4e8U5Rz7pODX8spxtMoXPJili4MkNZYoxYhPtEg9yWH\nw8e+Z1W+UqenTe0satgZRWzI2VOYyNRkeJTFlMc5FIZ0bKkyxtQ+ws/AE7vHUCQtG9CIkMvpj+K9\nOxdyTGhFdKMAxkTWLh9f+k4TgM7NuTokhIyQhHRJiQEmJR2SEjJCEdkpwSYlAsnOASYEAEkBAJJA\nSokg5l+PSQQcy0GVC+0zhXE83/M+t/S0/wC1vnI/zLrD/S0/7T85nnF4V6XbOETzf8y6z9Kj9p+c\n4f4k1h/pUftPzk5xeFejPE5POfzDq/06P2n5zn8w6v8ATo/afnHOHCvRwnnP5g1f6dP7T85z+YNX\n+nT+0/OOcXjXoyJwzzv8wav9On9p+cPr/Vfp0/A/OTnDjXoGEiZg/X+q/Tp+B+cie3dUf6dPwPzj\nnDjW80gZhntvUn8FXwPznPrnUfkq+B+ccoca2mEraZB7Y1B/BV8D85w9rXn8FfwPzk5ReNaTCVHr\nED2ncfw1/A/OR+sLfyp8DJyi6aE6GKjAMzfp9v5U+Bh9Pt/KnwMbh20tx9ZYLNwwTzMn6fb+VPgY\nfT7fyp8DJuNS2NcMamz5GXLarrxMP6wuxjCfAzg7QuHQJ8DM13mcbmwKplaCzODzMr6zv/KnwPzk\nh2tqB+Gv4H5yHPFuV1E9YalFFR9kxh23qQMBKvgfnK37V1DjBCfA/ONJzh8EqM+srA5JPJMQ+n2/\nlT4GH0+38qfAzU1HPLO5NGdmb9Ot/KnwMPp9v5U+BmtuemljiRxmIDtC4fhT4H5zn0+38qfAxyNN\nEcTszfp9v5U+BnR2haPwp8DG4aaJTd16RazTktmvlTKPrK78tfwPznPrC38qfA/OS1uWxpIBXWF9\nJ3rMz6wt/KnwMPrC38qfA/ONxntpwmZ9YW/lT4H5w+sLfyp8D85dppoVEbjLczIXW2K+4KmfdJfW\nN35U+B+czLp0z1fGtmSDTH+sbvyp8D8536yu/LX8D85rlHPTZBkgZi/Wl/5a/gfnO/Wt/wCWv4H5\ny8oabYMmJhfW1/5K/gfnO/XGo/JV8D845Q03hJCYH1zqPyVfA/Od+u9T+Sr4H5xyica35NABn2zz\n315qfyU/A/Od+vdV+nT8D85eUNV6MSazzX1/qh/Tp/afnOP29q2XG2oe4H5xzica9MLO7OGOcy5P\nEM9czx31tqS2SEJ9oPzl9f8AEOsrGAlJHtU/OJm1ljPx65D5SYAOZ5H+ZdaP6dH7T85IfxPrR/S0\n/wC1vnNc4xwrEhCE4OohCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAh\nCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAh\nCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAh\nCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAh\nCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCED/2Q==\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 0x1c9101cb400>"
      ]
     },
     "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": 4,
   "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": 4,
     "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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      "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 0x1c9101cb358>"
      ]
     },
     "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": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
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xAjsixan4FdsAQASCNkyC+wrAWk2J7jZPAMk9jJs1ZlJbgESIbKZDZlEyC+SJGaEyszbZ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\n/9k=\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 0x1c9101cb9b0>"
      ]
     },
     "execution_count": 6,
     "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": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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ERZZYy/Vu7lUOcAFbyglhtvsqOU2NjvCqw9ZOaeYEajiFYsnMjjbQWuqVxurCiuL34BFW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\nAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQf//Z\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 0x1c9101cb3c8>"
      ]
     },
     "execution_count": 7,
     "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": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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CU3aatEZekHyAM9jVO8sY4W3R9sjnAoZa2PAVB8wtFxglfHUoDslt4QcZ7/1r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fP6Hm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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 0x1c9100a0fd0>"
      ]
     },
     "execution_count": 8,
     "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": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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+fuzjfvWz8IkUD4Vzv8VZARr0RVGP6l4LL+c7Og00dlzi4j9kyqKZp7br+j3y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\nhAIQhAIQhAIQhAIQhAIQhAIQhAIQhAIQhAIQhAIQhAIQhA//2Q==\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"640\"\n",
       "            height=\"360\"\n",
       "            src=\"https://www.youtube.com/embed/CpejbZ-XEyM\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x1c9101cbda0>"
      ]
     },
     "execution_count": 9,
     "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": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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HHw4/s8WLqkiOfixPHj+3RAY8WM9Wnhh1hQcfDBPBDsA8/gg8EPQyDhPDEJ4o/btkgP/Z\n",
      "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 0x1c9101cbe10>"
      ]
     },
     "execution_count": 10,
     "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": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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Cz1ErkfLJJPfVO6fpbmL3Lvl9j86e1vI3I8uXc9qaUqjQRWyBpuhwtwQFJIA5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\n9Vt24R/DKyFURJ7V8ebjM5Y81Q32jeaJVDiIAjipuAykfWX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      "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 0x1c9101cb630>"
      ]
     },
     "execution_count": 11,
     "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": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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       "            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 0x1c9101cb438>"
      ]
     },
     "execution_count": 12,
     "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",
    "- 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": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
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J2HtLWajK0D+qztlq6mxCWoFdNG0Ma0tsbam6kdqManwGhhnw/knOkkyHlBmFrEoNe2Gp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\nk+Mp9HMP+zJ8ZQa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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 0x1c9101cb780>"
      ]
     },
     "execution_count": 13,
     "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": [
    "## Problem solving: Tower of Hanoi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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Bu9G+riyHDTQHPHiudPS1ljqmQSVUc0NUXPe7cxu/blB9tud42yc+moRFSuiI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\nCIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiIP//Z\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 0x1c9101cbba8>"
      ]
     },
     "execution_count": 14,
     "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": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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PIfM3qDr29IUUmGAiwqJAFbiRrlgxAuuNiqSU0cU+HvcYnOqITqW/XHatyPEo\nnAA2BG46LcNO3LYaKKSkzDUNcOZwug15cRjbskB6l6grI5nFrXaALy6jjtrCwf8AqNVqPgu/LFZj\nhvGg/NBcggjRDZVIkraewDBIOdrh+68nE6lxyx0cpN7eL+6C1JBtfetGspRILEXHVtXumpqmU56l\n4j3hjdSPzWy6NkegufOb3QUz56mlFpWukYNjhtHn5157vd/adrvzDtVjUlroyDttrdaGDGKN0gyN\n4xjyM51NtyDVkqqt7tIzG0/WcpIKKGQZpmCQnQucdVczNinZygAdl1rFgYdtwgr6jg7BKM1NI6KT\nyXEkH9wq92FSxvLZWuBG/aCr7uloHm61sQ0k1Yc0gMUJN9fGd+W5Bo8GqAsrJKkNIY1uQG20/wDQ\nulXmONsTAxjQ1o0AC9oCLBIAJJAA3la0GIUtTM6KnmbM5njcXyg3zkaA9SDaREQEREBERAUUvzkH\n3z8JUqil+cg++fhKCVYWUQYsPQuJ/wBSqTNRQVbXcprwy3pOi7dcv/qG0O4Oai9pmn3FBa8HawV+\nB0tQCTdtjm26G37LeqPmx99vxBcz/p7VGbBOJJDuKOh36kldNUfNj77fiCCVV/CD6PYl+Fl+AqwV\nfwg+j2JfhZfgKCwWFlYQasLWzhlRI/MTy2DNo0btPMtrMOcKjlL4ah8LHExtNhY2t1fldZL32Pyj\nr+dBcuLPrEelVOIzYq2cjD4KJ0TW3zTykFx5gAvLSXXzPcdbjS6rsd7qNFxNE4B8rsjnkhuVu8i+\n9BZcGsaZjdAah0PESMeWObmuLjeCrnMOcLn6SkhoaZkFKDGxgsNLX5zdS53/ANx1rbUF3mHOFrAF\nlZmL2njOTlG62o/f3KpzytlLXSkg7P3HvW1h7XS1Ofa2P63O7/t0FqqynwyAVNVJmeHGQkAOtluA\nVaLXqqOKqjcyQEZhYuaSCgpY6dtfJIKl/GtBLWnYLA7f5WjW0TsMla9zy+F5sCdoKmo5nUVa+klP\nLjNvONxWvi0/dtblB+Sh0F953lBZ0VSct+pSMiYXk7czsx10v+iqaWcRDIfyuVtmpcwWaA51tvMg\nsp6iPi+LsHk6Zdt1XiiJuYpXMO/e0L3CCdQS4na4jat+Lio2WzbU8fT/ABUv7qh0khL7nRzNU42p\ne0DirAbC4q4mMboyQRcHnUGaNpuCD1KjJiiZ3ELseSYj8moO6Xizns/xuvUUMrRyJGg9YK2M7S7c\nLLLqiCMXe+yjHciNRLk0wz54w1paOrlfmfxZI2G9kYK+EACKI25pFuDEKZw8f3L02phO+6tpN9al\nCdK8HEi7M2Jn3c97/styCua45JQYpBta7Q/yths8WwaLEscFQAx4a7eAdfQrEXoTAjrXl1QAPGHp\nUHe9moY+Rg6nX/W6x3vjOhfI7rzIJHztdv0UlPDG1hzAEuNyVXmkqqV5cy9RH/8Asdqmhqg85dQe\nYixQbjoWOBtotGcOpXse06ZgStpr/q7etamIVDA21wf2Qbkc4dbKdDqNVDPKSbLXom5oLk5XA3F+\nZJpRrYoNDEK8wMOb3rZwnCHzUMdT3QWPm5ZGS4tu3qrnhfW18UMWrnOsbi+Ubyuzj4qCJkbXANY0\nNGu4IKwYNMDcVx9UO1exgxc20tU933Whvat51XAzbIPyC1pcXp4xo17vMEEtNh1LTOD2R3kH13ak\ndi2lQT4/M52WlpgTuvdx9AWq9nCDENLvhYevJ/KDoKvEaOiF6moZH1XufRtXN4jwzsTHhtMXu3Pk\n7Ap6XggwnNXVBeduWPT3lW7MJo6SkmjpIGRufGW5rXJ059qDk8Loa3hXmmxHEXdzRusYWGxP5bB5\n12tHR09DTMp6WJscTNgC5D/Tt7s9ey/JAYbdeq7ZAREQEREBERAUUvzkH3z8JUqil+cg++fhKCVY\nWUQVuF4vDiEs8A5E8Dy1zOcXsCFS/wColQ1mBiO4JMrTbqIcueocTdhXCisqZo3upGTObI9ovkuX\nAHzLW4Z4v3fVBkZDmNDb21B26jq1QXv+mR/8etG4ZLely7So+bH32/EFx3+msWSjqn20fkt+Rcux\nqPmx99vxBBKq/hB9HsS/Cy/AVYKv4QfR7EvwsvwFBYIiIKSvbLT1RdlvE43D7aDnBWu2V8jQWAEO\n0BXRLQqMPcZDJTua25uWO2E845kFbxjydGqCrpIqzixUwiTi3ZmakWPOrHuCsfdp4uO/1s1/dZQS\nZ4XFk4yO01J0PmKDyXyAEgDage/ICADr7tF6hbJUOyQcq+1+5vXf9lZDC6cAAcYABawedUGjRQ91\nzhsrbxx3JsSNTsCuWMbGwNY0NaNgA2LEUTIWBkbQ1o5l7QEREFTjOCsxEtmjfxdQzQO3EcxVMMAx\nRl/mnW2ctdciDjn4ZiEcbpJomRMaCS90gAC0qaWtmFooxlO9y6jhQHd5JS0kWLb23i653D526XOw\najYg3IW1uWwLAfOvXE1ruUJrc/J/lemTB28Df5/4W5G/brYc6DTdBVnXjT1jL/2y8GikJOWoeSNw\n2furdly3dbcL6BbLcgHKy+coObfTVjASJ76Xy2utV8VUbGZ8h115P/feuvDgTsv5lHPG0ix1ug5q\nKlY8mz53brtePRsQ08sR5EsrPvEOH7LfqqGMXIu1225F1puBhLml/wCnYgx3VJHq6oBcN3F/yvdG\n+tqWyOic0hhtpvWlLmndlia5ztmhV1TtkoqVkUBYHActxF7nnQQd11sUIlNnM+sGnUc+hXuDFTKN\nJB+a06yWRxZTsPGPe7dvJXRUuE08dDHBPG2RwF3OI1uduqDWjrxvNlBVVrbF+UOK25sBgcbxTSxH\nmvmHvWlLgFa0Whq2O+80tPuughjNZUAPDxCw84ufQtY0FWJsxeJ27hex9C3GYZi7WgfIn/3/AIW5\nFhdYfnaiNn3AT2IK+NtYGg8Qco53t7VAIsQr3uZThvMZN3pXSR4bCPnS6Y/b2ehbYAaLAAAbggq8\nKwZmHsL3ScbUPFnPOwDmC2ZYJDewutxYQU0jTE75WIkddwvUVRh17SQBh53DMFbkX2rWnw+mnBzR\nhp526IJYDA5l4MmX7FlIufqMGq6d3GUMxJG4HKf5UVNwjmppOJxKF1xoXgWI84QdMvnfCDF8Sbi9\nQwVE0LY3lrWNeQLbtm1d7HVwS0wqGSsMJF897ALjeFeK0mIObS0MDZprgGYNufM3eUEn+njHZ699\nuSQwX69V2qqeDWFHCcLbE+3HPOeS3PzfkrZAREQEREBERAUUvzkH3z8JUqil+cg++fhKCVYWUQcN\nglAK+px+AgXdILX2HlP0XNYhwcqoa3iomHLewv8AUPMerrXacEo3x4vjQeCLygjrGZ66V8ET5M7m\nAuLcpPOEFVwVw8UGExsykOI1vt8YnX0q1qPmx99vxBSABoAGwKOo+bH32/EEEqr+EH0exL8LL8BV\ngq/hB9HsS/Cy/AUFgiIgIiIMIQDtCyiDGxZREBERAREQEREEc8LaiCSGQXa9paVwJiko6qWmlGVz\nD6eZfQlV41hDMSiDmEMqGDkO5+ooOfjmDXi13X2rbje292nbzlVEraikmMdRGY3DcRt8ymjllYcw\naQ6+1BdMfYA5h1ktP7qdj5b9W7kgfuquGpaQ3OH576k2stpj2bdD943/AHQWLHPtyw4+YDtKy43A\nL+MA/ILTFRHHtjbzbV6dVgWs1g05h/KCfJG5p5R5r3VRUxh0b25za9g4KearBHLkH59i8QRTVt+L\naWM3vdpZBo4bJxOaMPzEu1JW1JUZjxcbS550DRtJW7VxtoqaOGKETPkdlYDtzc91Y4dh0dGzMQHT\nOHKf+wQamD4T3M7uqoF6gjRu3IO1W6yiAiIgIiICIiAiIgIiIC1a6gp6+Ix1EYPM7e3zFbSIOBxj\nCajCyA975KNzgczP3Gy66bA8Jw6lgjqqRpkdI0ESv1Nv2VpNDHPE6KVgex4sWnetLB6N+Hwy0tyY\nmSExE+Sdbem6CwLmjaR6VjO3yh6VBLThshmjja5x8dtvG/le42wyMDmsYQfsoJM7fKHpTO3yh6Vj\niY/7bP8AEJxMf9tn+IQZzt8oelM7fKHpWOJj/ts/xCcTH/bZ/iEGc7fKHpTO3yh6VjiY/wC2z/EJ\nxMf9tn+IQZzt8oelRyuBkhsQeWfhK98TH/bZ/iF4kpo3gZQGOGrXNGoKCZRzScVBJJa+RpdbzBeY\nZi4mOUBsrdoGxw5wvGJNL8NqmtBJMLwAPMUGpgslHU8fVUT7tkdZ7N7HC9wVaL5RwMrpaThI2ISF\nrZnObI07HWB9919XQFFUfNj77fiClUVR82Pvt+IIJVX8IPo9iX4WX4CrBV/CD6PYl+Fl+AoLBERA\nREQEREBERAREQEREBERAWFlEEU9PDUsyTxNkbzOF1XP4PUDtWsfH913arZEFIODcIPJqpx/j2JLw\neuw8VWSh32wCFdog5l2AVmU/LAu3EO/hRT4VV07GyzTAlzw0NDv46l1aqcUla6upoCdl3nn5h+6C\nup6OKmdx0gc8g6X1ueoKxZMYWZpCG3N3XOgJ3KRscZddxBy7L7uteIIu7Km7h8hEebxnfwg3IIQ5\n7Z3jlAWbfdfaVsrCygIiICIiAiIgIiICIiAiIgIiICIiAoJI3RvMsIuT47PK/lTog8RyNkYHMNwV\n7UEkbo3mWEXJ8dnlfypI5GyMDmG4KD2iIgIiICIsIPE0QlaNS1zdWuG0FeYpS4mKUZZANQNhHOFm\nCphqWudBI2QNOU5TsKzNEJQNcrm6tcNoKDi6rg0+HhNDVxjbIXGw0dofeu4UMUmd2SVoErNbbj1h\nTICiqPmx99vxBSqKo+bH32/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      "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 0x1c9101cbb38>"
      ]
     },
     "execution_count": 15,
     "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": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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X2q2WdeNj/qUW4zFZRYRJNDC6MRWs4kOf2K1LW1bb2ZEQOIPaq+HS+XBI4iWL/wCTdxWh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\nouSAOtdOULvJYSOOxBIiifI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      "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 0x1c9101cb6a0>"
      ]
     },
     "execution_count": 16,
     "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": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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J23bzDm69qjesROoexO1u98jgYpIwwyNIaWuuL29AXWJ7ZImubut6upatikMj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\nIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIg/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 0x1c9101cbb70>"
      ]
     },
     "execution_count": 17,
     "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": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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Hjce2/qlqrRNW2NXJrecRKk7pOHmpLZHQBF5vbAlsuKGUBp8FF4y4IOc5Zwtn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\nBERAREQEREBERB//2Q==\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 0x1c9101cb860>"
      ]
     },
     "execution_count": 18,
     "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\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.6.1"
  },
  "livereveal": {
   "scroll": true,
   "start_slideshow_at": "selected"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}