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    "# SYDE 556/750: Simulating Neurobiological Systems\n",
    "\n",
    "\n",
    "## Lecture 10: Learning"
   ]
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    "## Learning\n",
    "\n",
    "- What do we mean by learning?\n",
    "    - When we use an integrator to keep track of location, is that learning?\n",
    "    - What about the learning used to complete a pattern in the Raven's Progressive Matrices task?\n",
    "    - Neither of these require any connection weights to change in the model\n",
    "    - But both allow future performance to be affected by past performance\n",
    "    - I suggest the term 'adaptation' to capture all such future-affected-by-past phenomena\n",
    "\n",
    "- So, we'll stick with a simple definition of learning\n",
    "    - Changing connection weights between groups of neurons"
   ]
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    "- Why might we want to change connection weights?\n",
    "- This is what traditional neural network approaches do\n",
    "    - Change connection weights until it performs the desired task\n",
    "    - Once it's doing the task, stop changing the weights\n",
    "- But we have a method for just solving for the optimal connection weights\n",
    "    - So why bother learning?"
   ]
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    "### Why learning might be useful\n",
    "\n",
    "- We might not know the function at the beginning of the task\n",
    "    - Example: a creature explores its environment and learns that eating red objects is bad, but eating green objects is good\n",
    "        - what are the inputs and outputs here?\n",
    "- The desired function might change\n",
    "    - Example: an ensemble whose input is a desired hand position, but the output is the muscle tension (or joint angles) needed to get there\n",
    "        - why would this change?\n",
    "- The optimal weights we solve for might not be optimal\n",
    "    - How could they not be optimal?\n",
    "    - What assumptions are we making?"
   ]
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    "### The simplest approach\n",
    "\n",
    "- What's the easiest way to deal with this, given what we know?\n",
    "- If we need new decoders\n",
    "    - Let's solve for them while the model's running\n",
    "    - Gather data to build up our $\\Gamma$ and $\\Upsilon$ matrices"
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    "\n",
    "- Example: eating red but not green objects\n",
    "    - Decoder from state to $Q$ value (utility of action) for eating\n",
    "    - State is some high-dimensional vector that includes the colour of what we're looking for\n",
    "         - And probably some other things, like whether it's small enough to be eaten\n",
    "    - Initially doesn't use colour to get output\n",
    "    - But we might experience a few bad outcomes after red, and good after green\n",
    "    - These become new $x$ samples, with corresponding $f(x)$ outputs\n",
    "    - Gather a few, recompute decoder\n",
    "        - Could even do this after every timestep\n",
    "- Example: converting hand position to muscle commands\n",
    "    - Send random signals to muscles\n",
    "    - Observe hand position\n",
    "    - Use that to train decoders\n",
    "- Example: going from optimal to even more optimal\n",
    "    - As the model runs, we gather $x$ values  \n",
    "    - Recompute decoder for those $x$ values\n",
    "    "
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    "### What's wrong with this approach\n",
    "\n",
    "- Feels like cheating\n",
    "- Why?\n",
    "- Two kinds of problems:\n",
    "    - Not biologically realistic\n",
    "        - How are neurons supposed to do all this?\n",
    "        - store data\n",
    "        - solve decoders\n",
    "        - timing\n",
    "    - Computationally expensive\n",
    "        - Even if we're not worried about realism"
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    "## Traditional neural networks\n",
    "\n",
    "- Traditionally, learning is the main method of constructing a model network\n",
    "- Usually incremental learning (gradient descent)\n",
    "    - As you get examples, shift the connection weights slightly based on that example\n",
    "    - Don't have to consider all the data when making an update\n",
    "- Example: Perceptron learning (1957)\n",
    "    - $\\Delta w_j = \\alpha(y_d - y)x_i$\n",
    "\n",
    "<img src=\"files/lecture_learning/perceptron.png\">\n",
    "\n",
    "- Problems with perceptron\n",
    "    - Can't do all possible functions\n",
    "    - Effectively just linear functions of $x$ (with a threshold; i.e. a linear classifier)\n",
    "    - Is that a problem (X)OR not?\n",
    "    \n"
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    "## Backprop and the NEF\n",
    "\n",
    "- How are nonlinear functions included?\n",
    "    - Multiple layers\n",
    "    \n",
    "<img src=\"files/lecture_learning/backprop.png\" width=\"600\">\n",
    "    \n",
    "- But now a new rule is needed\n",
    "    - Standard answer: backprop\n",
    "    - Same as perceptron for first (output) layer\n",
    "    - Backprop adds: Estimate correct \"hidden layer\" input, and repeat\n"
   ]
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    "- What would this be in NEF terms?\n",
    "- Remember that we're already fine with linear decoding\n",
    "    - Encoders (and $\\alpha$ and $J^{bias}$) are input layer of weights, decoders are output layer\n",
    "    - Note that in the NEF, we combine many of these layers together\n",
    "- We can just use the standard perceptron rule for decoders\n",
    "    - As long as there are lots of neurons, and we've initialized them well with the desired intercepts, maximum rates, and encoders we should be able to decode lots of functions\n",
    "    - So, what might backprop add to that?\n",
    "        - Think about encoders"
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    "## Biologically realistic perceptron learning\n",
    "\n",
    "-  [(MacNeil & Eliasmith, 2011)](http://compneuro.uwaterloo.ca/publications/macneil2011.html)  derive a simple, plausible learning rule starting with a delta rule\n",
    "- $E = 1/2 \\int (x-\\hat{x})^2 dx$\n",
    "- $\\delta E/\\delta d_i = (x-\\hat{x})a_i$ (as usual for finding decoders)\n",
    "- So, to move down the gradient:\n",
    "    - $\\Delta d_i = -\\kappa (x - \\hat{x})a_i$ (NEF notation)\n",
    "    - $\\Delta d_i = \\kappa (y_d - y)a_i$ (the standard perceptron/delta rule)\n"
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    "- How do we make it realistic?\n",
    "- Decoders don't exist in the brain\n",
    "    - Need weights\n",
    "- The NEF tells us:\n",
    "    - $\\omega_{ij} = \\alpha_j d_i \\cdot e_j$\n",
    "    - $\\Delta \\omega_{ij} = \\alpha_j \\kappa (y_d - y)a_i \\cdot e_j$\n",
    "- Let's write $(y_d - y)$ as $E$ (for error)\n",
    "    - $\\Delta \\omega_{ij} = \\alpha_j \\kappa a_i E \\cdot e_j$\n",
    "    - $\\Delta \\omega_{ij} = \\kappa a_i (\\alpha_j E \\cdot e_j)$\n",
    "- What's $\\alpha_j E \\cdot e_j$?\n",
    "    - That's the current that this neuron would get if it had $E$ as an input\n",
    "    - But we don't want this current to drive the neuron\n",
    "    - Rather, we want it to change the weight\n",
    "    - It's a *modulatory* input\n"
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    "- This is the \"Prescribed Error Sensitivity\" PES rule\n",
    "    - Any model in the NEF could use this instead of computing decoders\n",
    "    - Requires some other neural group computing the error $E$\n",
    "    - Used in Spaun for Q-value learning (reinforcement task)\n",
    "    - Can even be used to learn circular convolution\n",
    "        - Only demonstrated up to 3 dimensions in [(Bekolay et al, 2013)](http://compneuro.uwaterloo.ca/publications/bekolay2013.html)\n",
    "        - Why not more? Patience.\n"
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    "- Is this realistic?\n",
    "    - Local information only\n",
    "    - Need an error signal\n",
    "        - Does it look like anything like this happens in the brain?\n",
    "        - Yes\n",
    "            - Retinal slip error is computed in oculomotor system\n",
    "            - Dopamine seems to act as prediction error \n",
    "    - Weight changes proportional to pre-synaptic activity and post-synaptic activity (Hebbian rule)"
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      "Populating the interactive namespace from numpy and matplotlib\n"
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    "#From the learning examples in Nengo - a Communication Channel\n",
    "%pylab inline\n",
    "import nengo\n",
    "from nengo.processes import WhiteSignal\n",
    "\n",
    "model = nengo.Network('Learn a Communication Channel')\n",
    "with model:\n",
    "    stim = nengo.Node(output=WhiteSignal(10, high=5, rms=0.5), size_out=2)\n",
    "    \n",
    "    pre = nengo.Ensemble(60, dimensions=2)\n",
    "    post = nengo.Ensemble(60, dimensions=2)\n",
    "    \n",
    "    nengo.Connection(stim, pre)\n",
    "    conn = nengo.Connection(pre, post, function=lambda x: np.random.random(2))\n",
    "    \n",
    "    inp_p = nengo.Probe(stim)\n",
    "    pre_p = nengo.Probe(pre, synapse=0.01)\n",
    "    post_p = nengo.Probe(post, synapse=0.01)\n",
    "\n",
    "sim = nengo.Simulator(model)\n",
    "#sim.run(10.0)    "
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   "source": [
    "from nengo_gui.ipython import IPythonViz\n",
    "IPythonViz(model,'configs/pre_learn.py.cfg')"
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d9dbbe0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t=sim.trange()\n",
    "\n",
    "figure(figsize=(12, 8))\n",
    "subplot(2, 1, 1)\n",
    "plot(t, sim.data[inp_p].T[0], c='k', label='Input')\n",
    "plot(t, sim.data[pre_p].T[0], c='b', label='Pre')\n",
    "plot(t, sim.data[post_p].T[0], c='r', label='Post')\n",
    "ylabel(\"Dimension 1\")\n",
    "legend(loc='best')\n",
    "title('Random function computation')\n",
    "    \n",
    "subplot(2, 1, 2)\n",
    "plot(t, sim.data[inp_p].T[1], c='k', label='Input')\n",
    "plot(t, sim.data[pre_p].T[1], c='b', label='Pre')\n",
    "plot(t, sim.data[post_p].T[1], c='r', label='Post')\n",
    "ylabel(\"Dimension 2\")\n",
    "legend(loc='best');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                <div id=\"9e39dbeb-f28a-4f28-a01b-94fe26c87974\" style=\"\n",
       "                    width: 100%;\n",
       "                    border: 1px solid #cfcfcf;\n",
       "                    border-radius: 4px;\n",
       "                    text-align: center;\n",
       "                    position: relative;\">\n",
       "                  <div class=\"pb-text\" style=\"\n",
       "                      position: absolute;\n",
       "                      width: 100%;\">\n",
       "                    0%\n",
       "                  </div>\n",
       "                  <div class=\"pb-fill\" style=\"\n",
       "                      background-color: #bdd2e6;\n",
       "                      width: 0%;\">\n",
       "                    <style type=\"text/css\" scoped=\"scoped\">\n",
       "                        @keyframes pb-fill-anim {\n",
       "                            0% { background-position: 0 0; }\n",
       "                            100% { background-position: 100px 0; }\n",
       "                        }\n",
       "                    </style>\n",
       "                    &nbsp;\n",
       "                  </div>\n",
       "                </div>"
      ],
      "text/plain": [
       "HtmlProgressBar cannot be displayed. Please use the TerminalProgressBar. It can be enabled with `nengo.rc.set('progress', 'progress_bar', 'nengo.utils.progress.TerminalProgressBar')`."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "              (function () {\n",
       "                  var root = document.getElementById('9e39dbeb-f28a-4f28-a01b-94fe26c87974');\n",
       "                  var text = root.getElementsByClassName('pb-text')[0];\n",
       "                  var fill = root.getElementsByClassName('pb-fill')[0];\n",
       "\n",
       "                  text.innerHTML = 'Build finished in 0:00:01.';\n",
       "                  \n",
       "            fill.style.width = '100%';\n",
       "            fill.style.animation = 'pb-fill-anim 2s linear infinite';\n",
       "            fill.style.backgroundSize = '100px 100%';\n",
       "            fill.style.backgroundImage = 'repeating-linear-gradient(' +\n",
       "                '90deg, #bdd2e6, #edf2f8 40%, #bdd2e6 80%, #bdd2e6)';\n",
       "        \n",
       "                  \n",
       "                fill.style.animation = 'none';\n",
       "                fill.style.backgroundImage = 'none';\n",
       "            \n",
       "              })();\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                <div id=\"49ebc6fd-ab4d-4694-b230-563dee8b251a\" style=\"\n",
       "                    width: 100%;\n",
       "                    border: 1px solid #cfcfcf;\n",
       "                    border-radius: 4px;\n",
       "                    text-align: center;\n",
       "                    position: relative;\">\n",
       "                  <div class=\"pb-text\" style=\"\n",
       "                      position: absolute;\n",
       "                      width: 100%;\">\n",
       "                    0%\n",
       "                  </div>\n",
       "                  <div class=\"pb-fill\" style=\"\n",
       "                      background-color: #bdd2e6;\n",
       "                      width: 0%;\">\n",
       "                    <style type=\"text/css\" scoped=\"scoped\">\n",
       "                        @keyframes pb-fill-anim {\n",
       "                            0% { background-position: 0 0; }\n",
       "                            100% { background-position: 100px 0; }\n",
       "                        }\n",
       "                    </style>\n",
       "                    &nbsp;\n",
       "                  </div>\n",
       "                </div>"
      ],
      "text/plain": [
       "HtmlProgressBar cannot be displayed. Please use the TerminalProgressBar. It can be enabled with `nengo.rc.set('progress', 'progress_bar', 'nengo.utils.progress.TerminalProgressBar')`."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "              (function () {\n",
       "                  var root = document.getElementById('49ebc6fd-ab4d-4694-b230-563dee8b251a');\n",
       "                  var text = root.getElementsByClassName('pb-text')[0];\n",
       "                  var fill = root.getElementsByClassName('pb-fill')[0];\n",
       "\n",
       "                  text.innerHTML = 'Simulation finished in 0:00:11.';\n",
       "                  \n",
       "            if (100.0 > 0.) {\n",
       "                fill.style.transition = 'width 0.1s linear';\n",
       "            } else {\n",
       "                fill.style.transition = 'none';\n",
       "            }\n",
       "\n",
       "            fill.style.width = '100.0%';\n",
       "            fill.style.animation = 'none';\n",
       "            fill.style.backgroundImage = 'none'\n",
       "        \n",
       "                  \n",
       "                fill.style.animation = 'none';\n",
       "                fill.style.backgroundImage = 'none';\n",
       "            \n",
       "              })();\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now learn\n",
    "with model:\n",
    "    error = nengo.Ensemble(60, dimensions=2)\n",
    "    error_p = nengo.Probe(error, synapse=0.03)\n",
    "    \n",
    "    # Error = actual - target = post - pre\n",
    "    nengo.Connection(post, error)\n",
    "    nengo.Connection(pre, error, transform=-1)\n",
    "    \n",
    "    # Add the learning rule to the connection\n",
    "    conn.learning_rule_type = nengo.PES()\n",
    "    \n",
    "    # Connect the error into the learning rule\n",
    "    learn_conn = nengo.Connection(error, conn.learning_rule) \n",
    "\n",
    "sim = nengo.Simulator(model)\n",
    "sim.run(10.0)     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <script type=\"text/javascript\" id=\"981ba3b7-5786-482a-9870-450b2d1219bd\">\n",
       "            {\n",
       "                let req = new XMLHttpRequest();\n",
       "                req.addEventListener(\"load\", function() {\n",
       "                    if (this.status != 200 && this.response != 'OK') {\n",
       "                        let p = document.getElementById('981ba3b7-5786-482a-9870-450b2d1219bd').parentNode;\n",
       "                        p.innerHTML +=\n",
       "                            'The nengo_gui.jupyter notebook server ' +\n",
       "                            'extension was not loaded. Please activate it ' +\n",
       "                            'with the following command:' +\n",
       "                            '<pre>jupyter serverextension enable ' +\n",
       "                            'nengo_gui.jupyter</pre>';\n",
       "                        p.classList.add('output_stderr');\n",
       "                    }\n",
       "                });\n",
       "                req.open('GET', './nengo/check', true);\n",
       "                req.send();\n",
       "            }\n",
       "            </script>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vdom.v1+json": {
       "attributes": {
        "id": "4a40339c-d7dd-4c2d-a68c-2a65c929acac"
       },
       "children": [
        {
         "attributes": {
          "allowfullscreen": "allowfullscreen",
          "class": "cell",
          "frameborder": "0",
          "height": "600",
          "src": "./nengo/52808/?token=fe104d101ebbfefda9511833b525236dc5df14e797736f1e",
          "style": {
           "border": "1px solid #eee",
           "boxSizing": "border-box"
          },
          "width": "100%"
         },
         "tagName": "iframe"
        }
       ],
       "tagName": "div"
      },
      "text/html": [
       "\n",
       "                <div id=\"314345be-5d43-4b47-9f8d-12a37ee2b894\">\n",
       "                    <iframe\n",
       "                        src=\"./nengo/52808/?token=fe104d101ebbfefda9511833b525236dc5df14e797736f1e\"\n",
       "                        width=\"100%\"\n",
       "                        height=\"600\"\n",
       "                        frameborder=\"0\"\n",
       "                        class=\"cell\"\n",
       "                        style=\"border: 1px solid #eee; box-sizing: border-box;\"\n",
       "                        allowfullscreen></iframe>\n",
       "                </div>\n",
       "            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from nengo_gui.ipython import IPythonViz\n",
    "IPythonViz(model,'configs/simple_learn.py.cfg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11d891240>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e9ea7b8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t=sim.trange()\n",
    "\n",
    "figure(figsize=(12, 8))\n",
    "subplot(3, 1, 1)\n",
    "plot(t, sim.data[inp_p].T[0], c='k', label='Input')\n",
    "plot(t, sim.data[pre_p].T[0], c='b', label='Pre')\n",
    "plot(t, sim.data[post_p].T[0], c='r', label='Post')\n",
    "ylabel(\"Dimension 1\")\n",
    "legend(loc='best')\n",
    "title('Learn a communication channel')\n",
    "    \n",
    "subplot(3, 1, 2)\n",
    "plot(t, sim.data[inp_p].T[1], c='k', label='Input')\n",
    "plot(t, sim.data[pre_p].T[1], c='b', label='Pre')\n",
    "plot(t, sim.data[post_p].T[1], c='r', label='Post')\n",
    "ylabel(\"Dimension 2\")\n",
    "legend(loc='best');\n",
    "\n",
    "subplot(3, 1, 3)\n",
    "plot(sim.trange(), sim.data[error_p], c='b')\n",
    "ylim(-1, 1)\n",
    "legend((\"Error[0]\", \"Error[1]\"), loc='best');\n",
    "title('Error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                <div id=\"4a22d3a1-2c3f-4ba7-a58d-39c883e749e9\" style=\"\n",
       "                    width: 100%;\n",
       "                    border: 1px solid #cfcfcf;\n",
       "                    border-radius: 4px;\n",
       "                    text-align: center;\n",
       "                    position: relative;\">\n",
       "                  <div class=\"pb-text\" style=\"\n",
       "                      position: absolute;\n",
       "                      width: 100%;\">\n",
       "                    0%\n",
       "                  </div>\n",
       "                  <div class=\"pb-fill\" style=\"\n",
       "                      background-color: #bdd2e6;\n",
       "                      width: 0%;\">\n",
       "                    <style type=\"text/css\" scoped=\"scoped\">\n",
       "                        @keyframes pb-fill-anim {\n",
       "                            0% { background-position: 0 0; }\n",
       "                            100% { background-position: 100px 0; }\n",
       "                        }\n",
       "                    </style>\n",
       "                    &nbsp;\n",
       "                  </div>\n",
       "                </div>"
      ],
      "text/plain": [
       "HtmlProgressBar cannot be displayed. Please use the TerminalProgressBar. It can be enabled with `nengo.rc.set('progress', 'progress_bar', 'nengo.utils.progress.TerminalProgressBar')`."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "              (function () {\n",
       "                  var root = document.getElementById('4a22d3a1-2c3f-4ba7-a58d-39c883e749e9');\n",
       "                  var text = root.getElementsByClassName('pb-text')[0];\n",
       "                  var fill = root.getElementsByClassName('pb-fill')[0];\n",
       "\n",
       "                  text.innerHTML = 'Build finished in 0:00:01.';\n",
       "                  \n",
       "            fill.style.width = '100%';\n",
       "            fill.style.animation = 'pb-fill-anim 2s linear infinite';\n",
       "            fill.style.backgroundSize = '100px 100%';\n",
       "            fill.style.backgroundImage = 'repeating-linear-gradient(' +\n",
       "                '90deg, #bdd2e6, #edf2f8 40%, #bdd2e6 80%, #bdd2e6)';\n",
       "        \n",
       "                  \n",
       "                fill.style.animation = 'none';\n",
       "                fill.style.backgroundImage = 'none';\n",
       "            \n",
       "              })();\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Turning learning on and off to test generalization\n",
    "def inhibit(t):\n",
    "    return 2.0 if t > 10.0 else 0.0\n",
    "\n",
    "with model:\n",
    "    inhib = nengo.Node(inhibit)\n",
    "    inhib_conn = nengo.Connection(inhib, error.neurons, transform=[[-1]] * error.n_neurons)\n",
    "    \n",
    "sim = nengo.Simulator(model)\n",
    "#sim.run(16.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <script type=\"text/javascript\" id=\"77ce8481-2778-4ad4-a3f5-69f3247ca9f3\">\n",
       "            {\n",
       "                let req = new XMLHttpRequest();\n",
       "                req.addEventListener(\"load\", function() {\n",
       "                    if (this.status != 200 && this.response != 'OK') {\n",
       "                        let p = document.getElementById('77ce8481-2778-4ad4-a3f5-69f3247ca9f3').parentNode;\n",
       "                        p.innerHTML +=\n",
       "                            'The nengo_gui.jupyter notebook server ' +\n",
       "                            'extension was not loaded. Please activate it ' +\n",
       "                            'with the following command:' +\n",
       "                            '<pre>jupyter serverextension enable ' +\n",
       "                            'nengo_gui.jupyter</pre>';\n",
       "                        p.classList.add('output_stderr');\n",
       "                    }\n",
       "                });\n",
       "                req.open('GET', './nengo/check', true);\n",
       "                req.send();\n",
       "            }\n",
       "            </script>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vdom.v1+json": {
       "attributes": {
        "id": "2d9d4ee2-1a5e-4ac4-b9c0-a755025c383f"
       },
       "children": [
        {
         "attributes": {
          "allowfullscreen": "allowfullscreen",
          "class": "cell",
          "frameborder": "0",
          "height": "600",
          "src": "./nengo/53129/?token=776920da55c55576668c1f8586b17efaec6277750de647a6",
          "style": {
           "border": "1px solid #eee",
           "boxSizing": "border-box"
          },
          "width": "100%"
         },
         "tagName": "iframe"
        }
       ],
       "tagName": "div"
      },
      "text/html": [
       "\n",
       "                <div id=\"3c9f889c-b747-46df-ab2d-ed4815ee7643\">\n",
       "                    <iframe\n",
       "                        src=\"./nengo/53129/?token=776920da55c55576668c1f8586b17efaec6277750de647a6\"\n",
       "                        width=\"100%\"\n",
       "                        height=\"600\"\n",
       "                        frameborder=\"0\"\n",
       "                        class=\"cell\"\n",
       "                        style=\"border: 1px solid #eee; box-sizing: border-box;\"\n",
       "                        allowfullscreen></iframe>\n",
       "                </div>\n",
       "            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from nengo_gui.ipython import IPythonViz\n",
    "IPythonViz(model,'configs/control_learn.py.cfg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x110fb6fd0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tn0qoVdBut5OaejwAz3F9WOVr61YP9903kxtuOJfly62t4dHE4XBQLQN+00l6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\nWEAuK0uzDMoIkmqXlYW36ic3a0a8FV3IgjekRnv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Pioo1o3tDsjEPsrKyki+e/JzrUQvg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owCnpRSniqEmAS8gZ6SkgH8W0p5f4L33CMi3QCrVq1ixIgRjBkzJtWmKBSDmqBcoEov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\ntYf1CtdUDo8Z43a7mUHYIytf2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jEmsz/9aTjx/fd6Ln20EkIkwS6sRVjP2Nvb2wGj+PeG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tJFRH4jIkvrztHR1W5Ta0ZEzqsrUrZIRKbVyfhIiYjInSKyRkQWReu6icgTIrJMRP4oIl2q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\nojXZRuyEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e8b2650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t=sim.trange()\n",
    "\n",
    "figure(figsize=(12, 8))\n",
    "subplot(3, 1, 1)\n",
    "plot(t, sim.data[inp_p].T[0], c='k', label='Input')\n",
    "plot(t, sim.data[pre_p].T[0], c='b', label='Pre')\n",
    "plot(t, sim.data[post_p].T[0], c='r', label='Post')\n",
    "ylabel(\"Dimension 1\")\n",
    "legend(loc='best')\n",
    "title('Learn a communication channel')\n",
    "    \n",
    "subplot(3, 1, 2)\n",
    "plot(t, sim.data[inp_p].T[1], c='k', label='Input')\n",
    "plot(t, sim.data[pre_p].T[1], c='b', label='Pre')\n",
    "plot(t, sim.data[post_p].T[1], c='r', label='Post')\n",
    "ylabel(\"Dimension 2\")\n",
    "legend(loc='best');\n",
    "\n",
    "subplot(3, 1, 3)\n",
    "plot(sim.trange(), sim.data[error_p], c='b')\n",
    "ylim(-1, 1)\n",
    "legend((\"Error[0]\", \"Error[1]\"), loc='best');\n",
    "title('Error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                <div id=\"61a07815-9e9d-43fa-bc60-6dfe9ae01141\" style=\"\n",
       "                    width: 100%;\n",
       "                    border: 1px solid #cfcfcf;\n",
       "                    border-radius: 4px;\n",
       "                    text-align: center;\n",
       "                    position: relative;\">\n",
       "                  <div class=\"pb-text\" style=\"\n",
       "                      position: absolute;\n",
       "                      width: 100%;\">\n",
       "                    0%\n",
       "                  </div>\n",
       "                  <div class=\"pb-fill\" style=\"\n",
       "                      background-color: #bdd2e6;\n",
       "                      width: 0%;\">\n",
       "                    <style type=\"text/css\" scoped=\"scoped\">\n",
       "                        @keyframes pb-fill-anim {\n",
       "                            0% { background-position: 0 0; }\n",
       "                            100% { background-position: 100px 0; }\n",
       "                        }\n",
       "                    </style>\n",
       "                    &nbsp;\n",
       "                  </div>\n",
       "                </div>"
      ],
      "text/plain": [
       "HtmlProgressBar cannot be displayed. Please use the TerminalProgressBar. It can be enabled with `nengo.rc.set('progress', 'progress_bar', 'nengo.utils.progress.TerminalProgressBar')`."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "              (function () {\n",
       "                  var root = document.getElementById('61a07815-9e9d-43fa-bc60-6dfe9ae01141');\n",
       "                  var text = root.getElementsByClassName('pb-text')[0];\n",
       "                  var fill = root.getElementsByClassName('pb-fill')[0];\n",
       "\n",
       "                  text.innerHTML = 'Build finished in 0:00:01.';\n",
       "                  \n",
       "            fill.style.width = '100%';\n",
       "            fill.style.animation = 'pb-fill-anim 2s linear infinite';\n",
       "            fill.style.backgroundSize = '100px 100%';\n",
       "            fill.style.backgroundImage = 'repeating-linear-gradient(' +\n",
       "                '90deg, #bdd2e6, #edf2f8 40%, #bdd2e6 80%, #bdd2e6)';\n",
       "        \n",
       "                  \n",
       "                fill.style.animation = 'none';\n",
       "                fill.style.backgroundImage = 'none';\n",
       "            \n",
       "              })();\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Compute a nonlinear functions\n",
    "#model.connections.remove(err_fcn) #uncomment to try other fcns\n",
    "#del err_fcn\n",
    "model.connections.remove(inhib_conn)\n",
    "del inhib_conn\n",
    "model.nodes.remove(inhib)\n",
    "model.connections.remove(learn_conn)\n",
    "del learn_conn\n",
    "\n",
    "def nonlinear(x):\n",
    "    return x[0]*x[0], x[1]*x[1]\n",
    "\n",
    "with model:\n",
    "    err_fcn = nengo.Connection(pre, error, function=nonlinear, transform=-1)\n",
    "    \n",
    "    conn.learning_rule_type = nengo.PES(learning_rate=1e-4)\n",
    "    # Connect the error into the learning rule\n",
    "    learn_conn = nengo.Connection(error, conn.learning_rule)    \n",
    "    \n",
    "sim = nengo.Simulator(model)\n",
    "#sim.run(26.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <script type=\"text/javascript\" id=\"8809ce62-5121-480f-97cb-4ce8f0083c05\">\n",
       "            {\n",
       "                let req = new XMLHttpRequest();\n",
       "                req.addEventListener(\"load\", function() {\n",
       "                    if (this.status != 200 && this.response != 'OK') {\n",
       "                        let p = document.getElementById('8809ce62-5121-480f-97cb-4ce8f0083c05').parentNode;\n",
       "                        p.innerHTML +=\n",
       "                            'The nengo_gui.jupyter notebook server ' +\n",
       "                            'extension was not loaded. Please activate it ' +\n",
       "                            'with the following command:' +\n",
       "                            '<pre>jupyter serverextension enable ' +\n",
       "                            'nengo_gui.jupyter</pre>';\n",
       "                        p.classList.add('output_stderr');\n",
       "                    }\n",
       "                });\n",
       "                req.open('GET', './nengo/check', true);\n",
       "                req.send();\n",
       "            }\n",
       "            </script>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vdom.v1+json": {
       "attributes": {
        "id": "5b81f377-ab69-40e9-8fd9-5f02279bd0cd"
       },
       "children": [
        {
         "attributes": {
          "allowfullscreen": "allowfullscreen",
          "class": "cell",
          "frameborder": "0",
          "height": "600",
          "src": "./nengo/53324/?token=7ade327e175507ed7c8684635dc83682e2c53f309cf8683a",
          "style": {
           "border": "1px solid #eee",
           "boxSizing": "border-box"
          },
          "width": "100%"
         },
         "tagName": "iframe"
        }
       ],
       "tagName": "div"
      },
      "text/html": [
       "\n",
       "                <div id=\"b8d88fa1-da2c-49e4-9f35-e5d952c6df8d\">\n",
       "                    <iframe\n",
       "                        src=\"./nengo/53324/?token=7ade327e175507ed7c8684635dc83682e2c53f309cf8683a\"\n",
       "                        width=\"100%\"\n",
       "                        height=\"600\"\n",
       "                        frameborder=\"0\"\n",
       "                        class=\"cell\"\n",
       "                        style=\"border: 1px solid #eee; box-sizing: border-box;\"\n",
       "                        allowfullscreen></iframe>\n",
       "                </div>\n",
       "            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from nengo_gui.ipython import IPythonViz\n",
    "IPythonViz(model,'configs/square_learn.py.cfg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x116211850>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VT+AhhS/8MqyKE+BTxpHG86Yqb0bB2Rvgm3zSejF3lBPyUdNL7GDC9m5qWi4B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Vxj11wLA7iFnT374g7+9TJAv3aGfowj/+ks08KTawCmqbTVxGjoBGJxImzH+nA/O0wa6FjZldV3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f1se+EO9Gy5fAfJema3t1N6ZjBEJ7FpiG8DDbqePIsOc6aIBVd/oOubCaxBLPDtjK7f/0HP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\ndT3/717oTfCSeP93HFO9b63jPdUf74qWjrcuO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      "text/plain": [
       "<matplotlib.figure.Figure at 0x115f60ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t=sim.trange()\n",
    "\n",
    "figure(figsize=(12, 8))\n",
    "subplot(3, 1, 1)\n",
    "plot(t, sim.data[inp_p].T[0], c='k', label='Input')\n",
    "plot(t, sim.data[pre_p].T[0], c='b', label='Pre')\n",
    "plot(t, sim.data[post_p].T[0], c='r', label='Post')\n",
    "ylabel(\"Dimension 1\")\n",
    "legend(loc='best')\n",
    "title('Learn a nonlinear function')\n",
    "    \n",
    "subplot(3, 1, 2)\n",
    "plot(t, sim.data[inp_p].T[1], c='k', label='Input')\n",
    "plot(t, sim.data[pre_p].T[1], c='b', label='Pre')\n",
    "plot(t, sim.data[post_p].T[1], c='r', label='Post')\n",
    "ylabel(\"Dimension 2\")\n",
    "legend(loc='best');\n",
    "\n",
    "subplot(3, 1, 3)\n",
    "plot(sim.trange(), sim.data[error_p], c='b')\n",
    "ylim(-1, 1)\n",
    "legend((\"Error[0]\", \"Error[1]\"), loc='best');\n",
    "title('Error')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "- This rule can be used to learn any nonlinear vector function\n",
    "- It does as well, or better, than the typical NEF decoder optimization\n",
    "- It's a 'spike-based' rule... meaning it works in a spiking network\n",
    "- It has been used for 'constant supervision' as well as 'reinforcement learning' (occasional supervision) tasks (Spaun uses it for the RL task)\n",
    "- It moves the focus of learning research from weight changes or 'learning rules' to error signals\n",
    "- Backprop is one way of propagating error signals (unfortunately not bio-plausible)\n",
    "- Pretty much ignores encoders (which should maybe be about capturing all the incoming information, so as to compute any function over that information... though they can be optimized for a given fcn as well)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Applications of PES\n",
    "### Classical conditioning\n",
    "\n",
    "- Classical or Pavlovian conditioning uses an unconditioned stimuli (US) (meat for a dog) that ellicits an unconditioned response (UR) (salivating) to cause a conditioned response (CR) (salivating after learning) to be ellicited by a conditioned stimulus (CS) (ringing a bell).\n",
    "\n",
    "- The best known model of this is the Rescorla-Wagner model that states:\n",
    "\n",
    "$\\Delta V_x = \\alpha (\\lambda - \\sum_x V)$ \n",
    "\n",
    "where $V_x$ is the value of conditioned stimulus $x$, $\\alpha$ is a learning rate and salience parameter, $\\lambda$ is the max value (usually 1).  \n",
    "\n",
    "- In the model below there is only 1 element in $\\sum V$ (or you can assume there is little association between other stimuli and the US). The difference in brackets is like a reward prediction error."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "In this model: \n",
    "- There are three different US that are provided\n",
    "to the model, one after the other.  \n",
    "- Each has a different hardwired\n",
    "UR.\n",
    "- There is also a CS provided (a different\n",
    "one for each US)  \n",
    "- The model attempts to learn to trigger the correct\n",
    "CR in response to the CS.  \n",
    "- After learning, the CR should start to respond *before* the corresponding UR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "import nengo\n",
    "import numpy as np\n",
    "\n",
    "D = 3\n",
    "N = D*50\n",
    "\n",
    "def us_stim(t):\n",
    "    # cycle through the three US\n",
    "    t = t % 3\n",
    "    if 0.9 < t< 1: return [1, 0, 0]\n",
    "    if 1.9 < t< 2: return [0, 1, 0]\n",
    "    if 2.9 < t< 3: return [0, 0, 1]\n",
    "    return [0, 0, 0]\n",
    "\n",
    "def cs_stim(t):\n",
    "    # cycle through the three CS\n",
    "    t = t % 3\n",
    "    if 0.7 < t< 1: return [0.7, 0, 0.5]\n",
    "    if 1.7 < t< 2: return [0.6, 0.7, 0.8]\n",
    "    if 2.7 < t< 3: return [0, 1, 0]\n",
    "    return [0, 0, 0]\n",
    "\n",
    "model = nengo.Network(label=\"Classical Conditioning\")\n",
    "with model:\n",
    "    us_stim = nengo.Node(us_stim)\n",
    "    cs_stim = nengo.Node(cs_stim)\n",
    "\n",
    "    us = nengo.Ensemble(N, D)\n",
    "    cs = nengo.Ensemble(N*2, D*2)\n",
    "\n",
    "    nengo.Connection(us_stim, us[:D])\n",
    "    nengo.Connection(cs_stim, cs[:D])\n",
    "    nengo.Connection(cs[:D], cs[D:], synapse=0.2)\n",
    "\n",
    "    ur = nengo.Ensemble(N, D)\n",
    "    nengo.Connection(us, ur)\n",
    "    \n",
    "    cr = nengo.Ensemble(N, D)\n",
    "    learn_conn = nengo.Connection(cs, cr, function=lambda x: [0]*D)\n",
    "    learn_conn.learning_rule_type = nengo.PES(learning_rate=3e-4)\n",
    "\n",
    "    error = nengo.Ensemble(N, D)\n",
    "    nengo.Connection(error, learn_conn.learning_rule)\n",
    "    nengo.Connection(ur, error, transform=-1)\n",
    "    nengo.Connection(cr, error, transform=1, synapse=0.1)\n",
    "\n",
    "    stop_learn = nengo.Node([0])\n",
    "    nengo.Connection(stop_learn, error.neurons, transform=-10*np.ones((N, 1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
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       "\n",
       "                <div id=\"588d9a60-04c0-462d-acab-f24907f6fc7e\">\n",
       "                    <iframe\n",
       "                        src=\"./nengo/53777/?token=9168c25af39247838d4bfbcc1463577b21894b29bbb076b8\"\n",
       "                        width=\"100%\"\n",
       "                        height=\"600\"\n",
       "                        frameborder=\"0\"\n",
       "                        class=\"cell\"\n",
       "                        style=\"border: 1px solid #eee; box-sizing: border-box;\"\n",
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       "                </div>\n",
       "            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from nengo_gui.ipython import IPythonViz\n",
    "IPythonViz(model,'configs/learning2-conditioning.py.cfg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Cortical Consolidation\n",
    "\n",
    "- There is evidence that when you first learn a skill, it takes a lot of effort and you tend to perform fairly slowly.  \n",
    "- We would think of this as requiring a lot of intervention from the basal ganglia in selecting actions.  \n",
    "- As you get better at the skill you become much faster, and BG is used less because cortex 'takes over' cental aspects of that skill, consolidating it into cortico-cortical connections.  \n",
    "- The next model shows a toy version of this kind of behaviour.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "In this model:\n",
    "- there is a slow mapping from pre->wm->target (because of long synaptic time constants) \n",
    "- there is a fast, direct connection from pre->post \n",
    "- the fast connection is trained using the error signal from the slow system\n",
    "- the fast system learns to produce the correct output before the slow system\n",
    "- if you change the 'context' the fast system will learn the output\n",
    "- there is a more complete model that uses this kind of thing (but with BG) in [Aubin et al., 2016](http://compneuro.uwaterloo.ca/publications/aubin2016a.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "import nengo\n",
    "import numpy as np\n",
    "\n",
    "tau_slow = 0.2\n",
    "\n",
    "model = nengo.Network(\"Cortical Consolidation\")\n",
    "with model:\n",
    "    pre_value = nengo.Node(lambda t: np.sin(t))\n",
    "    \n",
    "    pre = nengo.Ensemble(100, 1)\n",
    "    post = nengo.Ensemble(100, 1)\n",
    "    target = nengo.Ensemble(100, 1)\n",
    "    nengo.Connection(pre_value, pre)\n",
    "\n",
    "    conn = nengo.Connection(pre, post, function=lambda x: np.random.random(),\n",
    "                learning_rule_type=nengo.PES())\n",
    "    \n",
    "    wm = nengo.Ensemble(300, 2, radius=1.4)\n",
    "    context = nengo.Node(1)\n",
    "    nengo.Connection(context, wm[1])\n",
    "    nengo.Connection(pre, wm[0], synapse=tau_slow)\n",
    "    \n",
    "    nengo.Connection(wm, target, synapse=tau_slow, \n",
    "                     function=lambda x: x[0]*x[1])\n",
    "                     \n",
    "    error = nengo.Ensemble(n_neurons=100, dimensions=1)\n",
    "    nengo.Connection(post, error, synapse=tau_slow*2, transform=1) #Delay the fast connection so they line up\n",
    "    nengo.Connection(target, error, transform=-1)\n",
    "    \n",
    "    nengo.Connection(error, conn.learning_rule)\n",
    "\n",
    "    stop_learn = nengo.Node([0])\n",
    "    nengo.Connection(stop_learn, error.neurons, transform=-10*np.ones((100,1)))\n",
    "    \n",
    "    both = nengo.Node(None, size_in=2) #For plotting\n",
    "    nengo.Connection(post, both[0], synapse=None)\n",
    "    nengo.Connection(target, both[1], synapse=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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       "                </div>\n",
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   ],
   "source": [
    "from nengo_gui.ipython import IPythonViz\n",
    "IPythonViz(model,'configs/learning3-consolidation.py.cfg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Reinforcement Learning - Do evaluations!!!!  Brain Day!\n",
    "\n",
    "- As mentioned in the last lecture, RL is a useful way to think about action selection.  \n",
    "- You have a set of actions and a set of states, and you figure out the value of each action in each state, letting you construct a big table $Q(s,a)$ which you can use to pick good actions.  \n",
    "- RL figures out what those values are through trial and error. (This is SARSA.)  \n",
    "\n",
    "$\\Delta Q(s,a) = \\alpha (R + \\gamma Q_{predicted} - Q_{old})$  where $R$ is reward, $\\alpha$ is a learning rate and $\\gamma$ is a discount factor.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "In the model:\n",
    "- the agent has three actions (go forward, turn left, and turn right)\n",
    "- its only sense are five range finders (radar)  \n",
    "- initially it should always go forward\n",
    "- it gets reward proportial to its forward speed, but a large negative reward for hitting walls\n",
    "- the error signal is simply the difference between the computed utility and the instantaneous reward\n",
    "- $\\Delta Q(s,a) = \\alpha (R - Q_{current})$\n",
    "- this error will only be applied to whatever action is currently being chosen, which means it cannot learn to do actions that will lead to *future* rewards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "import grid\n",
    "\n",
    "mymap=\"\"\"\n",
    "#########\n",
    "#       #\n",
    "#       #\n",
    "#   ##  #\n",
    "#   ##  #\n",
    "#       #\n",
    "#########\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "class Cell(grid.Cell):\n",
    "    def color(self):\n",
    "        return 'black' if self.wall else None\n",
    "    def load(self, char):\n",
    "        if char == '#':\n",
    "            self.wall = True\n",
    "\n",
    "world = grid.World(Cell, map=mymap, directions=4)\n",
    "\n",
    "body = grid.ContinuousAgent()\n",
    "world.add(body, x=1, y=3, dir=2)\n",
    "\n",
    "import nengo\n",
    "import numpy as np    \n",
    "\n",
    "def move(t, x):\n",
    "    speed, rotation = x\n",
    "    dt = 0.001\n",
    "    max_speed = 20.0\n",
    "    max_rotate = 10.0\n",
    "    body.turn(rotation * dt * max_rotate)\n",
    "    success = body.go_forward(speed * dt * max_speed)\n",
    "    if not success: #Hit a wall\n",
    "        return -1\n",
    "    else:\n",
    "        return speed\n",
    "\n",
    "model = nengo.Network(\"Simple RL\", seed=2)\n",
    "with model:\n",
    "    env = grid.GridNode(world, dt=0.005)\n",
    "    \n",
    "    #set up node to project movement commands to\n",
    "    movement_node = nengo.Node(move, size_in=2, label='reward')\n",
    "    movement = nengo.Ensemble(n_neurons=100, dimensions=2, radius=1.4)    \n",
    "    nengo.Connection(movement, movement_node)\n",
    "\n",
    "    def detect(t):\n",
    "        #put 5 sensors between -45 to 45 compared to facing direction\n",
    "        angles = (np.linspace(-0.5, 0.5, 5) + body.dir ) % world.directions\n",
    "        return [body.detect(d, max_distance=4)[0] for d in angles]\n",
    "    stim_radar = nengo.Node(detect)\n",
    "\n",
    "    #set up low fidelity sensors; noise might help exploration\n",
    "    radar = nengo.Ensemble(n_neurons=50, dimensions=5, radius=4)\n",
    "    nengo.Connection(stim_radar, radar)\n",
    "    \n",
    "    #set up BG to allow 3 actions (left/fwd/right)\n",
    "    bg = nengo.networks.actionselection.BasalGanglia(3)\n",
    "    thal = nengo.networks.actionselection.Thalamus(3)\n",
    "    nengo.Connection(bg.output, thal.input)\n",
    "    \n",
    "    #start with a kind of random selection process, but like going fwd most\n",
    "    def u_fwd(x):\n",
    "        return 0.8\n",
    "    def u_left(x):\n",
    "        return 0.6\n",
    "    def u_right(x):\n",
    "        return 0.7\n",
    "\n",
    "    conn_fwd = nengo.Connection(radar, bg.input[0], function=u_fwd, learning_rule_type=nengo.PES())\n",
    "    conn_left = nengo.Connection(radar, bg.input[1], function=u_left, learning_rule_type=nengo.PES())\n",
    "    conn_right = nengo.Connection(radar, bg.input[2], function=u_right, learning_rule_type=nengo.PES())\n",
    "        \n",
    "    nengo.Connection(thal.output[0], movement, transform=[[1],[0]])\n",
    "    nengo.Connection(thal.output[1], movement, transform=[[0],[1]])\n",
    "    nengo.Connection(thal.output[2], movement, transform=[[0],[-1]])\n",
    "    \n",
    "    errors = nengo.networks.EnsembleArray(n_neurons=50, n_ensembles=3)\n",
    "    nengo.Connection(movement_node, errors.input, transform=-np.ones((3,1)))\n",
    "    #inhibit learning for actions not currently chosen (recall BG is high for non-chosen actions)\n",
    "    nengo.Connection(bg.output[0], errors.ensembles[0].neurons, transform=np.ones((50,1))*4)    \n",
    "    nengo.Connection(bg.output[1], errors.ensembles[1].neurons, transform=np.ones((50,1))*4)    \n",
    "    nengo.Connection(bg.output[2], errors.ensembles[2].neurons, transform=np.ones((50,1))*4)    \n",
    "    nengo.Connection(bg.input, errors.input, transform=1)\n",
    "    \n",
    "    nengo.Connection(errors.ensembles[0], conn_fwd.learning_rule)\n",
    "    nengo.Connection(errors.ensembles[1], conn_left.learning_rule)\n",
    "    nengo.Connection(errors.ensembles[2], conn_right.learning_rule)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "from nengo_gui.ipython import IPythonViz\n",
    "IPythonViz(model,'configs/learning5-utility.py.cfg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Better RL\n",
    "\n",
    "- To improve our RL it would be good to predict future rewards more accurately. \n",
    "    - It would be good to learn the function $Q(s,a)$. \n",
    "- Let's assume that your policy is fixed, so future actions are fixed.  \n",
    "- As well, future rewards are 90% as good as current rewards (i.e. they are discounted).  \n",
    "- Consequently, we have:\n",
    "\n",
    "$Q(s,t) = R(s,t) + 0.9 R(s+1, t+1) + 0.9^2 R(s+2, t+2) + ...$.\n",
    "\n",
    "- So also,\n",
    "\n",
    "$Q(s+1,t+1) = R(s+1,t+1) + 0.9 R(s+2, t+2) + 0.9^2 R(s+3, t+3) + ...$.\n",
    "$0.9 Q(s+1,t+1) = 0.9 R(s+1,t+1) + 0.9^2 R(s+2, t+2) + 0.9^3 R(s+3, t+3) + ...$.\n",
    "\n",
    "- Substituting this last equation into the first gives \n",
    "\n",
    "$Q(s,t) = R(s,t) + 0.9 Q(s+1, t+1)$.\n",
    "\n",
    "- This suggests an error rule: \n",
    "\n",
    "$Error(t) = Q(s-1) - (R(s-1) + 0.9 Q(s))$\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "In this model:\n",
    "- the agent always moves randomly, it's not *using* what\n",
    "it learns to change its movement (it is just trying to anticipate future rewards)\n",
    "- the agent is given a reward whenever it is in the green square, and a \n",
    "punishment (negative reward) whenever it is in the red square  \n",
    "- it learns to anticipate the reward/punishment as shown in the value graph\n",
    "- we convert the error rule into the continuous domain by using a long time constant for s-1\n",
    "and a short time constant for s (assuming we switch states at each time step)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "import grid\n",
    "mymap=\"\"\"\n",
    "#######\n",
    "#     #\n",
    "# # # #\n",
    "# # # #\n",
    "#G   R#\n",
    "#######\n",
    "\"\"\"\n",
    "\n",
    "class Cell(grid.Cell):\n",
    "    def color(self):\n",
    "        if self.wall:\n",
    "            return 'black'\n",
    "        elif self.reward > 0:\n",
    "            return 'green'\n",
    "        elif self.reward < 0:\n",
    "            return 'red'\n",
    "        return None\n",
    "    def load(self, char):\n",
    "        self.reward = 0\n",
    "        if char == '#':\n",
    "            self.wall = True\n",
    "        if char == 'G':\n",
    "            self.reward = 10\n",
    "        elif char == 'R':\n",
    "            self.reward = -10\n",
    "\n",
    "world = grid.World(Cell, map=mymap, directions=4)\n",
    "\n",
    "body = grid.ContinuousAgent()\n",
    "world.add(body, x=1, y=2, dir=2)\n",
    "\n",
    "import nengo\n",
    "import numpy as np \n",
    "\n",
    "tau=0.1\n",
    "\n",
    "def move(t, x):\n",
    "    speed, rotation = x\n",
    "    dt = 0.001\n",
    "    max_speed = 20.0\n",
    "    max_rotate = 10.0\n",
    "    body.turn(rotation * dt * max_rotate)\n",
    "    body.go_forward(speed * dt * max_speed)\n",
    "    \n",
    "    if int(body.x) == 1:\n",
    "        world.grid[4][4].wall = True\n",
    "        world.grid[4][2].wall = False\n",
    "    if int(body.x) == 4:\n",
    "        world.grid[4][2].wall = True\n",
    "        world.grid[4][4].wall = False\n",
    "\n",
    "model = nengo.Network(\"Predict Value\", seed=2)\n",
    "with model:\n",
    "    env = grid.GridNode(world, dt=0.005)\n",
    "\n",
    "    movement = nengo.Node(move, size_in=2)\n",
    "    \n",
    "    def detect(t):\n",
    "        angles = (np.linspace(-0.5, 0.5, 3) + body.dir) % world.directions\n",
    "        return [body.detect(d, max_distance=4)[0] for d in angles]\n",
    "    stim_radar = nengo.Node(detect)\n",
    "    radar = nengo.Ensemble(n_neurons=50, dimensions=3, radius=4, seed=2,\n",
    "                noise=nengo.processes.WhiteSignal(10, 0.1, rms=1))\n",
    "    nengo.Connection(stim_radar, radar)\n",
    "\n",
    "    def braiten(x):\n",
    "        turn = x[2] - x[0]\n",
    "        spd = x[1] - 0.5\n",
    "        return spd, turn\n",
    "    nengo.Connection(radar, movement, function=braiten)  \n",
    "    \n",
    "    def position_func(t):\n",
    "        return body.x / world.width * 2 - 1, 1 - body.y/world.height * 2, body.dir / world.directions\n",
    "    position = nengo.Node(position_func)\n",
    "    state = nengo.Ensemble(100, 3)\n",
    "    nengo.Connection(position, state, synapse=None)\n",
    "    \n",
    "    reward = nengo.Node(lambda t: body.cell.reward)\n",
    "        \n",
    "    value = nengo.Ensemble(n_neurons=50, dimensions=1)\n",
    "\n",
    "    learn_conn = nengo.Connection(state, value, function=lambda x: 0,\n",
    "                                  learning_rule_type=nengo.PES(learning_rate=1e-4,\n",
    "                                                               pre_tau=tau))\n",
    "    nengo.Connection(reward, learn_conn.learning_rule, \n",
    "                     transform=-1, synapse=tau)\n",
    "    nengo.Connection(value, learn_conn.learning_rule, \n",
    "                     transform=-0.9, synapse=0.01)\n",
    "    nengo.Connection(value, learn_conn.learning_rule, \n",
    "                     transform=1, synapse=tau)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
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    }
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           "border": "1px solid #eee",
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          },
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       "tagName": "div"
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       "\n",
       "                <div id=\"121ef07d-0aff-49c8-a476-22b5ee95b0c5\">\n",
       "                    <iframe\n",
       "                        src=\"./nengo/61383/?token=dc413f507017217b788efa9708f99c1f218ad98a8f328f52\"\n",
       "                        width=\"100%\"\n",
       "                        height=\"600\"\n",
       "                        frameborder=\"0\"\n",
       "                        class=\"cell\"\n",
       "                        style=\"border: 1px solid #eee; box-sizing: border-box;\"\n",
       "                        allowfullscreen></iframe>\n",
       "                </div>\n",
       "            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from nengo_gui.ipython import IPythonViz\n",
    "IPythonViz(model, 'configs/learning6-value.py.cfg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Adaptive control\n",
    "\n",
    "In this example we again use the PES rule to learn an unknown function:\n",
    "   - the function is part of a controller for a system controlling a pendulum\n",
    "   - desired position is blue, actual position is black\n",
    "   - there is gravity, which makes the actual position go too far\n",
    "   - the learning rule determines how to supplement a standard PID controller to get the two to align\n",
    "   - If we turn learning off at the start, we'll notice that the two won't align.\n",
    "    \n",
    "The PES rule takes the control output and treats it as the error \n",
    "   - in this case we don't have an explicit difference between two values\n",
    "   - the PES rule effectively learns any constant error (like the I term does)\n",
    "   - the learning population gets the system state, and learns the error as a function of that state\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "import pendulum as pd\n",
    "import nengo\n",
    "import numpy as np\n",
    "\n",
    "model = nengo.Network(seed=3)\n",
    "with model:\n",
    "    env = pd.PendulumNode(seed=1, mass=4, max_torque=100)\n",
    "\n",
    "    desired = nengo.Node(lambda t: np.sin(t*np.pi))\n",
    "    nengo.Connection(desired, env[1], synapse=None)\n",
    "\n",
    "    pid = pd.PIDNode(dimensions=1, Kp=1, Kd=0.2, Ki=0)\n",
    "    nengo.Connection(pid, env[0], synapse=None)\n",
    "\n",
    "    nengo.Connection(desired, pid[0], synapse=None, transform=1)\n",
    "    nengo.Connection(env[0], pid[1], synapse=0, transform=1)\n",
    "    nengo.Connection(env[3], pid[3], synapse=0, transform=1)\n",
    "\n",
    "    nengo.Connection(desired, pid[2], synapse=None, transform=1000)\n",
    "    nengo.Connection(desired, pid[2], synapse=0, transform=-1000)\n",
    "\n",
    "    state = nengo.Ensemble(n_neurons=1000, dimensions=1,\n",
    "                           radius=1.5,\n",
    "                           #neuron_type=nengo.LIFRate(),\n",
    "                           )\n",
    "    nengo.Connection(env[0], state, synapse=None)\n",
    "\n",
    "\n",
    "    c = nengo.Connection(state, env[0], synapse=0,\n",
    "                         function=lambda x: 0,\n",
    "                         learning_rule_type=nengo.PES(learning_rate=1e-5))\n",
    "\n",
    "\n",
    "    stop_learning = nengo.Node(0)\n",
    "\n",
    "    error = nengo.Node(lambda t, x: x[0] if x[1]<0.5 else 0, size_in=2)\n",
    "    nengo.Connection(pid, error[0], synapse=None, transform=-1)\n",
    "    nengo.Connection(stop_learning, error[1], synapse=None)\n",
    "    nengo.Connection(error, c.learning_rule, synapse=None)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/celiasmi/Documents/nengo/nengo_gui/nengo_gui/jupyter.py:69: ConfigReuseWarning: Reusing config. Only the most recent visualization will update the config.\n",
      "  \"Reusing config. Only the most recent visualization will \"\n"
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       "                        p.innerHTML +=\n",
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       "                            'with the following command:' +\n",
       "                            '<pre>jupyter serverextension enable ' +\n",
       "                            'nengo_gui.jupyter</pre>';\n",
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       "\n",
       "                <div id=\"ce186f6b-f08d-40e0-8071-22bf14944483\">\n",
       "                    <iframe\n",
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       "                </div>\n",
       "            "
      ]
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     "metadata": {},
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   ],
   "source": [
    "from nengo_gui.ipython import IPythonViz\n",
    "IPythonViz(model, 'configs/pendulum.py.cfg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Unsupervised learning\n",
    "\n",
    "- Hebbian learning\n",
    "    - Neurons that fire together, wire together\n",
    "    - $\\Delta \\omega_{ij} = \\kappa a_i a_j$\n",
    "    - Just that would be unstable\n",
    "         - Why?\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "- BCM rule (Bienenstock, Cooper, & Munro, 1982)\n",
    "    - $\\Delta \\omega_{ij} = \\kappa a_i a_j (a_j-\\theta)$\n",
    "    - $\\theta$ is an activity threshold\n",
    "        - If post-synaptic neuron is more active than this threshold, increase strength\n",
    "        - Otherwise decrease it\n",
    "    - Other than that, it's a standard Hebbian rule\n",
    "    - Where would we get $\\theta$?\n",
    "        - Need to store something about the overall recent activity of neuron $j$ so it can be compared to its current activity\n",
    "        - Just have $\\theta$ be a psc-filtered spiking of $a_j$  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/envs/python3/lib/python3.6/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['sin', 'grid']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n",
      "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
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   ],
   "source": [
    "%pylab inline\n",
    "import nengo\n",
    "\n",
    "model = nengo.Network()\n",
    "with model:\n",
    "    sin = nengo.Node(lambda t: np.sin(t*4))\n",
    "    \n",
    "    pre = nengo.Ensemble(100, dimensions=1)\n",
    "    post = nengo.Ensemble(100, dimensions=1)\n",
    "\n",
    "    nengo.Connection(sin, pre)\n",
    "    conn = nengo.Connection(pre, post, solver=nengo.solvers.LstsqL2(weights=True))\n",
    "\n",
    "    pre_p = nengo.Probe(pre, synapse=0.01)\n",
    "    post_p = nengo.Probe(post, synapse=0.01)\n",
    "\n",
    "sim = nengo.Simulator(model)\n",
    "sim.run(2.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ec8ba58>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(sim.trange(), sim.data[pre_p], label=\"Pre\")\n",
    "plot(sim.trange(), sim.data[post_p], label=\"Post\")\n",
    "ylabel(\"Decoded value\")\n",
    "legend(loc=\"best\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                <div id=\"086dd7bd-c86b-4364-9eb4-e309faaddaa5\" style=\"\n",
       "                    width: 100%;\n",
       "                    border: 1px solid #cfcfcf;\n",
       "                    border-radius: 4px;\n",
       "                    text-align: center;\n",
       "                    position: relative;\">\n",
       "                  <div class=\"pb-text\" style=\"\n",
       "                      position: absolute;\n",
       "                      width: 100%;\">\n",
       "                    0%\n",
       "                  </div>\n",
       "                  <div class=\"pb-fill\" style=\"\n",
       "                      background-color: #bdd2e6;\n",
       "                      width: 0%;\">\n",
       "                    <style type=\"text/css\" scoped=\"scoped\">\n",
       "                        @keyframes pb-fill-anim {\n",
       "                            0% { background-position: 0 0; }\n",
       "                            100% { background-position: 100px 0; }\n",
       "                        }\n",
       "                    </style>\n",
       "                    &nbsp;\n",
       "                  </div>\n",
       "                </div>"
      ],
      "text/plain": [
       "HtmlProgressBar cannot be displayed. Please use the TerminalProgressBar. It can be enabled with `nengo.rc.set('progress', 'progress_bar', 'nengo.utils.progress.TerminalProgressBar')`."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "              (function () {\n",
       "                  var root = document.getElementById('086dd7bd-c86b-4364-9eb4-e309faaddaa5');\n",
       "                  var text = root.getElementsByClassName('pb-text')[0];\n",
       "                  var fill = root.getElementsByClassName('pb-fill')[0];\n",
       "\n",
       "                  text.innerHTML = 'Build finished in 0:00:01.';\n",
       "                  \n",
       "            fill.style.width = '100%';\n",
       "            fill.style.animation = 'pb-fill-anim 2s linear infinite';\n",
       "            fill.style.backgroundSize = '100px 100%';\n",
       "            fill.style.backgroundImage = 'repeating-linear-gradient(' +\n",
       "                '90deg, #bdd2e6, #edf2f8 40%, #bdd2e6 80%, #bdd2e6)';\n",
       "        \n",
       "                  \n",
       "                fill.style.animation = 'none';\n",
       "                fill.style.backgroundImage = 'none';\n",
       "            \n",
       "              })();\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                <div id=\"663c6189-ddbb-4293-95be-05c7a20429e6\" style=\"\n",
       "                    width: 100%;\n",
       "                    border: 1px solid #cfcfcf;\n",
       "                    border-radius: 4px;\n",
       "                    text-align: center;\n",
       "                    position: relative;\">\n",
       "                  <div class=\"pb-text\" style=\"\n",
       "                      position: absolute;\n",
       "                      width: 100%;\">\n",
       "                    0%\n",
       "                  </div>\n",
       "                  <div class=\"pb-fill\" style=\"\n",
       "                      background-color: #bdd2e6;\n",
       "                      width: 0%;\">\n",
       "                    <style type=\"text/css\" scoped=\"scoped\">\n",
       "                        @keyframes pb-fill-anim {\n",
       "                            0% { background-position: 0 0; }\n",
       "                            100% { background-position: 100px 0; }\n",
       "                        }\n",
       "                    </style>\n",
       "                    &nbsp;\n",
       "                  </div>\n",
       "                </div>"
      ],
      "text/plain": [
       "HtmlProgressBar cannot be displayed. Please use the TerminalProgressBar. It can be enabled with `nengo.rc.set('progress', 'progress_bar', 'nengo.utils.progress.TerminalProgressBar')`."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "              (function () {\n",
       "                  var root = document.getElementById('663c6189-ddbb-4293-95be-05c7a20429e6');\n",
       "                  var text = root.getElementsByClassName('pb-text')[0];\n",
       "                  var fill = root.getElementsByClassName('pb-fill')[0];\n",
       "\n",
       "                  text.innerHTML = 'Simulation finished in 0:00:21.';\n",
       "                  \n",
       "            if (100.0 > 0.) {\n",
       "                fill.style.transition = 'width 0.1s linear';\n",
       "            } else {\n",
       "                fill.style.transition = 'none';\n",
       "            }\n",
       "\n",
       "            fill.style.width = '100.0%';\n",
       "            fill.style.animation = 'none';\n",
       "            fill.style.backgroundImage = 'none'\n",
       "        \n",
       "                  \n",
       "                fill.style.animation = 'none';\n",
       "                fill.style.backgroundImage = 'none';\n",
       "            \n",
       "              })();\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conn.learning_rule_type = nengo.BCM(learning_rate=5e-10)\n",
    "\n",
    "with model:\n",
    "    trans_p = nengo.Probe(conn, 'weights', synapse=0.01, sample_every=0.01)\n",
    "\n",
    "sim = nengo.Simulator(model)\n",
    "sim.run(20.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x121ed6240>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=(12, 8))\n",
    "subplot(2, 1, 1)\n",
    "plot(sim.trange(), sim.data[pre_p], label=\"Pre\")\n",
    "plot(sim.trange(), sim.data[post_p], label=\"Post\")\n",
    "ylabel(\"Decoded value\")\n",
    "ylim(-1.6, 1.6)\n",
    "legend(loc=\"lower left\")\n",
    "\n",
    "subplot(2, 1, 2)\n",
    "# Find weight row with max variance\n",
    "neuron = np.argmax(np.mean(np.var(sim.data[trans_p], axis=0), axis=1))\n",
    "plot(sim.trange(dt=0.01), sim.data[trans_p][..., neuron])\n",
    "ylabel(\"Connection weight\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR:nengo_gui.server:Error response\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/celiasmi/Documents/nengo/nengo_gui/nengo_gui/server.py\", line 412, in do_GET\n",
      "    self.http_GET()\n",
      "  File \"/Users/celiasmi/Documents/nengo/nengo_gui/nengo_gui/guibackend.py\", line 137, in http_GET\n",
      "    server.HttpWsRequestHandler.http_GET(self)\n",
      "  File \"/Users/celiasmi/Documents/nengo/nengo_gui/nengo_gui/server.py\", line 428, in http_GET\n",
      "    response = getattr(self, command)()\n",
      "  File \"/Users/celiasmi/Documents/nengo/nengo_gui/nengo_gui/guibackend.py\", line 86, in auth_checked\n",
      "    return fn(inst)\n",
      "  File \"/Users/celiasmi/Documents/nengo/nengo_gui/nengo_gui/guibackend.py\", line 233, in serve_main\n",
      "    page = self.server.create_page(filename, reset_cfg=reset_cfg)\n",
      "  File \"/Users/celiasmi/Documents/nengo/nengo_gui/nengo_gui/guibackend.py\", line 457, in create_page\n",
      "    reset_cfg=reset_cfg)\n",
      "  File \"/Users/celiasmi/Documents/nengo/nengo_gui/nengo_gui/page.py\", line 114, in __init__\n",
      "    self.load()\n",
      "  File \"/Users/celiasmi/Documents/nengo/nengo_gui/nengo_gui/page.py\", line 172, in load\n",
      "    self.name_finder = nengo_gui.NameFinder(self.locals, self.model)\n",
      "  File \"/Users/celiasmi/Documents/nengo/nengo_gui/nengo_gui/namefinder.py\", line 13, in __init__\n",
      "    self.find_names(net)\n",
      "  File \"/Users/celiasmi/Documents/nengo/nengo_gui/nengo_gui/namefinder.py\", line 16, in find_names\n",
      "    net_name = self.known_name[net]\n",
      "KeyError: <Network \"Simple RL\" at 0x11ca9d978>\n",
      "ERROR:nengo_gui.server:Error response\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/celiasmi/Documents/nengo/nengo_gui/nengo_gui/server.py\", line 412, in do_GET\n",
      "    self.http_GET()\n",
      "  File \"/Users/celiasmi/Documents/nengo/nengo_gui/nengo_gui/guibackend.py\", line 137, in http_GET\n",
      "    server.HttpWsRequestHandler.http_GET(self)\n",
      "  File \"/Users/celiasmi/Documents/nengo/nengo_gui/nengo_gui/server.py\", line 428, in http_GET\n",
      "    response = getattr(self, command)()\n",
      "  File \"/Users/celiasmi/Documents/nengo/nengo_gui/nengo_gui/guibackend.py\", line 86, in auth_checked\n",
      "    return fn(inst)\n",
      "  File \"/Users/celiasmi/Documents/nengo/nengo_gui/nengo_gui/guibackend.py\", line 233, in serve_main\n",
      "    page = self.server.create_page(filename, reset_cfg=reset_cfg)\n",
      "  File \"/Users/celiasmi/Documents/nengo/nengo_gui/nengo_gui/guibackend.py\", line 457, in create_page\n",
      "    reset_cfg=reset_cfg)\n",
      "  File \"/Users/celiasmi/Documents/nengo/nengo_gui/nengo_gui/page.py\", line 114, in __init__\n",
      "    self.load()\n",
      "  File \"/Users/celiasmi/Documents/nengo/nengo_gui/nengo_gui/page.py\", line 172, in load\n",
      "    self.name_finder = nengo_gui.NameFinder(self.locals, self.model)\n",
      "  File \"/Users/celiasmi/Documents/nengo/nengo_gui/nengo_gui/namefinder.py\", line 13, in __init__\n",
      "    self.find_names(net)\n",
      "  File \"/Users/celiasmi/Documents/nengo/nengo_gui/nengo_gui/namefinder.py\", line 16, in find_names\n",
      "    net_name = self.known_name[net]\n",
      "KeyError: <Network \"Predict Value\" at 0x11f7eecf8>\n",
      "ERROR:nengo_gui.server:Error response\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/celiasmi/Documents/nengo/nengo_gui/nengo_gui/server.py\", line 412, in do_GET\n",
      "    self.http_GET()\n",
      "  File \"/Users/celiasmi/Documents/nengo/nengo_gui/nengo_gui/guibackend.py\", line 137, in http_GET\n",
      "    server.HttpWsRequestHandler.http_GET(self)\n",
      "  File \"/Users/celiasmi/Documents/nengo/nengo_gui/nengo_gui/server.py\", line 428, in http_GET\n",
      "    response = getattr(self, command)()\n",
      "  File \"/Users/celiasmi/Documents/nengo/nengo_gui/nengo_gui/guibackend.py\", line 86, in auth_checked\n",
      "    return fn(inst)\n",
      "  File \"/Users/celiasmi/Documents/nengo/nengo_gui/nengo_gui/guibackend.py\", line 233, in serve_main\n",
      "    page = self.server.create_page(filename, reset_cfg=reset_cfg)\n",
      "  File \"/Users/celiasmi/Documents/nengo/nengo_gui/nengo_gui/guibackend.py\", line 457, in create_page\n",
      "    reset_cfg=reset_cfg)\n",
      "  File \"/Users/celiasmi/Documents/nengo/nengo_gui/nengo_gui/page.py\", line 114, in __init__\n",
      "    self.load()\n",
      "  File \"/Users/celiasmi/Documents/nengo/nengo_gui/nengo_gui/page.py\", line 172, in load\n",
      "    self.name_finder = nengo_gui.NameFinder(self.locals, self.model)\n",
      "  File \"/Users/celiasmi/Documents/nengo/nengo_gui/nengo_gui/namefinder.py\", line 13, in __init__\n",
      "    self.find_names(net)\n",
      "  File \"/Users/celiasmi/Documents/nengo/nengo_gui/nengo_gui/namefinder.py\", line 16, in find_names\n",
      "    net_name = self.known_name[net]\n",
      "KeyError: <Network \"Cortical Consolidation\" at 0x11c802d30>\n"
     ]
    }
   ],
   "source": [
    "def sparsity_measure(vector):\n",
    "    # Max sparsity = 1 (single 1 in the vector)\n",
    "    v = np.sort(np.abs(vector))\n",
    "    n = v.shape[0]\n",
    "    k = np.arange(n) + 1\n",
    "    l1norm = np.sum(v)\n",
    "    summation = np.sum((v / l1norm) * ((n - k + 0.5) / n))\n",
    "    return 1 - 2 * summation\n",
    "\n",
    "print(\"Starting sparsity: {0}\".format(sparsity_measure(sim.data[trans_p][0])))\n",
    "print(\"Ending sparsity: {0}\".format(sparsity_measure(sim.data[trans_p][-1])))"
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    "- Result: only a few neurons will fire\n",
    "    - Sparsification\n",
    "- What would this do in NEF terms?        \n",
    "    - Still represent $x$, but with very sparse encoders (assuming the function doesn't change)\n",
    "- This is still a rule on the weight matrix, but functionally seems to be more about encoders than decoders\n",
    "    - What could we do, given that?  "
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    "## The homeostatic Prescribed Error Sensitivity (hPES) rule\n",
    "\n",
    "- Just do them both [(Bekolay et al., 2013)](http://compneuro.uwaterloo.ca/publications/bekolay2013.html)\n",
    "- And have a parameter $S$ to adjust how much of each\n",
    "\n",
    "$\\Delta \\omega_{ij} = \\kappa a_i (\\alpha_j S e_j \\cdot E + (1-S) a_j (a_j-\\theta))$\n",
    "\n",
    "- Works as well (or better) than PES\n",
    "    - Seems to be a bit more stable, but analysis is ongoing\n",
    "- Biological evidence?\n",
    "    - Spike-Timing Dependent Plasticity\n",
    "\n",
    "<img src=\"files/lecture_learning/STDP.png\" width=\"500\">\n",
    "\n",
    "<img src=\"files/lecture_learning/STDP2.png\" width=\"500\">\n",
    "\n",
    "- Still work to do for comparison, but seems promising\n",
    "- Error-driven for improving decoders\n",
    "- Hebbian sparsification to improve encoders\n",
    "    - Perhaps to sparsify connections (energy savings in the brain, but not necessarily in simulation)\n",
    "    \n"
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