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    "# SYDE 556/750: Simulating Neurobiological Systems\n",
    "\n",
    "### Symbols and symbolic-like representations"
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    "## Symbols and Symbol-like representation in neurons\n",
    "\n",
    "- We've seen how to represent vectors in neurons\n",
    "    - And how to compute functions on those vectors\n",
    "    - And dynamical systems\n",
    "- But how can we do anything like human language?\n",
    "    - How could we represent the fact that \"the number after 8 is 9\"\n",
    "    - Or \"dogs chase cats\"\n",
    "    - Or \"Anne knows that Bill thinks that Charlie likes Dave\"\n",
    "- Does the NEF help us at all with this?\n",
    "    - Or is this just too hard a problem yet?"
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    "### Traditional Cognitive Science\n",
    "\n",
    "- Lots of theories that work with structured information like this\n",
    "- Pretty much all of them use some representation framework like this:\n",
    "    - `after(eight, nine)`\n",
    "    - `chase(dogs, cats)`\n",
    "    - `knows(Anne, thinks(Bill, likes(Charlie, Dave)))`\n",
    "\n",
    "- Cognitive models manipulate these sorts of representations\n",
    "    - mental arithmetic\n",
    "    - driving a car\n",
    "    - using a GUI\n",
    "    - parsing language\n",
    "    - etc etc\n",
    "- Seems to match well to behavioural data, so something like this should be right\n",
    "- So how can we do this in neurons?\n",
    "    "
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    "- This is a hard problem\n",
    "- Jackendoff (2002) posed this as a major problem, identifying four linguistic challenges for cognitive neuroscience\n",
    "    - The Binding Problem\n",
    "    - The Problem of 2\n",
    "    - The Problem of Variables\n",
    "    - Working Memory vs Long-term memory"
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    "- The Binding Problem\n",
    "    - Suppose you see a red square and a blue circle\n",
    "    - How do you keep these ideas separate?  How does \"red\" get bound with \"square\", and how is it kept separate from \"blue\" and \"circle\"?\n",
    "- The Problem of 2\n",
    "    - \"The little star's beside the big star\"\n",
    "    - How do we keep those two uses of \"star\" separate?\n",
    "- The Problem of Variables\n",
    "    - Words seem to have types (nouns, verbs, adjectives, etc, etc)\n",
    "    - How do you make use of that?  \n",
    "    - E.g. \"blue *NOUN*\" is fine, but \"blue *VERB*\" is not (or is very different)\n",
    "- Working memory vs Long-term memory\n",
    "    - We can both use sentences (working memory) and store them indefinitely (long-term memory)\n",
    "    - How do these transfer back and forth?\n",
    "    - What are they in the brain?  This seems to require moving from storing something in neural activity to storing it in connection weights\n",
    "    "
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    "### Possible solutions\n",
    "\n",
    "1. Oscillations\n",
    "    - \"red square and blue circle\"\n",
    "    - Different patterns of activity for RED, SQUARE, BLUE, and CIRCLE\n",
    "    - Have the patterns for RED and SQUARE happen, then BLUE and CIRCLE, then back to RED and SQUARE\n",
    "    - More complex structures possible too:\n",
    "    - E.g. the LISA architecture\n",
    "    \n",
    "<img src=\"files/lecture_symbols/lisa.png\">\n",
    "\n",
    "- Problems\n",
    "    - What controls this oscillation?\n",
    "    - How is it parsed? (i.e., mapped to sets of oscillators, noise)\n",
    "    - How do we deal with the exponentional explosion of nodes needed?\n",
    "    - How to deal with A*B B*C  (but not C*A)\n"
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    "'2. Implementing Symbol Systems in Neurons\n",
    "   - Build a general-purpose symbol-binding system\n",
    "   - Lots of temporary pools of neurons\n",
    "   - Ways to temporarily associate them with particular concepts\n",
    "   - Ways to temporarily associate pools together\n",
    "   - Neural Blackboard Architecture\n",
    "    \n",
    "<img src=\"files/lecture_symbols/nba.png\">\n",
    "\n",
    "- Problems\n",
    "    - Very particular structure (doesn't seem to match biology)\n",
    "    - Uses a very large number of neurons (~500 million) to be flexible enough for simple sentences\n",
    "    - And that's just to represent the sentence, never mind controlling and manipulating it\n",
    "    \n"
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    "'3. Vector operators\n",
    "   - Paul Smolensky [1990](http://verbs.colorado.edu/~llbecker/papers/Smolensky-TensorProductVariableBinding.pdf) suggests using a mathematical operator called a 'tensor product' to bind vectors together.\n",
    "   - The idea is that this operator can 'bind' and its inverse 'unbind' these vectors together.\n",
    "   - It provides an algebraic way of specifying symbolic structures in a vector space.\n",
    "   - If you can represent vector spaces and operators in neurons (e.g. NEF), then it provides a way of working with symbolic structures in neurons.\n",
    "\n",
    "<img src=\"files/lecture_symbols/tensor.png\">\n",
    "\n",
    "- Problems\n",
    "    - Every time you bind vectors, the result has $D^2$ as many dimensions.\n",
    "    - It scales extremely poorly: the dimensionality of a structure representation is the base space to the power of depth, i.e. $D_S = D^{depth+1}$\n",
    "\n"
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    "### A deeper issue\n",
    "   - If we're just implementing something like the original symbol system in neurons, then we're reducing the neural aspects to *mere implementational details*.\n",
    "   - That is, if we had a perfectly good way to implement symbol structures in neurons, then we could take existing cognitive theories based on symbol structures and implement them in neurons.\n",
    "   - But so what?  That would be conceding the point that the neurons don't matter -- you don't *need* neurons to understand cognition.  \n",
    "        - Sure, they're useful for testing some aspects, like what firing patterns should be.\n",
    "        - But it's more for completeness sake than for a better understanding.\n",
    "        - (Same as wondering how a neuron gets implemented in terms of atoms or quarks. Not that important for function.)\n",
    "   - This is why the majority of cognitive scientists don't worry about neurons."
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    "### Semantic pointers\n",
    "\n",
    "- Tensor products are on the right track, if we can solve the scaling problem.\n",
    "- 'How to build a brain' exploits a compression operator introduced by Tony Plate for cognitive modeling called 'circular convolution' $\\circledast$ (this operator has been used in signal processing for a long time).\n",
    "- In the book, I suggest that neurally realized compressed vector representations are prevalent in the brain, and call such representations 'semantic pointers'.\n",
    "- Pointers: because they can be used like computer science pointers to be efficient references to complex representations.\n",
    "- Semantic: because (unlike CS pointers), their content is derived (through compression) from semantically related representations.\n",
    "\n"
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    "- This provides something that's similar to the symbolic approach, but much more tied to biology.\n",
    "    - Most of the same capabilities as the classic symbol systems.\n",
    "    - But not all.\n",
    "- Based on vectors and functions on those vectors.\n",
    "    - There is a vector for each concept (and maybe 'sub concepts').\n",
    "    - Build up structures by doing math on those vectors.\n",
    "    \n"
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    "- Example\n",
    "    - \"blue square and red circle\"\n",
    "    - Can't just do BLUE+SQUARE+RED+CIRCLE.\n",
    "        - Why?\n",
    "    - Need some other operation as well.\n",
    "- Requirements:\n",
    "     - Input 2 vectors, get a new vector as output.\n",
    "     - Reversible (given the output and one of the input vectors, generate the other input vector).\n",
    "     - Output vector is highly dissimilar to either input vector.\n",
    "         - Unlike addition, where the output is highly similar.\n",
    "    "
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    "- Circular convoluation can act as such a function.\n",
    "    - (There are many other such functions: XOR, Multiply, etc... collectively this kind of vector space binding to represent structures are sometimes called 'Vector Symbolic Architectures' (Gayler, 2003).)\n",
    "<img src=\"lecture_symbols/operators_compare.png\">"
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    "- Why circular convolution?\n",
    "    - Extensively studied (Plate, 1997: Holographic Reduced Representations).\n",
    "    - Easy to approximately invert (circular correlation).\n",
    "    \n",
    "- Examples:\n",
    "    - `BLUE` $\\circledast$ `SQUARE + RED` $\\circledast$ `CIRCLE`\n",
    "    - `DOG` $\\circledast$ `AGENT + CAT` $\\circledast$ `THEME + VERB` $\\circledast$ `CHASE`\n",
    "\n",
    "<img src=\"files/lecture_symbols/bind.png\">"
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    "- Unbinding:\n",
    "\n",
    "<img src=\"files/lecture_symbols/unbind.png\">\n",
    "\n",
    "\n",
    "\n"
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    "- Can also think of this operator as a compressed outer product\n",
    "\n",
    "<img src=\"files/lecture_symbols/circonv.png\">"
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    "- Lots of nice properties:\n",
    "    - Can store complex structures:\n",
    "         - `after(eight, nine)`\n",
    "         - `NUMBER` $\\circledast$ `EIGHT + NEXT` $\\circledast$ `NINE`\n",
    "         - `knows(Anne, thinks(Bill, likes(Charlie, Dave)))`\n",
    "         - `SUBJ` $\\circledast$ `ANNE + ACT` $\\circledast$ `KNOWS + OBJ` $\\circledast$ `(SUBJ` $\\circledast$ `BILL + ACT` $\\circledast$ `THINKS + OBJ` $\\circledast$ `(SUBJ` $\\circledast$ `CHARLIE + ACT` $\\circledast$ `LIKES + OBJ` $\\circledast$ `DAVE))`\n",
    "    - But gracefully degrades!\n",
    "        - As representation gets more complex, the accuracy of breaking it apart decreases.\n",
    "    - Keeps similarity information.\n",
    "        - If `RED` is similar to `PINK` then `RED` $\\circledast$ `CIRCLE` is similar to `PINK` $\\circledast$ `CIRCLE`.\n",
    "        \n",
    "- But rather complicated:\n",
    "    - Seems like a weird operation for neurons to do."
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    "### Circular convolution in the NEF\n",
    "\n",
    "- Or is it?\n",
    "- Circular convolution is a whole bunch ($D^2$) of multiplies.\n",
    "- Like any convolution, it can also be written as a Fourier transform, an elementwise multiply, and another Fourier transform:\n",
    "    - i.e.  $A \\circledast B = IDFT(DFT(A).DFT(B)))$\n",
    "- The discrete fourier transform is just a linear operation, hence a matrix.\n",
    "- So that's just $D$ pairwise multiplies.\n",
    "- In fact, circular convolution turns out to be *exactly* what the NEF shows neurons are good at (i.e., low order polynomials)."
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    "### Jackendoff's Challenges\n",
    "\n",
    "- As pointed out in [Vector Symbolic Architectures Answer Jackendoff's Challenges for Cognitive Neuroscience].(http://arxiv.org/ftp/cs/papers/0412/0412059.pdf)\n",
    "- The Binding Problem:\n",
    "    - There is a lot of \"binding\" (structurally combining items) in linguistics (more than vision) .\n",
    "    - `RED` $\\circledast$ `CIRCLE + BLUE` $\\circledast$ `TRIANGLE`\n",
    "    - After it is bound, we can ask \"what color is the circle by doing $\\circledast$ `CIRCLE'`.\n",
    "        - where `'` is \"inverse\".\n",
    "        - (`RED` $\\circledast$ `CIRCLE + BLUE` $\\circledast$ `TRIANGLE`) $\\circledast$ `CIRCLE'`\n",
    "        - = `RED` $\\circledast$ `CIRCLE` $\\circledast$ `CIRCLE' + BLUE` $\\circledast$ `TRIANGLE` $\\circledast$ `CIRCLE'`\n",
    "        - = `RED + BLUE` $\\circledast$ `TRIANGLE` $\\circledast$ `CIRCLE'`\n",
    "        - = `RED + noise`\n",
    "        - $\\approx$ `RED`\n",
    "        \n",
    "- The Problem of 2:\n",
    "    - How can we distinguish two uses of the same concept in a sentence?\n",
    "    - \"The little star's beside the big star.\"\n",
    "    - `OBJ1` $\\circledast$ `(TYPE` $\\circledast$ `STAR + SIZE` $\\circledast$ `LITTLE) + OBJ2` $\\circledast$ `(TYPE` $\\circledast$ `STAR + SIZE` $\\circledast$ `BIG) + BESIDE` $\\circledast$ `OBJ1` $\\circledast$ `OBJ2`\n",
    "        - Notice that `BESIDE` $\\circledast$ `OBJ1` $\\circledast$ `OBJ2` = `BESIDE` $\\circledast$ `OBJ2` $\\circledast$ `OBJ1`\n",
    "        - And that we can distinguish the two uses of star.\n",
    "- The Problem of Variables:\n",
    "    - How can we manipulate abstract place holders?\n",
    "    - `S` = `RED` $\\circledast$ `NOUN`\n",
    "    - `VAR` = `BALL` $\\circledast$ `NOUN'`\n",
    "    - `S` $\\circledast$ `VAR` = `RED` $\\circledast$ `BALL`\n",
    "- Binding in Working Memory vs Long-term memory:\n",
    "    - How can we use and manipulate the same structures in both WM and LTM?\n",
    "    - Vectors are what we work with (activity of neurons).\n",
    "    - Functions are long-term memory (connection weights).\n",
    "    - Functions return (and modify) vectors."
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    "### Symbol-like manipulation\n",
    "\n",
    "- Can do a lot of standard symbol operations.\n",
    "- Sometimes you have to explicitly bind and unbind to manipulate the data.\n",
    "- Less accuracy for more complex structures.\n",
    "- *But we can also do more with these representations*\n",
    "    - Sometimes you don't have to explicitly bind/unbind to manipulate.\n",
    "    - Can define other vector operations not available for symbols...\n",
    "\n",
    "\n",
    "   "
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    "### Raven's Progressive Matrices\n",
    "\n",
    "- An IQ test that's generally considered to be the best at measuring general-purpose \"fluid\" intelligence:\n",
    "    - Nonverbal (so it's not measuring language skills, and fairly unbiased across cultures, hopefully).\n",
    "    - Fill in the blank: given eight possible answers, pick one.\n",
    "    \n",
    "<img src=\"files/lecture_symbols/ravens.png\" width=\"300\">\n",
    "\n",
    "- This is not an actual question on the test:\n",
    "    - The test is copyrighted.\n",
    "    - They don't want the test to leak out, since it's been the same set of 60 questions since 1936.\n",
    "    - But they do look like that.\n"
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    "- How can we model people doing this task?\n",
    "- A fair number of different attempts:\n",
    "    - None neural.\n",
    "    - Generally use the approach of building in a large set of different types of patterns to look for, and then trying them all in turn.\n",
    "    - Which seems wrong for a test that's supposed to be about *flexible, fluid* intelligence.\n",
    "    \n",
    "- Does this vector approach offer an alternative?\n",
    "\n"
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    "- First we need to represent the different patterns as a vector.\n",
    "    - This is a hard image interpretation problem.\n",
    "    - Still ongoing work here.\n",
    "    - So we'll skip it and start with things in vector form.\n",
    "    \n",
    "<img src=\"files/lecture_symbols/ravens2.png\">    \n",
    "    \n",
    "- How do we represent a picture?\n",
    "    - `SHAPE` $\\circledast$ `ARROW + NUMBER` $\\circledast$ `ONE + DIRECTION` $\\circledast$ `UP`\n",
    "    - Can do variations like this for all the pictures.\n",
    "    - Fairly standard with most assumptions about how people represent complex scenes.\n",
    "    - But that part is not being modelled (yet!).\n",
    "    \n",
    "- We have shown that it's possible to build these sorts of representations up directly from visual stimuli.\n",
    "    - With a very simple vision system that can only recognize a few different shapes.\n",
    "    - And where items have to be shown sequentially as it has no way of moving its eyes."
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    "## Examples in Spaun\n",
    "\n",
    "- Let's look at a simpler but related example: \n",
    "    - Binding used for building a list to remember"
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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"720\"\n",
       "            height=\"400\"\n",
       "            src=\"https://www.youtube.com/embed/U_Q6Xjz9QHg?playlist=U_Q6Xjz9QHg&loop=1&autoplay=0\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x10f42d690>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('U_Q6Xjz9QHg', width=720, height=400, loop=1, autoplay=0, playlist='U_Q6Xjz9QHg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "- The memory of the list is built up by using a basal ganglia action selection system to control feeding values into an integrator\n",
    "    - The thought bubble shows how close the decoded values are to the ideal\n",
    "    - Notice the forgetting!  \n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "- The same system can be used to do a version of the Raven's Matrices task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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Hjce2/qlqrRNW2NXJrecRKk7pOHmpLZHQBF5vbAlsuKGUBp8FF4y4IOc5Zwtn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\nBERAREQEREBERB//2Q==\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"720\"\n",
       "            height=\"400\"\n",
       "            src=\"https://www.youtube.com/embed/Q_LRvnwnYp8?playlist=Q_LRvnwnYp8&loop=1&autoplay=0\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x10f42d810>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('Q_LRvnwnYp8', width=720, height=400, loop=1, autoplay=0, playlist='Q_LRvnwnYp8')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "- `S1 = ONE` $\\circledast$ `P1`\n",
    "- `S2 = ONE` $\\circledast$ `P1 + ONE` $\\circledast$ `P2`\n",
    "- `S3 = ONE` $\\circledast$ `P1 + ONE` $\\circledast$ `P2 + ONE` $\\circledast$ `P3`\n",
    "- `S4 = FOUR` $\\circledast$ `P1`\n",
    "- `S5 = FOUR` $\\circledast$ `P1 + FOUR` $\\circledast$ `P2`\n",
    "- `S6 = FOUR` $\\circledast$ `P1 + FOUR` $\\circledast$ `P2 + FOUR` $\\circledast$ `P3`\n",
    "- `S7 = FIVE` $\\circledast$ `P1`\n",
    "- `S8 = FIVE` $\\circledast$ `P1 + FIVE` $\\circledast$ `P2`\n",
    "\n",
    "- what is `S9`?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "How does Spaun make a guess at the end?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "- Let's figure out what the transformation is\n",
    "- `T1 = S2` $\\circledast$ `S1'`\n",
    "- `T2 = S3` $\\circledast$ `S2'`\n",
    "- `T3 = S5` $\\circledast$ `S4'`\n",
    "- `T4 = S6` $\\circledast$ `S5'`\n",
    "- `T5 = S8` $\\circledast$ `S7'`\n",
    "\n",
    "- `T = (T1 + T2 + T3 + T4 + T5)/5`\n",
    "- `S9 = S8` $\\circledast$ `T`\n",
    "\n",
    "- `S9 = FIVE` $\\circledast$ `P1 + FIVE` $\\circledast$ `P2 + FIVE` $\\circledast$ `P3`\n",
    "\n",
    "- This becomes a novel way of manipulating structured information\n",
    "    - Exploiting the fact that it is a vector underneath\n",
    "    - [A spiking neural model applied to the study of human performance and cognitive decline on Raven's Advanced Progressive Matrices](http://www.sciencedirect.com/science/article/pii/S0160289613001542)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Using Nengo\n",
    "- How can we use Nengo to perform these kinds of operations?\n",
    "- There's a 'SPA' module you can import, and it lets you do all kinds of symbol-like manipulations (and other cognitive processing).\n",
    "- Let's try answering simple questions about the bindings in a representation.\n",
    "- I just stole this from the 'Question answering' example in Nengo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "%pylab inline\n",
    "\n",
    "import nengo\n",
    "import nengo_spa as spa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Number of dimensions for the Semantic Pointers\n",
    "dimensions = 32\n",
    "\n",
    "model = spa.Network(label=\"Simple question answering\")\n",
    "\n",
    "with model:\n",
    "    colour_in = spa.Transcode(colour_input, output_vocab=dimensions)\n",
    "    shape_in = spa.Transcode(shape_input, output_vocab=dimensions)\n",
    "    cue = spa.Transcode(cue_input, output_vocab=dimensions)    \n",
    "    \n",
    "    conv = spa.State(dimensions)\n",
    "    out = spa.State(dimensions)\n",
    "\n",
    "    # Connect the buffers\n",
    "    colour_in * shape_in >> conv\n",
    "    conv * ~cue >> out "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The input will switch every 0.5 seconds between `RED` and `BLUE`. In the same way the shape input switches between `CIRCLE` and `SQUARE`. Thus, the network will bind alternatingly `RED * CIRCLE` and `BLUE * SQUARE` for 0.5 seconds each.\n",
    "\n",
    "The cue for deconvolving bound semantic pointers cycles through `CIRCLE`, `RED`, `SQUARE`, and `BLUE` within one second. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "def colour_input(t):\n",
    "    if (t // 0.5) % 2 == 0:\n",
    "        return 'RED'\n",
    "    else:\n",
    "        return 'BLUE'\n",
    "\n",
    "def shape_input(t):\n",
    "    if (t // 0.5) % 2 == 0:\n",
    "        return 'CIRCLE'\n",
    "    else:\n",
    "        return 'SQUARE'\n",
    "\n",
    "def cue_input(t):\n",
    "    sequence = ['0', 'CIRCLE', 'RED', '0', 'SQUARE', 'BLUE'] \n",
    "    idx = int((t // (1. / len(sequence))) % len(sequence))\n",
    "    return sequence[idx]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "with model:\n",
    "    model.config[nengo.Probe].synapse = nengo.Lowpass(0.03)\n",
    "    p_colour_in = nengo.Probe(colour_in.output)\n",
    "    p_shape_in = nengo.Probe(shape_in.output)\n",
    "    p_cue = nengo.Probe(cue.output)\n",
    "    p_conv = nengo.Probe(conv.output)\n",
    "    p_out = nengo.Probe(out.output)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "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"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "            <script type=\"text/javascript\" id=\"eccbf9e9-1cea-4005-9562-035035186815\">\n",
       "            {\n",
       "                let req = new XMLHttpRequest();\n",
       "                req.addEventListener(\"load\", function() {\n",
       "                    if (this.status != 200 && this.response != 'OK') {\n",
       "                        let p = document.getElementById('eccbf9e9-1cea-4005-9562-035035186815').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": "0a930841-0fbe-4d94-807e-5c3ec79725d3"
       },
       "children": [
        {
         "attributes": {
          "allowfullscreen": "allowfullscreen",
          "class": "cell",
          "frameborder": "0",
          "height": "600",
          "src": "./nengo/57018/?token=6e1acb17dd66b34efb36cea4328c9daa1ee517b7875e7d1c",
          "style": {
           "border": "1px solid #eee",
           "boxSizing": "border-box"
          },
          "width": "100%"
         },
         "tagName": "iframe"
        }
       ],
       "tagName": "div"
      },
      "text/html": [
       "\n",
       "                <div id=\"785823ec-4b17-4d9a-aa30-1b740e8832c6\">\n",
       "                    <iframe\n",
       "                        src=\"./nengo/57018/?token=6e1acb17dd66b34efb36cea4328c9daa1ee517b7875e7d1c\"\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/blue_red_spa.py.cfg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                <div id=\"7710456c-7205-4914-9dc6-e8c6b57bd66b\" 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('7710456c-7205-4914-9dc6-e8c6b57bd66b');\n",
       "                  var text = root.getElementsByClassName('pb-text')[0];\n",
       "                  var fill = root.getElementsByClassName('pb-fill')[0];\n",
       "\n",
       "                  text.innerHTML = 'Build finished in 0:00:08.';\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=\"5a94e012-28f6-462d-9406-e32cdf174869\" 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('5a94e012-28f6-462d-9406-e32cdf174869');\n",
       "                  var text = root.getElementsByClassName('pb-text')[0];\n",
       "                  var fill = root.getElementsByClassName('pb-fill')[0];\n",
       "\n",
       "                  text.innerHTML = 'Simulation finished in 0:00:25.';\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": [
    "sim = nengo.Simulator(model)\n",
    "sim.run(3.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122959978>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "vocab = model.vocabs[dimensions]\n",
    "\n",
    "plt.subplot(5, 1, 1)\n",
    "plt.plot(sim.trange(), spa.similarity(sim.data[p_colour_in], vocab))\n",
    "plt.legend(vocab.keys(), fontsize='x-small')\n",
    "plt.ylabel(\"colour\")\n",
    "\n",
    "plt.subplot(5, 1, 2)\n",
    "plt.plot(sim.trange(), spa.similarity(sim.data[p_shape_in], vocab))\n",
    "plt.legend(vocab.keys(), fontsize='x-small')\n",
    "plt.ylabel(\"shape\")\n",
    "\n",
    "plt.subplot(5, 1, 3)\n",
    "plt.plot(sim.trange(), spa.similarity(sim.data[p_cue], vocab))\n",
    "plt.legend(vocab.keys(), fontsize='x-small')\n",
    "plt.ylabel(\"cue\")\n",
    "\n",
    "plt.subplot(5, 1, 4)\n",
    "for pointer in ['RED * CIRCLE', 'BLUE * SQUARE']:\n",
    "    plt.plot(sim.trange(), vocab.parse(pointer).dot(sim.data[p_conv].T), label=pointer)\n",
    "plt.legend(fontsize='x-small')\n",
    "plt.ylabel(\"convolved\")\n",
    "\n",
    "plt.subplot(5, 1, 5)\n",
    "plt.plot(sim.trange(), spa.similarity(sim.data[p_out], vocab))\n",
    "plt.legend(vocab.keys(), fontsize='x-small')\n",
    "plt.ylabel(\"output\")\n",
    "plt.xlabel(\"time [s]\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/envs/python3/lib/python3.6/site-packages/ipykernel_launcher.py:1: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.from_iter(generator)) or the python sum builtin instead.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "30400"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(ens.n_neurons for ens in model.all_ensembles) #Total number of neurons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  },
  "livereveal": {
   "scroll": true,
   "start_slideshow_at": "selected"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}