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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\n",
    "- 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)\n",
    "    - How do we deal with the exponentional explosion of nodes needed?\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'd just be conceeding the point that the neurons don't matter -- you don't need them to develop the theory.  \n",
    "        - They're useful for testing some aspects, like what firing patterns should be\n",
    "        - But it's more for completeness sake than for understanding\n",
    "        - No more interesting than the question of \"well, how does that neuron get implemented in terms of chemistry\"  or atoms.  Or quarks.\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 called 'circular convolution' $\\circledast$\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\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"
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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 stuff\n",
    "- 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",
    "\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\n",
    "    - 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",
    "\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": {
    "collapsed": false,
    "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 this stuff\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": 16,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "%pylab inline\n",
    "\n",
    "import nengo\n",
    "from nengo import spa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Number of dimensions for the Semantic Pointers\n",
    "dimensions = 32\n",
    "\n",
    "model = spa.SPA(label=\"Simple question answering\")\n",
    "\n",
    "with model:\n",
    "    model.color_in = spa.Buffer(dimensions=dimensions)\n",
    "    model.shape_in = spa.Buffer(dimensions=dimensions)\n",
    "    model.conv = spa.Buffer(dimensions=dimensions)\n",
    "    model.cue = spa.Buffer(dimensions=dimensions)\n",
    "    model.out = spa.Buffer(dimensions=dimensions)\n",
    "    \n",
    "    # Connect the buffers\n",
    "    cortical_actions = spa.Actions(\n",
    "        'conv = color_in * shape_in',\n",
    "        'out = conv * ~cue'\n",
    "    )\n",
    "    model.cortical = spa.Cortical(cortical_actions)  "
   ]
  },
  {
   "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": 18,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "def color_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",
    "\n",
    "with model:\n",
    "    model.inp = spa.Input(color_in=color_input, shape_in=shape_input, cue=cue_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "with model:\n",
    "    model.config[nengo.Probe].synapse = nengo.Lowpass(0.03)\n",
    "    color_in = nengo.Probe(model.color_in.state.output)\n",
    "    shape_in = nengo.Probe(model.shape_in.state.output)\n",
    "    cue = nengo.Probe(model.cue.state.output)\n",
    "    conv = nengo.Probe(model.conv.state.output)\n",
    "    out = nengo.Probe(model.out.state.output)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                <div id=\"51505d12-4825-46be-95e9-1f60e3840c3c\">\n",
       "                    <iframe\n",
       "                        src=\"http://localhost:65165\"\n",
       "                        width=\"100%\"\n",
       "                        height=\"600\"\n",
       "                        frameborder=\"0\"\n",
       "                        class=\"cell\"\n",
       "                        style=\"border: 1px solid #eee;\"\n",
       "                        allowfullscreen></iframe>\n",
       "                </div>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "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": 20,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "sim = nengo.Simulator(model)\n",
    "sim.run(3.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GI3fffTevvvoqf/vb3wBYs2YNjz76KIMGDcJoNPLiiy92yzX43FIRQkwFXkaJOfcfKeXz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3f0tIwH/DBubs29fhya3dlrdaevSAX35RyR5BhZ/7+alEvhkZagV3skluzx7lEH6KU9Qw0N+f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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12037ead0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "vocab = model.get_default_vocab(dimensions)\n",
    "\n",
    "plt.subplot(5, 1, 1)\n",
    "plt.plot(sim.trange(), model.similarity(sim.data, color_in))\n",
    "plt.legend(model.get_output_vocab('color_in').keys, fontsize='x-small')\n",
    "plt.ylabel(\"color\")\n",
    "\n",
    "plt.subplot(5, 1, 2)\n",
    "plt.plot(sim.trange(), model.similarity(sim.data, shape_in))\n",
    "plt.legend(model.get_output_vocab('shape_in').keys, fontsize='x-small')\n",
    "plt.ylabel(\"shape\")\n",
    "\n",
    "plt.subplot(5, 1, 3)\n",
    "plt.plot(sim.trange(), model.similarity(sim.data, cue))\n",
    "plt.legend(model.get_output_vocab('cue').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[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[out], vocab))\n",
    "plt.legend(model.get_output_vocab('out').keys, fontsize='x-small')\n",
    "plt.ylabel(\"output\")\n",
    "plt.xlabel(\"time [s]\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35200"
      ]
     },
     "execution_count": 22,
     "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 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  },
  "livereveal": {
   "scroll": true,
   "start_slideshow_at": "selected"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}