Nengo Tutorial.ipynb

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      "# Nengo Tutorial\n",
      "\n",
      "## Nengo\n",
      "\n",
      "- Our neural simulator\n",
      "- Implements the basics of what we cover in this class\n",
      "    - So you don't have to worry about the implementation details\n",
      "    - But understanding the theory will help you use it better\n",
      "    \n",
      "- website: [Nengo.ca](http://nengo.ca)\n",
      "- install: [Download Nengo](http://nengo.ca/download)\n",
      "    - use the \"latest development version of Nengo\"\n",
      "    - Java application; runs on Linux, OS X, Windows\n",
      "    - Open Source: [Github repository](https://github.com/ctn-waterloo/nengo_1.4)\n",
      "\n",
      "## How to use it\n",
      "\n",
      "- Drag and drop interface: [Tutorials](http://nengo.ca/docs/html/tutorial.html)\n",
      "- Python scripting: [Tutorials](http://nengo.ca/docs/html/scripting.html)\n",
      "- basic usage:\n",
      "    "
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     "input": [
      "import nef\n",
      "net = nef.Network('Example')\n",
      "net.make_input('input', [0])\n",
      "\n",
      "net.make('A', neurons=75, dimensions=1)\n",
      "net.make('B', neurons=50, dimensions=1)\n",
      "\n",
      "def square(x):\n",
      "    return x[0]*x[0]\n",
      "\n",
      "net.connect('A', 'B', func=square)\n",
      "\n",
      "net.connect('input', 'A')\n",
      "\n",
      "net.add_to_nengo()"
     ],
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      "- More advanced usage\n",
      "    - the ``make`` command builds ensembles\n",
      "         - [documentation](http://nengo.ca/docs/html/nef.Network.html#nef.Network.make)\n",
      "    - the ``connect`` command forms connections between ensembles\n",
      "         - [documentation](http://nengo.ca/docs/html/nef.Network.html#nef.Network.connect)"
     ]
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     "source": [
      "## The creature we made in class\n",
      "\n",
      "- Should set ``motor`` to be the direction we tell it to go in with ``command``, unless ``scared`` is 1, in which can it should try to go back to ``position`` (0,0)\n"
     ]
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     "input": [
      "import nef\n",
      "\n",
      "net = nef.Network('Creature')\n",
      "\n",
      "net.make_input('command_input', [0,0])\n",
      "\n",
      "net.make('command', neurons=100, dimensions=2)\n",
      "\n",
      "net.make('motor', neurons=100, dimensions=2)\n",
      "\n",
      "net.make('position', neurons=1000, dimensions=2, radius=5)\n",
      "\n",
      "net.make('scared_direction', neurons=100, dimensions=2)\n",
      "\n",
      "def negative(x):\n",
      "    return -x[0], -x[1]\n",
      "\n",
      "net.connect('position', 'scared_direction', func=negative)\n",
      "\n",
      "net.connect('position', 'position')\n",
      "\n",
      "net.make('plan', neurons=500, dimensions=5)\n",
      "net.connect('command', 'plan', index_post=[0,1])\n",
      "net.connect('scared_direction', 'plan', index_post=[2,3])\n",
      "net.make('scared', neurons=50, dimensions=1)\n",
      "net.make_input('scared_input', [0])\n",
      "net.connect('scared_input', 'scared')\n",
      "net.connect('scared', 'plan', index_post=[4])\n",
      "\n",
      "def plan_function(x):\n",
      "    c_x, c_y, s_x, s_y, s = x\n",
      "    return s*(s_x)+(1-s)*c_x, s*(s_y)+(1-s)*c_y\n",
      "    \n",
      "net.connect('plan', 'motor', func=plan_function)    \n",
      "\n",
      "\n",
      "def rescale(x):\n",
      "    return x[0]*0.1, x[1]*0.1\n",
      "\n",
      "net.connect('motor', 'position', func=rescale)\n",
      "\n",
      "\n",
      "net.connect('command_input', 'command')\n",
      "\n",
      "net.add_to_nengo()"
     ],
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