1_introduction.ipynb

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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Getting started\n",
      "======\n",
      "\n",
      "Replicated into ipython notebook form from http://www.nest-initiative.org/images/4/49/NEST_By_Example-2-6-0.pdf\n",
      "\n",
      "Import numpy into our session"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You may need this extra step if you installed nest into an alternate path, e.g. `/opt`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sys\n",
      "sys.path.append('/opt/lib/python2.7/site-packages/')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Our first simulation investigates the response of one integrate-and-fire neuron to an alternating current and Poisson spike trains from an excitatory and an inhibitory source.  \n",
      "We record the membrane potential of the neuron to observe how the stimuli influence the neuron.\n",
      "\n",
      "In this model, we inject a sine current with a frequency of 2 Hz and an amplitude of 100 pA into a neuron.  \n",
      "At the same time, the neuron receives random spiking input from two sources known as Poisson generators representing\n",
      "1. a large population of excitatory neurons\n",
      "2. a population of inhibitory neurons\n",
      "\n",
      "The rates of each population is set as the product of its assumed number of neurons and their average firing rate.\n",
      "This network is simulated for 1000 milliseconds, and its membrane potential is plotted vs time."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import nest\n",
      "import pylab\n",
      "import nest.voltage_trace"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "neuron = nest.Create(\"iaf_neuron\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Create the sine current generator"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sine = nest.Create('ac_generator', 1, {'amplitude': 100.0, 'frequency': 2.0})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Create a simulator of two large populations of neurons as poisson noise.\n",
      "\n",
      "Rates here correspond to the total spikes/s of the population."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "noise = nest.Create('poisson_generator', 2, [{'rate': 70000.0}, {'rate': 20000.0}])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Create a voltmeter to monitor membrane potentials"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "voltmeter = nest.Create('voltmeter', 1, {'withgid': True})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Make the connections\n",
      "1. Sine wave generator to neuron\n",
      "2. Voltmeter to neuron\n",
      "3. One noise ensemble as excitatory ensemble to neuron\n",
      "4. One noise ensemble as inhibitory ensemble to neuron"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nest.Connect(sine, neuron)\n",
      "nest.Connect(voltmeter, neuron)\n",
      "nest.Connect(noise[:1], neuron, syn_spec={'weight': 1.0, 'delay': 1.0})\n",
      "nest.Connect(noise[1:], neuron, syn_spec={'weight': -1.0, 'delay': 1.0})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Simulate the whole neuronal network for 1000 milliseconds"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nest.Simulate(1000.0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot the trace of the membrane potential"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nest.voltage_trace.from_device(voltmeter)\n",
      "pylab.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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bP15IVRN9LY/KSqBbN36D/vVX9X3//AM4OQFbtgBTp+o3DmF6pKXx/lUtWtT/GHl53C1l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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f6f8457f910>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Print the network structure"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nest.PrintNetwork()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    }
   ],
   "metadata": {}
  }
 ]
}