examples/simplecell/simplecell.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating a simple cell optimisation\n",
    "\n",
    "This notebook will explain how to set up an optimisation of simple single compartmental cell with two free parameters that need to be optimised.\n",
    "As this optimisation is for example purpose only, no real experimental data is used in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install matplotlib if needed\n",
    "!pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "%load_ext autoreload\n",
    "%autoreload"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we need to import the module that contains all the functionality to create electrical cell models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import bluepyopt as bpop\n",
    "import bluepyopt.ephys as ephys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to see a lot of information about the internals, \n",
    "the verbose level can be set to 'debug' by commenting out\n",
    "the following lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import logging\n",
    "# logger = logging.getLogger()\n",
    "# logger.setLevel(logging.DEBUG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting up a cell template\n",
    "-------------------------\n",
    "First a template that will describe the cell has to be defined. A template consists of:\n",
    "* a morphology\n",
    "* model mechanisms\n",
    "* model parameters\n",
    "\n",
    "### Creating a morphology\n",
    "A morphology can be loaded from a file (SWC or ASC)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "morph = ephys.morphologies.NrnFileMorphology('simple.swc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default a Neuron morphology has the following sectionlists: somatic, axonal, apical and basal. Let's create an object that points to the somatic sectionlist. This object will be used later to specify where mechanisms have to be added etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "somatic_loc = ephys.locations.NrnSeclistLocation('somatic', seclist_name='somatic')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Creating a mechanism\n",
    "\n",
    "Now we can add ion channels to this morphology. Let's add the default Neuron Hodgkin-Huxley mechanism to the soma. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "hh_mech = ephys.mechanisms.NrnMODMechanism(\n",
    "        name='hh',\n",
    "        suffix='hh',\n",
    "        locations=[somatic_loc])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The 'name' field can be chosen by the user, this name should be unique. The 'suffix' points to the same field in the NMODL file of the channel. 'locations' specifies which sections the mechanism will be added to.\n",
    "\n",
    "### Creating parameters\n",
    "\n",
    "Next we need to specify the parameters of the model. A parameter can be in two states: frozen and not-frozen. When a parameter is frozen it has an exact value, otherwise it only has some bounds but the exact value is not known yet.\n",
    "Let's define first a parameter that sets the capacitance of the soma to a frozen value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm_param = ephys.parameters.NrnSectionParameter(\n",
    "        name='cm',\n",
    "        param_name='cm',\n",
    "        value=1.0,\n",
    "        locations=[somatic_loc],\n",
    "        frozen=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And parameters that represent the maximal conductance of the sodium and potassium channels. These two parameters will be optimised later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "gnabar_param = ephys.parameters.NrnSectionParameter(                                    \n",
    "        name='gnabar_hh',\n",
    "        param_name='gnabar_hh',\n",
    "        locations=[somatic_loc],\n",
    "        bounds=[0.05, 0.125],\n",
    "        frozen=False)     \n",
    "gkbar_param = ephys.parameters.NrnSectionParameter(\n",
    "        name='gkbar_hh',\n",
    "        param_name='gkbar_hh',\n",
    "        bounds=[0.01, 0.075],\n",
    "        locations=[somatic_loc],\n",
    "        frozen=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Creating the template\n",
    "\n",
    "To create the cell template, we pass all these objects to the constructor of the template"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "simple_cell = ephys.models.CellModel(\n",
    "        name='simple_cell',\n",
    "        morph=morph,\n",
    "        mechs=[hh_mech],\n",
    "        params=[cm_param, gnabar_param, gkbar_param])  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can print out a description of the cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simple_cell:\n",
      "  morphology:\n",
      "    simple.swc\n",
      "  mechanisms:\n",
      "    hh: [<bluepyopt.ephys.locations.NrnSeclistLocation object at 0x109c30090>] hh\n",
      "  params:\n",
      "    cm: ['somatic'] cm = 1.0\n",
      "    gnabar_hh: ['somatic'] gnabar_hh = [0.05, 0.125]\n",
      "    gkbar_hh: ['somatic'] gkbar_hh = [0.01, 0.075]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(simple_cell)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "With this cell we can build a cell evaluator."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Setting up a cell evaluator\n",
    "\n",
    "To optimise the parameters of the cell we need to create cell evaluator object. \n",
    "This object will need to know which protocols to inject, which parameters to optimise, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Creating the protocols\n",
    "\n",
    "A protocol consists of a set of stimuli, and a set of responses (i.e. recordings). These responses will later be used to calculate\n",
    "the score of the parameter values.\n",
    "Let's create two protocols, two square current pulses at somatic`[0]`(0.5) with different amplitudes.\n",
    "We first need to create a location object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "soma_loc = ephys.locations.NrnSeclistCompLocation(\n",
    "        name='soma',\n",
    "        seclist_name='somatic',\n",
    "        sec_index=0,\n",
    "        comp_x=0.5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and then the stimuli, recordings and protocols. For each protocol we add a recording and a stimulus in the soma."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "sweep_protocols = []\n",
    "for protocol_name, amplitude in [('step1', 0.01), ('step2', 0.05)]:\n",
    "    stim = ephys.stimuli.NrnSquarePulse(\n",
    "                step_amplitude=amplitude,\n",
    "                step_delay=100,\n",
    "                step_duration=50,\n",
    "                location=soma_loc,\n",
    "                total_duration=200)\n",
    "    rec = ephys.recordings.CompRecording(\n",
    "            name='%s.soma.v' % protocol_name,\n",
    "            location=soma_loc,\n",
    "            variable='v')\n",
    "    protocol = ephys.protocols.SweepProtocol(protocol_name, [stim], [rec])\n",
    "    sweep_protocols.append(protocol)\n",
    "twostep_protocol = ephys.protocols.SequenceProtocol('twostep', protocols=sweep_protocols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Running a protocol on a cell\n",
    "\n",
    "Now we're at a stage where we can actually run a protocol on the cell. We first need to create a Simulator object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nrn = ephys.simulators.NrnSimulator()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The run() method of a protocol accepts a cell model, a set of parameter values and a simulator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "default_params = {'gnabar_hh': 0.1, 'gkbar_hh': 0.03}\n",
    "responses = twostep_protocol.run(cell_model=simple_cell, param_values=default_params, sim=nrn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the response traces is now easy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Oq1aFQLVyJbzxRqgB1tdDv37Qv38IWEceGYLukUemvx46NDz69k0vt6kp1BD37Wt9znzd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\nClAiIlKSFKBERKQk/X8VJQqrY+AsNwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109c37d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_responses(responses):\n",
    "    plt.subplot(2,1,1)\n",
    "    plt.plot(responses['step1.soma.v']['time'], responses['step1.soma.v']['voltage'], label='step1')\n",
    "    plt.legend()\n",
    "    plt.subplot(2,1,2)\n",
    "    plt.plot(responses['step2.soma.v']['time'], responses['step2.soma.v']['voltage'], label='step2')\n",
    "    plt.legend()\n",
    "    plt.tight_layout()\n",
    "\n",
    "plot_responses(responses)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, when we use different parameter values, the response looks different."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ClwGfMbPF0XIOyqgr0qQuXeIugRSr/fv3M2XKFPr06cOAAQPYunUr06ZN4+KL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\niYhIQfr/vuwszbfvtwAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109c92750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "other_params = {'gnabar_hh': 0.05, 'gkbar_hh': 0.05}\n",
    "plot_responses(twostep_protocol.run(cell_model=simple_cell, param_values=other_params, sim=nrn))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Defining eFeatures and objectives\n",
    "\n",
    "For every response we need to define a set of eFeatures we will use for the fitness calculation later. We have to combine features together into objectives that will be used by the optimalisation algorithm. In this case we will create one objective per feature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "efel_feature_means = {'step1': {'Spikecount': 1}, 'step2': {'Spikecount': 5}}\n",
    "\n",
    "objectives = []\n",
    "\n",
    "for protocol in sweep_protocols:\n",
    "    stim_start = protocol.stimuli[0].step_delay\n",
    "    stim_end = stim_start + protocol.stimuli[0].step_duration\n",
    "    for efel_feature_name, mean in efel_feature_means[protocol.name].items():\n",
    "        feature_name = '%s.%s' % (protocol.name, efel_feature_name)\n",
    "        feature = ephys.efeatures.eFELFeature(\n",
    "                    feature_name,\n",
    "                    efel_feature_name=efel_feature_name,\n",
    "                    recording_names={'': '%s.soma.v' % protocol.name},\n",
    "                    stim_start=stim_start,\n",
    "                    stim_end=stim_end,\n",
    "                    exp_mean=mean,\n",
    "                    exp_std=0.05 * mean)\n",
    "        objective = ephys.objectives.SingletonObjective(\n",
    "            feature_name,\n",
    "            feature)\n",
    "        objectives.append(objective)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating the cell evaluator\n",
    "\n",
    "We will need an object that can use these objective definitions to calculate the scores from a protocol response. This is called a ScoreCalculator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_calc = ephys.objectivescalculators.ObjectivesCalculator(objectives) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combining everything together we have a CellEvaluator. The CellEvaluator constructor has a field 'parameter_names' which contains the (ordered) list of names of the parameters that are used as input (and will be fitted later on)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "cell_evaluator = ephys.evaluators.CellEvaluator(\n",
    "        cell_model=simple_cell,\n",
    "        param_names=['gnabar_hh', 'gkbar_hh'],\n",
    "        fitness_protocols={twostep_protocol.name: twostep_protocol},\n",
    "        fitness_calculator=score_calc,\n",
    "        sim=nrn)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluating the cell\n",
    "\n",
    "The cell can now be evaluate for a certain set of parameter values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'step2.Spikecount': 0.0, 'step1.Spikecount': 0.0}\n"
     ]
    }
   ],
   "source": [
    "print(cell_evaluator.evaluate_with_dicts(default_params))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Setting up and running an optimisation\n",
    "\n",
    "Now that we have a cell template and an evaluator for this cell, we can set up an optimisation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimisation = bpop.optimisations.DEAPOptimisation(\n",
    "        evaluator=cell_evaluator,\n",
    "        offspring_size = 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And this optimisation can be run for a certain number of generations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gen\tnevals\tavg  \tstd    \tmin\tmax\n",
      "1  \t10    \t149.4\t189.154\t4  \t500\n",
      "2  \t10    \t137.9\t173.863\t4  \t500\n",
      "3  \t10    \t25   \t15.4337\t4  \t40 \n",
      "4  \t10    \t10   \t13.5941\t0  \t40 \n",
      "5  \t10    \t7.4  \t12.7922\t0  \t40 \n"
     ]
    }
   ],
   "source": [
    "final_pop, hall_of_fame, logs, hist = optimisation.run(max_ngen=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The optimisation has return us 4 objects: final population, hall of fame, statistical logs and history. \n",
    "\n",
    "The final population contains a list of tuples, with each tuple representing the two parameters of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final population:  [[0.10250198363407481, 0.027124836082684685], [0.11390738180576473, 0.03287250134852333], [0.10250198363407481, 0.027124836082684685], [0.10250198363407481, 0.027124836082684685], [0.08975403922850962, 0.027124836082684685], [0.10563659695553552, 0.029575522695622657], [0.10101393431903045, 0.026690299820979417], [0.10299326453483033, 0.027124836082684685], [0.10728309642324604, 0.02657948667306241], [0.11010771065262573, 0.02657948667306241], [0.11382709430476985, 0.04098326369171712], [0.11382709430476985, 0.04098326369171712], [0.1170346997494812, 0.036309352642717674], [0.09315627382586422, 0.03338461398216595], [0.09315627382586422, 0.03338461398216595], [0.11382709430476985, 0.04098326369171712], [0.1170346997494812, 0.036309352642717674], [0.09157830984841198, 0.029628023252804073], [0.09315627382586422, 0.03427793299271563], [0.11284691872242712, 0.04421675460560999]]\n"
     ]
    }
   ],
   "source": [
    "print('Final population: ', final_pop)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best individual found during the optimisation is the first individual of the hall of fame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best individual:  [0.10299326453483033, 0.027124836082684685]\n",
      "Fitness values:  (0.0, 0.0)\n"
     ]
    }
   ],
   "source": [
    "best_ind = hall_of_fame[0]\n",
    "print('Best individual: ', best_ind)\n",
    "print('Fitness values: ', best_ind.fitness.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can evaluate this individual and make use of a convenience function of the cell evaluator to return us a dict of the parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'step2.Spikecount': 0.0, 'step1.Spikecount': 0.0}\n"
     ]
    }
   ],
   "source": [
    "best_ind_dict = cell_evaluator.param_dict(best_ind)\n",
    "print(cell_evaluator.evaluate_with_dicts(best_ind_dict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see the evaluation returns the same values as the fitness values provided by the optimisation output. \n",
    "We can have a look at the responses now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iitqCqgG+6+6HAscAl5vZOJRuQySvujro3j0Eqd274y07Pb61cWO85SbVGpk3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\nRESkLP1/QNTmhvEqndUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a23ef90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_responses(twostep_protocol.run(cell_model=simple_cell, param_values=best_ind_dict, sim=nrn))\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at the optimisation statistics.\n",
    "We can plot the minimal score (sum of all objective scores) found in every optimisation. \n",
    "The optimisation algorithm uses negative fitness scores, so we actually have to look at the maximum values log."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 4.4000000000000004)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TRNM8EngKOFJSA/Br4BLgsnZtHgCuBu5Nisb29gUAuv8QZmZ2cFIrAhHxnqSr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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109fe7e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy\n",
    "gen_numbers = logs.select('gen')\n",
    "min_fitness = logs.select('min')\n",
    "max_fitness = logs.select('max')\n",
    "plt.plot(gen_numbers, min_fitness, label='min fitness')\n",
    "plt.xlabel('generation #')\n",
    "plt.ylabel('score (# std)')\n",
    "plt.legend()\n",
    "plt.xlim(min(gen_numbers) - 1, max(gen_numbers) + 1) \n",
    "plt.ylim(0.9*min(min_fitness), 1.1 * max(min_fitness)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}