nengo_bioneurons/tests/wilson_compare.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "import nengo\n",
    "from nengo.utils.matplotlib import rasterplot\n",
    "from nengo.params import Default, NumberParam\n",
    "from nengo.dists import Uniform\n",
    "from nengo.neurons import *\n",
    "from nengo.builder.neurons import *\n",
    "from nengo.dists import Uniform\n",
    "from nengo.solvers import NoSolver\n",
    "from nengo.base import ObjView\n",
    "from nengo.builder import Builder, Operator, Signal\n",
    "from nengo.exceptions import BuildError\n",
    "from nengo.builder.connection import build_decoders, BuiltConnection\n",
    "from nengo.utils.builder import full_transform\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set(context='poster', style='whitegrid')\n",
    "%matplotlib inline\n",
    "\n",
    "import time\n",
    "\n",
    "import sys\n",
    "\n",
    "from brian2 import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neuron Type Definitions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4th order Runge-Kutta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "class WilsonRK4(NeuronType):\n",
    "\n",
    "    probeable = ('spikes', 'voltage', 'recovery', 'conductance')\n",
    "    threshold = NumberParam('threshold')\n",
    "    tau_V = NumberParam('tau_V')\n",
    "    tau_R = NumberParam('tau_R')\n",
    "    tau_H = NumberParam('tau_H')\n",
    "    \n",
    "    _v0 = -0.754  # initial voltage\n",
    "    _r0 = 0.279  # initial recovery\n",
    "    _maxJ = 2.0  # clip input current at this maximum to avoid catastrophic shutdown\n",
    "    \n",
    "    def __init__(self, threshold=-0.20, tau_V=0.00097, tau_R=0.0056, tau_H=0.0990):\n",
    "        super(WilsonRK4, self).__init__()\n",
    "        self.threshold = threshold\n",
    "        self.tau_V = tau_V\n",
    "        self.tau_R = tau_R\n",
    "        self.tau_H = tau_H\n",
    "        \n",
    "        # TODO(arvoelke): Try replacing this solver with something like\n",
    "        # http://www2.gsu.edu/~matrhc/PyDSTool.htm\n",
    "        # The main consideration is that we need a callback to count spikes\n",
    "        from scipy.integrate import ode\n",
    "        self.solver = ode(self._ode_fun).set_integrator(\n",
    "            'dopri5', first_step=0.00005, nsteps=100,\n",
    "            rtol=1e-2, atol=1e-3)  # runge-kutta method of order (4)5\n",
    "        \n",
    "    def rates(self, x, gain, bias):\n",
    "        \"\"\"Estimates steady-state firing rate given gain and bias.\"\"\"\n",
    "        J = self.current(x, gain, bias)\n",
    "        voltage = self._v0*np.ones_like(J)\n",
    "        recovery = self._r0*np.ones_like(J)\n",
    "        conductance = np.zeros_like(J)\n",
    "\n",
    "        return settled_firingrate(\n",
    "            self.step_math, J, [voltage, recovery, conductance],\n",
    "            dt=0.001, settle_time=0.1, sim_time=1.0)\n",
    "\n",
    "    def _ode_fun(self, dummy_t, y, J):  # first argument to scipy.integrate.ode\n",
    "        V, R, H = np.split(y, 3)\n",
    "        dV = (-(17.81 + 47.58*V + 33.80*np.square(V))*(V - 0.48) -\n",
    "              26*R*(V + 0.95) - 13*H*(V + 0.95) + J)\n",
    "        dR = -R + 1.29*V + 0.79 + 3.30*np.square(V + 0.38)\n",
    "        dH = -H + 11*(V + 0.754)*(V + 0.69)\n",
    "        return np.concatenate((\n",
    "            dV / self.tau_V, dR / self.tau_R, dH / self.tau_H))\n",
    "\n",
    "    def step_math(self, dt, J, spiked, V, R, H):\n",
    "        # It's a little silly to be reinitializing the solver on\n",
    "        # every time-step, but any other ways that I could think of would \n",
    "        # violate the nengo builder's assumption that the neuron's state is\n",
    "        # encapsulated by the signals in SimNeurons\n",
    "        self.solver.set_initial_value(np.concatenate((V, R, H)))\n",
    "        self.solver.set_f_params(J.clip(max=self._maxJ))\n",
    "        \n",
    "        spiked[:] = 0\n",
    "        AP = V > self.threshold\n",
    "        def spike_detector(dummy_t, y):  # callback for each sub-step\n",
    "            V_t = y[:len(V)] > self.threshold\n",
    "            spiked[:] += V_t & (~AP)  # note the \"+=\"\n",
    "            AP[:] = V_t\n",
    "        self.solver.set_solout(spike_detector)\n",
    "\n",
    "        V[:], R[:], H[:] = np.split(self.solver.integrate(self.solver.t + dt), 3)\n",
    "        if not self.solver.successful():\n",
    "            raise ValueError(\"ODE solver failed with status code: %d\" % (\n",
    "                self.solver.get_return_code()))\n",
    "        spiked[:] /= dt\n",
    "\n",
    "        return spiked, V, R, H\n",
    "\n",
    "\n",
    "@Builder.register(WilsonRK4)\n",
    "def build_wilsonneuron(model, neuron_type, neurons):\n",
    "    model.sig[neurons]['voltage'] = Signal(\n",
    "        neuron_type._v0*np.ones(neurons.size_in), name=\"%s.voltage\" % neurons)\n",
    "    model.sig[neurons]['recovery'] = Signal(\n",
    "        neuron_type._r0*np.ones(neurons.size_in), name=\"%s.recovery\" % neurons)\n",
    "    model.sig[neurons]['conductance'] = Signal(\n",
    "        np.zeros(neurons.size_in), name=\"%s.conductance\" % neurons)\n",
    "    model.add_op(SimNeurons(\n",
    "        neurons=neuron_type,\n",
    "        J=model.sig[neurons]['in'],\n",
    "        output=model.sig[neurons]['out'],\n",
    "        states=[model.sig[neurons]['voltage'],\n",
    "                model.sig[neurons]['recovery'],\n",
    "                model.sig[neurons]['conductance']]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Euler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "class WilsonEuler(NeuronType):\n",
    "\n",
    "    probeable = ('spikes', 'voltage', 'recovery', 'conductance', 'AP')\n",
    "    threshold = NumberParam('threshold')\n",
    "    tau_V = NumberParam('tau_V')\n",
    "    tau_R = NumberParam('tau_R')\n",
    "    tau_H = NumberParam('tau_H')\n",
    "    \n",
    "    _v0 = -0.754  # initial voltage\n",
    "    _r0 = 0.279  # initial recovery\n",
    "    _maxJ = 2.0  # clip input current at this maximum to avoid catastrophic shutdown\n",
    "    \n",
    "    def __init__(self, threshold=-0.20, tau_V=0.00097, tau_R=0.0056, tau_H=0.0990):\n",
    "        super(WilsonEuler, self).__init__()\n",
    "        self.threshold = threshold\n",
    "        self.tau_V = tau_V\n",
    "        self.tau_R = tau_R\n",
    "        self.tau_H = tau_H\n",
    "        \n",
    "    @property\n",
    "    def _argreprs(self):\n",
    "        args = []\n",
    "        def add(attr, default):\n",
    "            if getattr(self, attr) != default:\n",
    "                args.append(\"%s=%s\" %(attr, getattr(self, attr)))\n",
    "        add(\"threshold\", -0.20)\n",
    "        add(\"tau_V\", 0.00097)\n",
    "        add(\"tau_R\", 0.0056)\n",
    "        add(\"tau_H\", 0.0990)\n",
    "        return args\n",
    "\n",
    "\n",
    "    def gain_bias(self, max_rates, intercepts):\n",
    "        max_rates = np.array(max_rates, dtype=float, copy=False, ndmin=1)\n",
    "        intercepts = np.array(intercepts, dtype=float, copy=False, ndmin=1)\n",
    "\n",
    "        J_steps = 201  # Odd number so that 0 is a sample\n",
    "        max_rate = max_rates.max()\n",
    "\n",
    "        # Find range of J that will achieve max rates (assume monotonic)\n",
    "        J_threshold = None\n",
    "        J_max = None\n",
    "        Jr = 1.0\n",
    "        for _ in range(10):\n",
    "            J = np.linspace(-Jr, Jr, J_steps)\n",
    "            rate = self.rates(J, np.ones(J_steps), np.zeros(J_steps))\n",
    "#             print('J', J, 'euler rate', rate)\n",
    "            if J_threshold is None and (rate <= 0).any():\n",
    "                J_threshold = J[np.where(rate <= 0)[0][-1]]\n",
    "            if J_max is None and (rate >= max_rate).any():\n",
    "                J_max = J[np.where(rate >= max_rate)[0][0]]\n",
    "            if J_threshold is not None and J_max is not None:\n",
    "                break\n",
    "            else:\n",
    "                Jr *= 2\n",
    "        else:\n",
    "            if J_threshold is None:\n",
    "                raise RuntimeError(\"Could not find firing threshold\")\n",
    "            if J_max is None:\n",
    "                raise RuntimeError(\"Could not find max current\")\n",
    "\n",
    "        J = np.linspace(J_threshold, J_max, J_steps)\n",
    "        rate = self.rates(J, np.ones(J_steps), np.zeros(J_steps))\n",
    "        gain = np.zeros_like(max_rates)\n",
    "        bias = np.zeros_like(max_rates)\n",
    "        J_tops = np.interp(max_rates, rate, J)\n",
    "        gain[:] = (J_threshold - J_tops) / (intercepts - 1)\n",
    "        bias[:] = J_tops - gain\n",
    "\n",
    "        return gain, bias\n",
    "\n",
    "    def rates(self, x, gain, bias):\n",
    "        \"\"\"Estimates steady-state firing rate given gain and bias.\"\"\"\n",
    "        J = self.current(x, gain, bias)\n",
    "        voltage = self._v0*np.ones_like(J)\n",
    "        recovery = self._r0*np.ones_like(J)\n",
    "        conductance = np.zeros_like(J)\n",
    "        AP = np.zeros_like(J, dtype=bool)\n",
    "\n",
    "        return settled_firingrate(\n",
    "            self.step_math, J, [voltage, recovery, conductance, AP],\n",
    "            dt=0.00005, settle_time=0.1, sim_time=1.0)\n",
    "\n",
    "\n",
    "    def step_math(self, dt, J, spiked, V, R, H, AP):\n",
    "        if np.abs(J).any() >= 2.0:\n",
    "            warnings.warn(\"input current exceeds failure point; clipping\")\n",
    "            J = J.clip(max=self._maxJ)\n",
    "\n",
    "        dV = -(17.81 + 47.58*V + 33.80*np.square(V))*(V - 0.48) - 26*R*(V + 0.95) - 13*H*(V + 0.95) + J\n",
    "        dR = -R + 1.29*V + 0.79 + 3.30*np.square(V + 0.38)\n",
    "        dH = -H + 11*(V + 0.754)*(V + 0.69)\n",
    "        \n",
    "        V[:] = (V + dV * dt/self.tau_V).clip(-0.9, 0.3)\n",
    "        R[:] = (R + dR * dt/self.tau_R)  # .clip(0.18, 0.42)\n",
    "        H[:] = (H + dH * dt/self.tau_H)  # .clip(0, 0.23)\n",
    "        spiked[:] = (V > self.threshold) & (~AP)\n",
    "        spiked /= dt\n",
    "        AP[:] = V > self.threshold\n",
    "        \n",
    "        return spiked, V, R, H, AP\n",
    "\n",
    "\n",
    "@Builder.register(WilsonEuler)\n",
    "def build_wilsonneuron(model, neuron_type, neurons):\n",
    "    model.sig[neurons]['voltage'] = Signal(\n",
    "        neuron_type._v0*np.ones(neurons.size_in), name=\"%s.voltage\" % neurons)\n",
    "    model.sig[neurons]['recovery'] = Signal(\n",
    "        neuron_type._r0*np.ones(neurons.size_in), name=\"%s.recovery\" % neurons)\n",
    "    model.sig[neurons]['conductance'] = Signal(\n",
    "        np.zeros(neurons.size_in), name=\"%s.conductance\" % neurons)\n",
    "    model.sig[neurons]['AP'] = Signal(\n",
    "        np.zeros(neurons.size_in, dtype=bool), name=\"%s.AP\" % neurons)\n",
    "    model.add_op(SimNeurons(\n",
    "        neurons=neuron_type,\n",
    "        J=model.sig[neurons]['in'],\n",
    "        output=model.sig[neurons]['out'],\n",
    "        states=[model.sig[neurons]['voltage'],\n",
    "            model.sig[neurons]['recovery'],\n",
    "            model.sig[neurons]['conductance'],\n",
    "            model.sig[neurons]['AP']]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Brian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING    /home/pduggins/nengo/nengo/builder/builder.py:234: UserWarning: Type '<class 'nengo.connection.Connection'>' already has a builder. Overwriting.\n",
      "  % nengo_class)\n",
      " [py.warnings]\n",
      "WARNING:py.warnings:/home/pduggins/nengo/nengo/builder/builder.py:234: UserWarning: Type '<class 'nengo.connection.Connection'>' already has a builder. Overwriting.\n",
      "  % nengo_class)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "BrianLogger.suppress_name('resolution_conflict')\n",
    "\n",
    "class WilsonBrian(NeuronType):\n",
    "    \n",
    "    probeable = ('spikes', 'voltage', 'recovery', 'conductance')\n",
    "    tauv = NumberParam('tauv')\n",
    "    taur = NumberParam('taur')\n",
    "    tauc = NumberParam('tauc')\n",
    "    \n",
    "    _v0 = -0.754  # initial voltage\n",
    "    _r0 = 0.279  # initial recovery\n",
    "    _maxJ = 2.0  # clip input current at this maximum to avoid catastrophic shutdown\n",
    "\n",
    "    def __init__(self, dt_brian=0.00005, ode='euler', thr=-0.20, tauv=0.00097, taur=0.0056, tauc=0.0990):\n",
    "        super(WilsonBrian, self).__init__()\n",
    "        self.dt_brian = dt_brian\n",
    "        self.ode = ode\n",
    "        self.thr = thr\n",
    "        self.tauv = tauv\n",
    "        self.taur = taur\n",
    "        self.tauc = tauc\n",
    "        self.namespace = {'tauv': self.tauv, 'taur': self.taur, 'tauc': self.tauc, 'thr': self.thr}\n",
    "\n",
    "        self.eqns = '''\n",
    "        # v = clip(v, -0.9, 0.3) : 1\n",
    "        dv/dt = Hz/tauv * (-(17.81 + 47.58*v + 33.80*v*v)*(v - 0.48)\n",
    "            - 26*r*(v + 0.95) - 13*c*(v + 0.95) + J) : 1\n",
    "        dr/dt = Hz/taur * (-r + 1.29*v + 0.79 + 3.30*(v + 0.38)*(v + 0.38)) : 1\n",
    "        dc/dt = Hz/tauc * (-c + 11*(v + 0.754)*(v + 0.69)) : 1\n",
    "        J : 1\n",
    "        '''\n",
    "        \n",
    "    @property\n",
    "    def _argreprs(self):\n",
    "        args = []\n",
    "        def add(attr, default):\n",
    "            if getattr(self, attr) != default:\n",
    "                args.append(\"%s=%s\" %(attr, getattr(self, attr)))\n",
    "        add(\"tauv\", 0.00097)\n",
    "        add(\"taur\", 0.0056)\n",
    "        add(\"tauh\", 0.0990)\n",
    "        return args\n",
    "        \n",
    "    def gain_bias(self, max_rates, intercepts):\n",
    "        max_rates = np.array(max_rates, dtype=float, copy=False, ndmin=1)\n",
    "        intercepts = np.array(intercepts, dtype=float, copy=False, ndmin=1)\n",
    "\n",
    "        J_steps = 101  # Odd number so that 0 is a sample\n",
    "        max_rate = max_rates.max()\n",
    "\n",
    "        # Find range of J that will achieve max rates (assume monotonic)\n",
    "        J_threshold = None\n",
    "        J_max = None\n",
    "        Jr = 1.0\n",
    "        for _ in range(10):\n",
    "            J_test = np.linspace(-Jr, Jr, J_steps)\n",
    "            rate = self.rates(J_test, np.ones(J_steps), np.zeros(J_steps))\n",
    "#             print('J', J_test, 'brian rate', rate)\n",
    "            if J_threshold is None and (rate <= 0).any():\n",
    "                J_threshold = J_test[np.where(rate <= 0)[0][-1]]\n",
    "            if J_max is None and (rate >= max_rate).any():\n",
    "                J_max = J_test[np.where(rate >= max_rate)[0][0]]\n",
    "            if J_threshold is not None and J_max is not None:\n",
    "                break\n",
    "            else:\n",
    "                Jr *= 2\n",
    "        else:\n",
    "            if J_threshold is None:\n",
    "                raise RuntimeError(\"Could not find firing threshold\")\n",
    "            if J_max is None:\n",
    "                raise RuntimeError(\"Could not find max current\")\n",
    "\n",
    "        J = np.linspace(J_threshold, J_max, J_steps)\n",
    "        rate = self.rates(J, np.ones(J_steps), np.zeros(J_steps))\n",
    "        gain = np.zeros_like(max_rates)\n",
    "        bias = np.zeros_like(max_rates)\n",
    "        J_tops = np.interp(max_rates, rate, J)\n",
    "        gain[:] = (J_threshold - J_tops) / (intercepts - 1)\n",
    "        bias[:] = J_tops - gain\n",
    "\n",
    "        return gain, bias\n",
    "\n",
    "    def rates(self, x, gain, bias):\n",
    "        \"\"\"Estimates steady-state firing rate given gain and bias.\"\"\"\n",
    "        J_test = self.current(x, gain, bias)\n",
    "        brianens = NeuronGroup(J_test.shape[0], self.eqns,\n",
    "            threshold='v > thr', namespace=self.namespace, method=self.ode, refractory=5*ms)\n",
    "        brianens.v = self._v0*np.ones_like(J_test)\n",
    "        brianens.r = self._r0*np.ones_like(J_test)\n",
    "        brianens.c = np.zeros_like(J_test)\n",
    "        brianmon_v = StateMonitor(brianens, 'v', record=True)\n",
    "        brianmon_s = SpikeMonitor(brianens, record=True)\n",
    "        briannet = Network(brianens, brianmon_v, brianmon_s)\n",
    "        defaultclock.dt = self.dt_brian*second\n",
    "        \n",
    "        # reimplement settled_firingrate()\n",
    "        settle_time = 0.1\n",
    "        sim_time = 1.0\n",
    "        dt = 0.001  # dt_nengo\n",
    "        settle_steps = int(settle_time / dt)\n",
    "        sim_steps = int(sim_time / dt)\n",
    "        spiked = np.zeros_like(J_test)\n",
    "        voltage = np.array(brianens.v)\n",
    "        all_spikes = np.zeros_like(J_test)\n",
    "        print(\"running transients...\")\n",
    "        for t in range(settle_steps):\n",
    "            v, s = self.step_math(J_test, briannet, brianens, brianmon_v, brianmon_s, voltage, spiked, dt)\n",
    "            sys.stdout.write(\"\\r{:6.2f}% done...\".format(100 * (t + 1) / settle_steps))\n",
    "        sys.stdout.write(\"\\n\")\n",
    "        print(\"calculating rate...\")\n",
    "        for t in range(sim_steps):\n",
    "            v, s = self.step_math(J_test, briannet, brianens, brianmon_v, brianmon_s, voltage, spiked, dt)\n",
    "            all_spikes += s*dt\n",
    "            sys.stdout.write(\"\\r{:6.2f}% done...\".format(100 * (t + 1) / sim_steps))\n",
    "        sys.stdout.write(\"\\n\")\n",
    "\n",
    "        return all_spikes / sim_time\n",
    "    \n",
    "    def step_math(self, J, briannet, brianens, brianmon_v, brianmon_s, voltage, spiked, dt):\n",
    "        spiked_before = brianmon_s.i[:]\n",
    "        brianens.J = J.clip(max=_maxJ)  # drive brian neurons\n",
    "        briannet.run(dt*second)  # run brian neurons\n",
    "        brianens.v = brianens.v[:].clip(-0.9, 0.3)\n",
    "        voltage[:] = brianens.v\n",
    "        spiked = np.zeros_like(voltage)\n",
    "        spiked_after = brianmon_s.i[:]\n",
    "        n_spikes_new = spiked_after.shape[0] - spiked_before.shape[0]\n",
    "        for s in range(n_spikes_new):\n",
    "            spiked[spiked_after[-(s+1)]] = 1.0/dt\n",
    "        return voltage, spiked\n",
    "\n",
    "\n",
    "class SimBrianNeurons(Operator):\n",
    "    def __init__(self, neuron_type, neurons, J, output, states):\n",
    "        super(SimBrianNeurons, self).__init__()\n",
    "        self.neuron_type = neuron_type\n",
    "        self.neurons = neurons\n",
    "        self.reads = [states[0], J]\n",
    "        self.sets = [output, states[1]]\n",
    "        self.updates = []\n",
    "        self.incs = []\n",
    "        \n",
    "        self.neurons.v = states[1].initial_value\n",
    "        self.neurons.r = states[2].initial_value\n",
    "        self.neurons.c = states[3].initial_value\n",
    "        self.brianens = neurons\n",
    "        self.brianmon_v = StateMonitor(self.brianens, 'v', record=True)\n",
    "        self.brianmon_s = SpikeMonitor(self.brianens, record=True)\n",
    "        self.briannet = Network(self.brianens, self.brianmon_v, self.brianmon_s)\n",
    "        defaultclock.dt = self.neuron_type.dt_brian*second\n",
    "\n",
    "    def make_step(self, signals, dt, rng):\n",
    "        J = signals[self.current]\n",
    "        output = signals[self.output]\n",
    "        voltage = signals[self.voltage]\n",
    "        time = signals[self.time]\n",
    "        def step_nrn():\n",
    "            self.neuron_type.step_math(J, self.briannet, self.brianens, \n",
    "                self.brianmon_v, self.brianmon_s, voltage, output, dt)  # output==spiked?\n",
    "        return step_nrn\n",
    "\n",
    "    @property\n",
    "    def time(self):\n",
    "        return self.reads[0]\n",
    "    @property\n",
    "    def current(self):\n",
    "        return self.reads[1]\n",
    "    @property\n",
    "    def output(self):\n",
    "        return self.sets[0]\n",
    "    @property\n",
    "    def voltage(self):\n",
    "        return self.sets[1]\n",
    "    \n",
    "@Builder.register(WilsonBrian)\n",
    "def build_wilsonneuron(model, neuron_type, neurons):\n",
    "    \n",
    "    model.sig[neurons]['voltage'] = Signal(\n",
    "        neuron_type._v0*np.ones(neurons.size_in), name=\"%s.voltage\" % neurons)\n",
    "    model.sig[neurons]['recovery'] = Signal(\n",
    "        neuron_type._r0*np.ones(neurons.size_in), name=\"%s.recovery\" % neurons)\n",
    "    model.sig[neurons]['conductance'] = Signal(\n",
    "        np.zeros(neurons.size_in), name=\"%s.conductance\" % neurons)\n",
    "    thr = neuron_type.thr\n",
    "\n",
    "    brianens = NeuronGroup(neurons.size_in, neuron_type.eqns,\n",
    "        threshold='v > thr', namespace=neuron_type.namespace, method=neuron_type.ode, refractory=5*ms)\n",
    "    \n",
    "    brianop = SimBrianNeurons(\n",
    "        neuron_type=neuron_type,\n",
    "        neurons=brianens,\n",
    "        J=model.sig[neurons]['in'],\n",
    "        output=model.sig[neurons]['out'],\n",
    "        states=[\n",
    "            model.time,\n",
    "            model.sig[neurons]['voltage'],\n",
    "            model.sig[neurons]['recovery'],\n",
    "            model.sig[neurons]['conductance']])\n",
    "\n",
    "    model.params[neurons] = {'briannet': brianop.briannet, 'brianens': brianop.brianens}\n",
    "    model.add_op(brianop)\n",
    "\n",
    "\n",
    "class TransmitSpikes(Operator):\n",
    "    def __init__(self, conn, spikes, states):\n",
    "        super(TransmitSpikes, self).__init__()\n",
    "        self.conn = conn\n",
    "        self.time = states[0]\n",
    "        self.reads = [spikes, states[0]]\n",
    "        self.updates = []\n",
    "        self.sets = []\n",
    "        self.incs = []\n",
    "        \n",
    "    def make_step(self, signals, dt, rng):\n",
    "        spikes = signals[self.spikes]\n",
    "        time = signals[self.time]\n",
    "        def step():\n",
    "            spiked = np.where(spikes > 0)[0]\n",
    "            spiketimes = time * np.ones((spiked.shape[0])) * ms\n",
    "            self.conn.briansgg.set_spikes(spiked, spiketimes)\n",
    "        return step\n",
    "\n",
    "    @property\n",
    "    def spikes(self):\n",
    "        return self.reads[0]\n",
    "\n",
    "\n",
    "@Builder.register(nengo.Connection)\n",
    "def build_connection(model, conn):\n",
    "    rng = np.random.RandomState(model.seeds[conn])\n",
    "\n",
    "    if isinstance(conn.post_obj, nengo.Ensemble) and isinstance(conn.post_obj.neuron_type, WilsonBrian) \\\n",
    "            and isinstance(conn.pre_obj, nengo.Ensemble):\n",
    "        if not isinstance(conn.solver, NoSolver) and conn.solver.weights is False:\n",
    "            raise BuildError(\"Connections to WilsonNeuorns must provide a NoSolver with weight=True\"\n",
    "                            \" (got %s from %s to %s)\" % (conn.solver, conn.pre_obj, conn.post_obj))\n",
    "        #todo: check that conn_pre is emitting spikes\n",
    "        briannet = model.params[conn.post_obj.neurons]['briannet']\n",
    "        brianens = model.params[conn.post_obj.neurons]['brianens']\n",
    "        briansgg = SpikeGeneratorGroup(conn.pre_obj.n_neurons, indices=[], times=[]*ms)  # filled in during TransmitSpikes\n",
    "        transform = full_transform(conn, slice_pre=False)\n",
    "        w = build_decoders(model, conn, rng, transform)[1]\n",
    "        \n",
    "        syn_eqn = '''\n",
    "        dg/dt = -tau*g/second : 1 (clock-driven)\n",
    "        I_post = g : 1 (summed)\n",
    "        w : 1\n",
    "        '''\n",
    "        briansyn = Synapses(briansgg, brianens, namespace={'tau': conn.synapse.tau},\n",
    "            model=syn_eqn, on_pre='g += w', method='rk4')  # rk4\n",
    "        briansyn.connect()\n",
    "        for pre in range(w.shape[0]):\n",
    "            for post in range(w.shape[1]):\n",
    "                briansyn.w[pre, post] = w[pre, post]\n",
    "#         briansyn.w[:, :] = w['i, j']\n",
    "#         briansyn.w = w.ravel()  # todo: ensure indexing is correct\n",
    "        briannet.add(briansgg)\n",
    "        briannet.add(briansyn)\n",
    "        conn.briansgg = briansgg\n",
    "        \n",
    "        brianspiker = TransmitSpikes(conn, model.sig[conn.pre_obj]['out'], states=[model.time])\n",
    "        model.add_op(brianspiker)\n",
    "\n",
    "    else:\n",
    "        return nengo.builder.connection.build_connection(model, conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def go(n_neurons, neuron_type, t, dt, sample_every=0.001, max_rates=Uniform(30, 60), seed=0):\n",
    "    with nengo.Network() as model:\n",
    "        stim = nengo.Node(output=lambda t: np.sin(2*np.pi*5*t))\n",
    "        x = nengo.Ensemble(n_neurons, 1, seed=seed, neuron_type=neuron_type, max_rates=max_rates)\n",
    "        nengo.Connection(stim, x, synapse=None)\n",
    "#         nengo.Connection(stim, x.neurons, synapse=None)\n",
    "        p = nengo.Probe(x.neurons, synapse=0.1, sample_every=sample_every)\n",
    "        p_s = nengo.Probe(x.neurons, 'spikes', synapse=None, sample_every=sample_every)\n",
    "        p_v = nengo.Probe(x.neurons, 'voltage', synapse=None, sample_every=sample_every)\n",
    "    \n",
    "    with nengo.Simulator(model, dt=dt) as sim:\n",
    "        sim.run(t)\n",
    "    \n",
    "    return sim.trange(sample_every=sample_every), sim.data[p], sim.data[p_s], sim.data[p_v]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Euler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vdom.v1+json": {
       "attributes": {},
       "tagName": "div"
      },
      "text/html": [
       "\n",
       "                <script>\n",
       "                    if (Jupyter.version.split(\".\")[0] < 5) {\n",
       "                        var pb = document.getElementById(\"0cd42eba-5dfd-446c-8486-fb4bf7c1d3e5\");\n",
       "                        var text = document.createTextNode(\n",
       "                            \"HMTL progress bar requires Jupyter Notebook >= \" +\n",
       "                            \"5.0 or Jupyter Lab. Alternatively, you can use \" +\n",
       "                            \"TerminalProgressBar().\");\n",
       "                        pb.parentNode.insertBefore(text, pb);\n",
       "                    }\n",
       "                </script>\n",
       "                <div id=\"0cd42eba-5dfd-446c-8486-fb4bf7c1d3e5\" 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('0cd42eba-5dfd-446c-8486-fb4bf7c1d3e5');\n",
       "                  var text = root.getElementsByClassName('pb-text')[0];\n",
       "                  var fill = root.getElementsByClassName('pb-fill')[0];\n",
       "\n",
       "                  text.innerHTML = 'Build finished in 0:00:06.';\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",
       "        "
      ],
      "application/vdom.v1+json": {
       "attributes": {
        "id": "bd9c59be-536e-4d48-8932-b11fc2f0ed7c",
        "style": {
         "border": "1px solid #cfcfcf",
         "borderRadius": "4px",
         "boxSizing": "border-box",
         "position": "relative",
         "textAlign": "center",
         "width": "100%"
        }
       },
       "children": [
        {
         "attributes": {
          "class": "pb-text",
          "style": {
           "position": "absolute",
           "width": "100%"
          }
         },
         "children": [
          "Build finished in 0:00:06."
         ],
         "tagName": "div"
        },
        {
         "attributes": {
          "class": "pb-fill",
          "style": {
           "animation": "none",
           "backgroundColor": "#bdd2e6",
           "backgroundImage": "none",
           "backgroundSize": "100px 100%",
           "width": "100%"
          }
         },
         "children": [
          {
           "attributes": {
            "scoped": "scoped",
            "type": "text/css"
           },
           "children": [
            "\n                        @keyframes pb-fill-anim {\n                            0% { background-position: 0 0; }\n                            100% { background-position: 100px 0; }\n                        }}"
           ],
           "tagName": "style"
          },
          " "
         ],
         "tagName": "div"
        }
       ],
       "tagName": "div"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vdom.v1+json": {
       "attributes": {},
       "tagName": "div"
      },
      "text/html": [
       "\n",
       "                <script>\n",
       "                    if (Jupyter.version.split(\".\")[0] < 5) {\n",
       "                        var pb = document.getElementById(\"d4663163-e22e-4e1f-92fd-9dfc2ec4bca0\");\n",
       "                        var text = document.createTextNode(\n",
       "                            \"HMTL progress bar requires Jupyter Notebook >= \" +\n",
       "                            \"5.0 or Jupyter Lab. Alternatively, you can use \" +\n",
       "                            \"TerminalProgressBar().\");\n",
       "                        pb.parentNode.insertBefore(text, pb);\n",
       "                    }\n",
       "                </script>\n",
       "                <div id=\"d4663163-e22e-4e1f-92fd-9dfc2ec4bca0\" 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('d4663163-e22e-4e1f-92fd-9dfc2ec4bca0');\n",
       "                  var text = root.getElementsByClassName('pb-text')[0];\n",
       "                  var fill = root.getElementsByClassName('pb-fill')[0];\n",
       "\n",
       "                  text.innerHTML = 'Simulation finished in 0:00:03.';\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",
       "        "
      ],
      "application/vdom.v1+json": {
       "attributes": {
        "id": "a443bb9f-6b90-4f8c-8b65-ed67a57dc9a2",
        "style": {
         "border": "1px solid #cfcfcf",
         "borderRadius": "4px",
         "boxSizing": "border-box",
         "position": "relative",
         "textAlign": "center",
         "width": "100%"
        }
       },
       "children": [
        {
         "attributes": {
          "class": "pb-text",
          "style": {
           "position": "absolute",
           "width": "100%"
          }
         },
         "children": [
          "Simulation finished in 0:00:03."
         ],
         "tagName": "div"
        },
        {
         "attributes": {
          "class": "pb-fill",
          "style": {
           "animation": "none",
           "backgroundColor": "#bdd2e6",
           "backgroundImage": "none",
           "transition": "width 0.1s linear",
           "width": "100%"
          }
         },
         "children": [
          {
           "attributes": {
            "scoped": "scoped",
            "type": "text/css"
           },
           "children": [
            "\n                        @keyframes pb-fill-anim {\n                            0% { background-position: 0 0; }\n                            100% { background-position: 100px 0; }\n                        }}"
           ],
           "tagName": "style"
          },
          " "
         ],
         "tagName": "div"
        }
       ],
       "tagName": "div"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "runtime = 7.443 s\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "start = time.time()\n",
    "t1, a1, s1, v1 = go(3, t=0.5, dt=0.00005, sample_every=0.00005, neuron_type=WilsonEuler())\n",
    "end = time.time()\n",
    "print('runtime = %.3f s' %(end-start))\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
    "ax.plot(t1, v1)\n",
    "ax.set(xlabel='time (s)', ylabel='Voltage (mV)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vdom.v1+json": {
       "attributes": {},
       "tagName": "div"
      },
      "text/html": [
       "\n",
       "                <script>\n",
       "                    if (Jupyter.version.split(\".\")[0] < 5) {\n",
       "                        var pb = document.getElementById(\"626a9cd9-75ae-4cdd-b7f9-e7f02e75cc67\");\n",
       "                        var text = document.createTextNode(\n",
       "                            \"HMTL progress bar requires Jupyter Notebook >= \" +\n",
       "                            \"5.0 or Jupyter Lab. Alternatively, you can use \" +\n",
       "                            \"TerminalProgressBar().\");\n",
       "                        pb.parentNode.insertBefore(text, pb);\n",
       "                    }\n",
       "                </script>\n",
       "                <div id=\"626a9cd9-75ae-4cdd-b7f9-e7f02e75cc67\" 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('626a9cd9-75ae-4cdd-b7f9-e7f02e75cc67');\n",
       "                  var text = root.getElementsByClassName('pb-text')[0];\n",
       "                  var fill = root.getElementsByClassName('pb-fill')[0];\n",
       "\n",
       "                  text.innerHTML = 'Build finished in 0:00:06.';\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",
       "        "
      ],
      "application/vdom.v1+json": {
       "attributes": {
        "id": "c60c4fe2-b60b-444c-98fe-7d5cb3638ed6",
        "style": {
         "border": "1px solid #cfcfcf",
         "borderRadius": "4px",
         "boxSizing": "border-box",
         "position": "relative",
         "textAlign": "center",
         "width": "100%"
        }
       },
       "children": [
        {
         "attributes": {
          "class": "pb-text",
          "style": {
           "position": "absolute",
           "width": "100%"
          }
         },
         "children": [
          "Build finished in 0:00:06."
         ],
         "tagName": "div"
        },
        {
         "attributes": {
          "class": "pb-fill",
          "style": {
           "animation": "none",
           "backgroundColor": "#bdd2e6",
           "backgroundImage": "none",
           "backgroundSize": "100px 100%",
           "width": "100%"
          }
         },
         "children": [
          {
           "attributes": {
            "scoped": "scoped",
            "type": "text/css"
           },
           "children": [
            "\n                        @keyframes pb-fill-anim {\n                            0% { background-position: 0 0; }\n                            100% { background-position: 100px 0; }\n                        }}"
           ],
           "tagName": "style"
          },
          " "
         ],
         "tagName": "div"
        }
       ],
       "tagName": "div"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vdom.v1+json": {
       "attributes": {},
       "tagName": "div"
      },
      "text/html": [
       "\n",
       "                <script>\n",
       "                    if (Jupyter.version.split(\".\")[0] < 5) {\n",
       "                        var pb = document.getElementById(\"8be0b98e-5f20-495b-b886-a5ce869a4fa1\");\n",
       "                        var text = document.createTextNode(\n",
       "                            \"HMTL progress bar requires Jupyter Notebook >= \" +\n",
       "                            \"5.0 or Jupyter Lab. Alternatively, you can use \" +\n",
       "                            \"TerminalProgressBar().\");\n",
       "                        pb.parentNode.insertBefore(text, pb);\n",
       "                    }\n",
       "                </script>\n",
       "                <div id=\"8be0b98e-5f20-495b-b886-a5ce869a4fa1\" 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('8be0b98e-5f20-495b-b886-a5ce869a4fa1');\n",
       "                  var text = root.getElementsByClassName('pb-text')[0];\n",
       "                  var fill = root.getElementsByClassName('pb-fill')[0];\n",
       "\n",
       "                  text.innerHTML = 'Simulation finished in 0:00:01.';\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",
       "        "
      ],
      "application/vdom.v1+json": {
       "attributes": {
        "id": "7a134a93-c30d-4d29-a016-32226abd0ca9",
        "style": {
         "border": "1px solid #cfcfcf",
         "borderRadius": "4px",
         "boxSizing": "border-box",
         "position": "relative",
         "textAlign": "center",
         "width": "100%"
        }
       },
       "children": [
        {
         "attributes": {
          "class": "pb-text",
          "style": {
           "position": "absolute",
           "width": "100%"
          }
         },
         "children": [
          "Simulation finished in 0:00:01."
         ],
         "tagName": "div"
        },
        {
         "attributes": {
          "class": "pb-fill",
          "style": {
           "animation": "none",
           "backgroundColor": "#bdd2e6",
           "backgroundImage": "none",
           "transition": "width 0.1s linear",
           "width": "100%"
          }
         },
         "children": [
          {
           "attributes": {
            "scoped": "scoped",
            "type": "text/css"
           },
           "children": [
            "\n                        @keyframes pb-fill-anim {\n                            0% { background-position: 0 0; }\n                            100% { background-position: 100px 0; }\n                        }}"
           ],
           "tagName": "style"
          },
          " "
         ],
         "tagName": "div"
        }
       ],
       "tagName": "div"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "runtime = 5.864 s\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "start = time.time()\n",
    "t1, a1, s1, v1 = go(3, t=0.5, dt=0.0002, sample_every=0.0002, neuron_type=WilsonEuler())\n",
    "end = time.time()\n",
    "print('runtime = %.3f s' %(end-start))\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
    "ax.plot(t1, v1)\n",
    "ax.set(xlabel='time (s)', ylabel='Voltage (mV)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RK4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vdom.v1+json": {
       "attributes": {},
       "tagName": "div"
      },
      "text/html": [
       "\n",
       "                <script>\n",
       "                    if (Jupyter.version.split(\".\")[0] < 5) {\n",
       "                        var pb = document.getElementById(\"2001c054-2e5a-4399-971b-aa3b5677e350\");\n",
       "                        var text = document.createTextNode(\n",
       "                            \"HMTL progress bar requires Jupyter Notebook >= \" +\n",
       "                            \"5.0 or Jupyter Lab. Alternatively, you can use \" +\n",
       "                            \"TerminalProgressBar().\");\n",
       "                        pb.parentNode.insertBefore(text, pb);\n",
       "                    }\n",
       "                </script>\n",
       "                <div id=\"2001c054-2e5a-4399-971b-aa3b5677e350\" 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('2001c054-2e5a-4399-971b-aa3b5677e350');\n",
       "                  var text = root.getElementsByClassName('pb-text')[0];\n",
       "                  var fill = root.getElementsByClassName('pb-fill')[0];\n",
       "\n",
       "                  text.innerHTML = 'Build finished in 0:00:22.';\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",
       "        "
      ],
      "application/vdom.v1+json": {
       "attributes": {
        "id": "a3697fda-6bd7-4f1d-9853-780fe061b72c",
        "style": {
         "border": "1px solid #cfcfcf",
         "borderRadius": "4px",
         "boxSizing": "border-box",
         "position": "relative",
         "textAlign": "center",
         "width": "100%"
        }
       },
       "children": [
        {
         "attributes": {
          "class": "pb-text",
          "style": {
           "position": "absolute",
           "width": "100%"
          }
         },
         "children": [
          "Build finished in 0:00:22."
         ],
         "tagName": "div"
        },
        {
         "attributes": {
          "class": "pb-fill",
          "style": {
           "animation": "none",
           "backgroundColor": "#bdd2e6",
           "backgroundImage": "none",
           "backgroundSize": "100px 100%",
           "width": "100%"
          }
         },
         "children": [
          {
           "attributes": {
            "scoped": "scoped",
            "type": "text/css"
           },
           "children": [
            "\n                        @keyframes pb-fill-anim {\n                            0% { background-position: 0 0; }\n                            100% { background-position: 100px 0; }\n                        }}"
           ],
           "tagName": "style"
          },
          " "
         ],
         "tagName": "div"
        }
       ],
       "tagName": "div"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vdom.v1+json": {
       "attributes": {},
       "tagName": "div"
      },
      "text/html": [
       "\n",
       "                <script>\n",
       "                    if (Jupyter.version.split(\".\")[0] < 5) {\n",
       "                        var pb = document.getElementById(\"f89fc761-af8b-403d-9fe9-d0d6aedee8b3\");\n",
       "                        var text = document.createTextNode(\n",
       "                            \"HMTL progress bar requires Jupyter Notebook >= \" +\n",
       "                            \"5.0 or Jupyter Lab. Alternatively, you can use \" +\n",
       "                            \"TerminalProgressBar().\");\n",
       "                        pb.parentNode.insertBefore(text, pb);\n",
       "                    }\n",
       "                </script>\n",
       "                <div id=\"f89fc761-af8b-403d-9fe9-d0d6aedee8b3\" 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('f89fc761-af8b-403d-9fe9-d0d6aedee8b3');\n",
       "                  var text = root.getElementsByClassName('pb-text')[0];\n",
       "                  var fill = root.getElementsByClassName('pb-fill')[0];\n",
       "\n",
       "                  text.innerHTML = 'Simulation finished in 0:00:07.';\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",
       "        "
      ],
      "application/vdom.v1+json": {
       "attributes": {
        "id": "da9175fc-c49e-488e-8a69-3f932ef5ca35",
        "style": {
         "border": "1px solid #cfcfcf",
         "borderRadius": "4px",
         "boxSizing": "border-box",
         "position": "relative",
         "textAlign": "center",
         "width": "100%"
        }
       },
       "children": [
        {
         "attributes": {
          "class": "pb-text",
          "style": {
           "position": "absolute",
           "width": "100%"
          }
         },
         "children": [
          "Simulation finished in 0:00:07."
         ],
         "tagName": "div"
        },
        {
         "attributes": {
          "class": "pb-fill",
          "style": {
           "animation": "none",
           "backgroundColor": "#bdd2e6",
           "backgroundImage": "none",
           "transition": "width 0.1s linear",
           "width": "100%"
          }
         },
         "children": [
          {
           "attributes": {
            "scoped": "scoped",
            "type": "text/css"
           },
           "children": [
            "\n                        @keyframes pb-fill-anim {\n                            0% { background-position: 0 0; }\n                            100% { background-position: 100px 0; }\n                        }}"
           ],
           "tagName": "style"
          },
          " "
         ],
         "tagName": "div"
        }
       ],
       "tagName": "div"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "runtime = 28.769 s\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "start = time.time()\n",
    "t1, a1, s1, v1 = go(3, t=0.5, dt=0.00005, sample_every=0.00005, neuron_type=WilsonRK4())\n",
    "end = time.time()\n",
    "print('runtime = %.3f s' %(end-start))\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
    "ax.plot(t1, v1)\n",
    "ax.set(xlabel='time (s)', ylabel='Voltage (mV)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vdom.v1+json": {
       "attributes": {},
       "tagName": "div"
      },
      "text/html": [
       "\n",
       "                <script>\n",
       "                    if (Jupyter.version.split(\".\")[0] < 5) {\n",
       "                        var pb = document.getElementById(\"af5f490c-2574-494a-9b8e-d643e0e0fe67\");\n",
       "                        var text = document.createTextNode(\n",
       "                            \"HMTL progress bar requires Jupyter Notebook >= \" +\n",
       "                            \"5.0 or Jupyter Lab. Alternatively, you can use \" +\n",
       "                            \"TerminalProgressBar().\");\n",
       "                        pb.parentNode.insertBefore(text, pb);\n",
       "                    }\n",
       "                </script>\n",
       "                <div id=\"af5f490c-2574-494a-9b8e-d643e0e0fe67\" 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('af5f490c-2574-494a-9b8e-d643e0e0fe67');\n",
       "                  var text = root.getElementsByClassName('pb-text')[0];\n",
       "                  var fill = root.getElementsByClassName('pb-fill')[0];\n",
       "\n",
       "                  text.innerHTML = 'Build finished in 0:00:22.';\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",
       "        "
      ],
      "application/vdom.v1+json": {
       "attributes": {
        "id": "c2aace50-90ff-4d2a-a2fe-b228177766c8",
        "style": {
         "border": "1px solid #cfcfcf",
         "borderRadius": "4px",
         "boxSizing": "border-box",
         "position": "relative",
         "textAlign": "center",
         "width": "100%"
        }
       },
       "children": [
        {
         "attributes": {
          "class": "pb-text",
          "style": {
           "position": "absolute",
           "width": "100%"
          }
         },
         "children": [
          "Build finished in 0:00:22."
         ],
         "tagName": "div"
        },
        {
         "attributes": {
          "class": "pb-fill",
          "style": {
           "animation": "none",
           "backgroundColor": "#bdd2e6",
           "backgroundImage": "none",
           "backgroundSize": "100px 100%",
           "width": "100%"
          }
         },
         "children": [
          {
           "attributes": {
            "scoped": "scoped",
            "type": "text/css"
           },
           "children": [
            "\n                        @keyframes pb-fill-anim {\n                            0% { background-position: 0 0; }\n                            100% { background-position: 100px 0; }\n                        }}"
           ],
           "tagName": "style"
          },
          " "
         ],
         "tagName": "div"
        }
       ],
       "tagName": "div"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vdom.v1+json": {
       "attributes": {},
       "tagName": "div"
      },
      "text/html": [
       "\n",
       "                <script>\n",
       "                    if (Jupyter.version.split(\".\")[0] < 5) {\n",
       "                        var pb = document.getElementById(\"0535ada1-6bd1-4445-98e9-41c6ced5d892\");\n",
       "                        var text = document.createTextNode(\n",
       "                            \"HMTL progress bar requires Jupyter Notebook >= \" +\n",
       "                            \"5.0 or Jupyter Lab. Alternatively, you can use \" +\n",
       "                            \"TerminalProgressBar().\");\n",
       "                        pb.parentNode.insertBefore(text, pb);\n",
       "                    }\n",
       "                </script>\n",
       "                <div id=\"0535ada1-6bd1-4445-98e9-41c6ced5d892\" 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('0535ada1-6bd1-4445-98e9-41c6ced5d892');\n",
       "                  var text = root.getElementsByClassName('pb-text')[0];\n",
       "                  var fill = root.getElementsByClassName('pb-fill')[0];\n",
       "\n",
       "                  text.innerHTML = 'Simulation finished in 0:00:03.';\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",
       "        "
      ],
      "application/vdom.v1+json": {
       "attributes": {
        "id": "6b1cb620-7b65-4991-8eb6-fdc7d8ef06c7",
        "style": {
         "border": "1px solid #cfcfcf",
         "borderRadius": "4px",
         "boxSizing": "border-box",
         "position": "relative",
         "textAlign": "center",
         "width": "100%"
        }
       },
       "children": [
        {
         "attributes": {
          "class": "pb-text",
          "style": {
           "position": "absolute",
           "width": "100%"
          }
         },
         "children": [
          "Simulation finished in 0:00:03."
         ],
         "tagName": "div"
        },
        {
         "attributes": {
          "class": "pb-fill",
          "style": {
           "animation": "none",
           "backgroundColor": "#bdd2e6",
           "backgroundImage": "none",
           "transition": "width 0.1s linear",
           "width": "100%"
          }
         },
         "children": [
          {
           "attributes": {
            "scoped": "scoped",
            "type": "text/css"
           },
           "children": [
            "\n                        @keyframes pb-fill-anim {\n                            0% { background-position: 0 0; }\n                            100% { background-position: 100px 0; }\n                        }}"
           ],
           "tagName": "style"
          },
          " "
         ],
         "tagName": "div"
        }
       ],
       "tagName": "div"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "runtime = 24.148 s\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "start = time.time()\n",
    "t1, a1, s1, v1 = go(3, t=0.5, dt=0.001, sample_every=0.001, neuron_type=WilsonRK4())\n",
    "end = time.time()\n",
    "print('runtime = %.3f s' %(end-start))\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
    "ax.plot(t1, v1)\n",
    "ax.set(xlabel='time (s)', ylabel='Voltage (mV)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Brian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running transients...\n",
      " 74.00% done..."
     ]
    },
    {
     "data": {
      "application/vdom.v1+json": {
       "attributes": {},
       "tagName": "div"
      },
      "text/html": [
       "\n",
       "                <script>\n",
       "                    if (Jupyter.version.split(\".\")[0] < 5) {\n",
       "                        var pb = document.getElementById(\"36736c23-08e3-41d8-9073-d38a107d3bdb\");\n",
       "                        var text = document.createTextNode(\n",
       "                            \"HMTL progress bar requires Jupyter Notebook >= \" +\n",
       "                            \"5.0 or Jupyter Lab. Alternatively, you can use \" +\n",
       "                            \"TerminalProgressBar().\");\n",
       "                        pb.parentNode.insertBefore(text, pb);\n",
       "                    }\n",
       "                </script>\n",
       "                <div id=\"36736c23-08e3-41d8-9073-d38a107d3bdb\" 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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 75.00% done..."
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "              (function () {\n",
       "                  var root = document.getElementById('36736c23-08e3-41d8-9073-d38a107d3bdb');\n",
       "                  var text = root.getElementsByClassName('pb-text')[0];\n",
       "                  var fill = root.getElementsByClassName('pb-fill')[0];\n",
       "\n",
       "                  text.innerHTML = 'Build finished in 0:01:32.';\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",
       "        "
      ],
      "application/vdom.v1+json": {
       "attributes": {
        "id": "ac5698c3-5a39-493d-81cb-ebab18e693f9",
        "style": {
         "border": "1px solid #cfcfcf",
         "borderRadius": "4px",
         "boxSizing": "border-box",
         "position": "relative",
         "textAlign": "center",
         "width": "100%"
        }
       },
       "children": [
        {
         "attributes": {
          "class": "pb-text",
          "style": {
           "position": "absolute",
           "width": "100%"
          }
         },
         "children": [
          "Build finished in 0:01:32."
         ],
         "tagName": "div"
        },
        {
         "attributes": {
          "class": "pb-fill",
          "style": {
           "animation": "none",
           "backgroundColor": "#bdd2e6",
           "backgroundImage": "none",
           "backgroundSize": "100px 100%",
           "width": "100%"
          }
         },
         "children": [
          {
           "attributes": {
            "scoped": "scoped",
            "type": "text/css"
           },
           "children": [
            "\n                        @keyframes pb-fill-anim {\n                            0% { background-position: 0 0; }\n                            100% { background-position: 100px 0; }\n                        }}"
           ],
           "tagName": "style"
          },
          " "
         ],
         "tagName": "div"
        }
       ],
       "tagName": "div"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100.00% done...\n",
      "calculating rate...\n",
      "100.00% done...\n",
      "running transients...\n",
      "100.00% done...\n",
      "calculating rate...\n",
      "100.00% done...\n",
      "running transients...\n",
      "100.00% done...\n",
      "calculating rate...\n",
      "100.00% done...\n"
     ]
    },
    {
     "data": {
      "application/vdom.v1+json": {
       "attributes": {},
       "tagName": "div"
      },
      "text/html": [
       "\n",
       "                <script>\n",
       "                    if (Jupyter.version.split(\".\")[0] < 5) {\n",
       "                        var pb = document.getElementById(\"5ebf55ac-697a-46ed-8061-8890efe4b14d\");\n",
       "                        var text = document.createTextNode(\n",
       "                            \"HMTL progress bar requires Jupyter Notebook >= \" +\n",
       "                            \"5.0 or Jupyter Lab. Alternatively, you can use \" +\n",
       "                            \"TerminalProgressBar().\");\n",
       "                        pb.parentNode.insertBefore(text, pb);\n",
       "                    }\n",
       "                </script>\n",
       "                <div id=\"5ebf55ac-697a-46ed-8061-8890efe4b14d\" 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('5ebf55ac-697a-46ed-8061-8890efe4b14d');\n",
       "                  var text = root.getElementsByClassName('pb-text')[0];\n",
       "                  var fill = root.getElementsByClassName('pb-fill')[0];\n",
       "\n",
       "                  text.innerHTML = 'Simulation finished in 0:00:14.';\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",
       "        "
      ],
      "application/vdom.v1+json": {
       "attributes": {
        "id": "6345a9e7-320c-48eb-a663-e8cb754c18a8",
        "style": {
         "border": "1px solid #cfcfcf",
         "borderRadius": "4px",
         "boxSizing": "border-box",
         "position": "relative",
         "textAlign": "center",
         "width": "100%"
        }
       },
       "children": [
        {
         "attributes": {
          "class": "pb-text",
          "style": {
           "position": "absolute",
           "width": "100%"
          }
         },
         "children": [
          "Simulation finished in 0:00:14."
         ],
         "tagName": "div"
        },
        {
         "attributes": {
          "class": "pb-fill",
          "style": {
           "animation": "none",
           "backgroundColor": "#bdd2e6",
           "backgroundImage": "none",
           "transition": "width 0.1s linear",
           "width": "100%"
          }
         },
         "children": [
          {
           "attributes": {
            "scoped": "scoped",
            "type": "text/css"
           },
           "children": [
            "\n                        @keyframes pb-fill-anim {\n                            0% { background-position: 0 0; }\n                            100% { background-position: 100px 0; }\n                        }}"
           ],
           "tagName": "style"
          },
          " "
         ],
         "tagName": "div"
        }
       ],
       "tagName": "div"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "runtime = 105.299 s\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "start = time.time()\n",
    "t1, a1, s1, v1 = go(3, t=0.5, dt=0.001, sample_every=0.001, neuron_type=WilsonBrian(dt_brian=0.000025, ode='euler'))\n",
    "end = time.time()\n",
    "print('runtime = %.3f s' %(end-start))\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
    "ax.plot(t1, v1)\n",
    "ax.set(xlabel='time (s)', ylabel='Voltage (mV)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}