{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 539,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "# %load imports.py\n",
    "%load_ext autoreload\n",
    "%autoreload\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 540,
   "metadata": {},
   "outputs": [],
   "source": [
    "import brian2 as br\n",
    "from brian2.units import *\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings \n",
    "def set_parameters_from_dict(neurongroup, dictionary_of_parameters):\n",
    "    for param_key, param_value in dictionary_of_parameters.items():\n",
    "        try: \n",
    "            neurongroup.__setattr__(param_key, param_value)\n",
    "        except AttributeError as err:\n",
    "            warnings.warn(\"{:s} has no paramater {:s}\".format(neurongroup.name, param_key))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Definition of the Neuron and Synapse Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 542,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING    /home/drangmeister/miniconda3/envs/micro_circuit_notebooks/lib/python3.7/site-packages/ipykernel_launcher.py:7: UserWarning: single_neuron has no paramater tau\n",
      "  import sys\n",
      " [py.warnings]\n",
      "WARNING    /home/drangmeister/miniconda3/envs/micro_circuit_notebooks/lib/python3.7/site-packages/ipykernel_launcher.py:7: UserWarning: single_neuron has no paramater v_threshold\n",
      "  import sys\n",
      " [py.warnings]\n",
      "WARNING    /home/drangmeister/miniconda3/envs/micro_circuit_notebooks/lib/python3.7/site-packages/ipykernel_launcher.py:7: UserWarning: single_neuron has no paramater v_reset\n",
      "  import sys\n",
      " [py.warnings]\n",
      "WARNING    /home/drangmeister/miniconda3/envs/micro_circuit_notebooks/lib/python3.7/site-packages/ipykernel_launcher.py:7: UserWarning: single_neuron has no paramater v_refractory\n",
      "  import sys\n",
      " [py.warnings]\n",
      "WARNING    /home/drangmeister/miniconda3/envs/micro_circuit_notebooks/lib/python3.7/site-packages/ipykernel_launcher.py:7: UserWarning: single_neuron has no paramater u_ext\n",
      "  import sys\n",
      " [py.warnings]\n"
     ]
    }
   ],
   "source": [
    "# Hodkin Huxley model from Brian2 documentation\n",
    "area = 20000*umetre**2\n",
    "Cm = 1*ufarad*cm**-2 * area\n",
    "gl = 5e-5*siemens*cm**-2 * area\n",
    "El = -65*mV\n",
    "EK = -90*mV\n",
    "ENa = 50*mV\n",
    "g_na = 100*msiemens*cm**-2 * area\n",
    "g_kd = 30*msiemens*cm**-2 * area\n",
    "VT = -63*mV\n",
    "# The model\n",
    "eqs = br.Equations('''\n",
    "dv/dt = (gl*(El-v) - g_na*(m*m*m)*h*(v-ENa) - g_kd*(n*n*n*n)*(v-EK) + I + ih)/Cm : volt\n",
    "dm/dt = 0.32*(mV**-1)*(13.*mV-v+VT)/\n",
    "    (exp((13.*mV-v+VT)/(4.*mV))-1.)/ms*(1-m)-0.28*(mV**-1)*(v-VT-40.*mV)/\n",
    "    (exp((v-VT-40.*mV)/(5.*mV))-1.)/ms*m : 1\n",
    "dn/dt = 0.032*(mV**-1)*(15.*mV-v+VT)/\n",
    "    (exp((15.*mV-v+VT)/(5.*mV))-1.)/ms*(1.-n)-.5*exp((10.*mV-v+VT)/(40.*mV))/ms*n : 1\n",
    "dh/dt = 0.128*exp((17.*mV-v+VT)/(18.*mV))/ms*(1.-h)-4./(1+exp((40.*mV-v+VT)/(5.*mV)))/ms*h : 1\n",
    "I : amp\n",
    "''')\n",
    "\n",
    "# # First H-current from Izhikevich p. 48 \n",
    "# ghbar = 40. * nS #Other values would be 0.5, 2, 3.5, 20 depending on neuron type (Rothman, Manis)\n",
    "# Eh = -43*mV\n",
    "# V_half_h = -75.0\n",
    "# k_h = -5.5\n",
    "# V_max_h = -75.\n",
    "# sigma_h = 15.\n",
    "# C_amp_h = 1000.\n",
    "# C_base_h = 100.\n",
    "# eqs_ih = \"\"\"\n",
    "# ih = ghbar*r*(Eh-v) : amp\n",
    "# dr/dt= (rinf-r)/rtau : 1\n",
    "# rinf = 1. / (1+exp((V_half_h - v/mV) / k_h)) : 1\n",
    "# rtau = (C_base_h + C_amp_h * exp(-(V_max_h-v/mV)**2./sigma_h**2)) * ms : second\n",
    "# \"\"\"\n",
    "# Second H-current from Izhikevich p. 48 \n",
    "ghbar = 40. * nS #Other values would be 0.5, 2, 3.5, 20 depending on neuron type (Rothman, Manis)\n",
    "Eh = -1.*mV\n",
    "V_half_h = -90.0\n",
    "k_h = -9.\n",
    "V_max_h = -75.\n",
    "sigma_h = 20.\n",
    "C_amp_h = 40.\n",
    "C_base_h = 10.\n",
    "eqs_ih = \"\"\"\n",
    "ih = ghbar*r*(Eh-v) : amp\n",
    "dr/dt= (rinf-r)/rtau : 1\n",
    "rinf = 1. / (1+exp((V_half_h - v/mV) / k_h)) : 1\n",
    "rtau = (C_base_h + C_amp_h * exp(-(V_max_h-v/mV)**2./sigma_h**2)) * ms : second\n",
    "\"\"\"\n",
    "\n",
    "# eqs_ih = \"\"\"\n",
    "# ih = ghbar*r*(Eh-v) : amp\n",
    "# dr/dt=(rinf-r)/rtau : 1\n",
    "# rinf = 1. / (1+exp((v/mV + 90.) / 7.)) : 1\n",
    "# # rtau = ((100000. / (237.*exp((v/mV+60.) / 12.) + 17.*exp(-(v/mV+60.) / 14.))) + 25.)*ms : second\n",
    "# rtau = 0.01*(100. + 1000. * exp(-(-76.-v/mV)**2./15.**2)) * ms : second #From Izhikevich\n",
    "# \"\"\"\n",
    "\n",
    "eqs += eqs_ih\n",
    "\n",
    "delta_synapse_model = 'perturbation: volt'\n",
    "delta_synapse = 'v+=perturbation'\n",
    "\n",
    "threshold = \"v>v_threshold\"\n",
    "\n",
    "neuron_properties = {\n",
    "    \"tau\": 10*ms,\n",
    "    \"v_threshold\": -40*mV,\n",
    "    \"v_reset\": -75*mV,\n",
    "    \"v_refractory\": 'v > -40*mV',\n",
    "    \"u_ext\": - 39 * mV\n",
    "}\n",
    "\n",
    "# pulse_strength = -10. * mV\n",
    "\n",
    "neuron = br.NeuronGroup(N=1, \\\n",
    "                        name='single_neuron',\\\n",
    "                        model=eqs, \\\n",
    "                        threshold='v > -40*mV', \\\n",
    "                        refractory = 'v > -40*mV',\\\n",
    "                        method='exponential_euler')\n",
    "\n",
    "set_parameters_from_dict(neuron, neuron_properties)\n",
    "neuron.v = El\n",
    "neuron.I = 0.7*nA\n",
    "\n",
    "record_variables = ['v','ih']\n",
    "spike_recorder = br.SpikeMonitor(source=neuron)\n",
    "state_recorder = br.StateMonitor(neuron, record_variables, record=True)\n",
    "\n",
    "auto_synapse = br.Synapses(source=neuron, target=neuron, model=delta_synapse_model, on_pre = delta_synapse, delay = 0.0 * ms)\n",
    "auto_synapse.connect()\n",
    "\n",
    "\n",
    "net = br.Network(neuron)\n",
    "net.add(auto_synapse)\n",
    "net.add(spike_recorder)\n",
    "net.add(state_recorder)\n",
    "net.store()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis Functions for the Spike Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 543,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_sim(delay=0.0, pulse_strength=0.0, record_states=False, duration=50 * ms):\n",
    "    net.restore()\n",
    "    state_recorder.record = record_states\n",
    "    auto_synapse.perturbation = pulse_strength\n",
    "    auto_synapse.delay = delay\n",
    "    \n",
    "    net.run(duration=duration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 544,
   "metadata": {
    "collapsed": false,
    "outputHidden": false,
    "inputHidden": false
   },
   "outputs": [],
   "source": [
    "def get_mean_period(spike_train):\n",
    "    return (np.max(spike_train) - np.min(spike_train)) / (spike_train.shape[0] - 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 545,
   "metadata": {
    "collapsed": false,
    "outputHidden": false,
    "inputHidden": false
   },
   "outputs": [],
   "source": [
    "def get_mean_response(spike_trains_dict,t_isi):\n",
    "    mean_response_dict = {}\n",
    "#     t_isi = get_mean_period(spike_trains_dict[0.0 * ms])\n",
    "    for delay, spike_train in spike_trains_dict.items():\n",
    "        mean_response_dict[delay] = get_mean_period(spike_train) - t_isi\n",
    "    return mean_response_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 546,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 547,
   "metadata": {
    "collapsed": false,
    "outputHidden": false,
    "inputHidden": false
   },
   "outputs": [],
   "source": [
    "def plot_spiking(spike_trains_dict):\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    for key, times in spike_trains_dict.items():\n",
    "        ax.plot(times/ms, key*np.ones(times.shape), 'b.')\n",
    "#     ax.plot(spike_train[0]/ms, np.ones(spike_train[0].shape), 'b|')\n",
    "\n",
    "    ax.grid(axis='x')\n",
    "#     ax.set_ylim(-0.1, 1.1)\n",
    "    ax.set_xlabel(\"Time(ms)\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 548,
   "metadata": {
    "collapsed": false,
    "outputHidden": false,
    "inputHidden": false
   },
   "outputs": [],
   "source": [
    "def plot_mean_period(mean_period_dict):\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    ax.plot(list(mean_period_dict.keys()), list(mean_period_dict.values()), 'b')\n",
    "\n",
    "    ax.grid(axis='x')\n",
    "    ax.set_xlabel(\"Delay (ms)\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 549,
   "metadata": {
    "collapsed": false,
    "outputHidden": false,
    "inputHidden": false
   },
   "outputs": [],
   "source": [
    "def plot_mean_response(mean_response_dict):\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    ax.plot(np.array(list(mean_response_dict.keys())), 1e3*np.array(list(mean_response_dict.values())), 'b')\n",
    "    ax.plot(list(mean_response_dict.keys()), list(mean_response_dict.keys()), 'k--')\n",
    "\n",
    "    ax.grid(axis='x')\n",
    "    ax.set_xlabel(\"t_inh (ms)\")\n",
    "    ax.set_ylabel(\"exc_spike_delay (ms)\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 550,
   "metadata": {
    "collapsed": false,
    "outputHidden": false,
    "inputHidden": false
   },
   "outputs": [],
   "source": [
    "def plot_delay_minus_response(mean_response_dict):\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    print(np.array(list(mean_response_dict.keys())))\n",
    "    print(np.array(list(mean_response_dict.values())))\n",
    "    print(np.array(list(mean_response_dict.keys()))-np.array(list(mean_response_dict.values())))\n",
    "    ax.plot(list(mean_response_dict.keys()), np.array(list(mean_response_dict.keys()))-1e3*np.array(list(mean_response_dict.values())), 'b')\n",
    "\n",
    "    ax.grid(axis='x')\n",
    "    ax.set_xlabel(\"Delay (ms)\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 551,
   "metadata": {
    "collapsed": false,
    "outputHidden": false,
    "inputHidden": false
   },
   "outputs": [],
   "source": [
    "def plot_phase_response_curve(mean_response_dict, t_isi):\n",
    "    \n",
    "    phi_inh = np.array(list(mean_response_dict.keys())) * ms / t_isi\n",
    "    delta_phi = -np.array(list(mean_response_dict.values())) * second / t_isi\n",
    "    \n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    ax.plot(phi_inh, delta_phi, 'b')\n",
    "    ax.plot(phi_inh, -phi_inh, 'k--')\n",
    "\n",
    "    ax.grid(axis='x')\n",
    "    ax.set_xlabel(\"phase of inhibition (ms)\")\n",
    "    ax.set_ylabel(\"phase shift (ms)\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Execution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 552,
   "metadata": {
    "collapsed": false,
    "outputHidden": false,
    "inputHidden": false
   },
   "outputs": [],
   "source": [
    "run_sim(record_states=True, pulse_strength=0.0*mV, duration=200 * ms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 553,
   "metadata": {
    "collapsed": false,
    "outputHidden": false,
    "inputHidden": false
   },
   "outputs": [],
   "source": [
    "t_state = state_recorder.t\n",
    "v_state = state_recorder[1].v\n",
    "ih_state = state_recorder[1].ih\n",
    "spike_train = spike_recorder.spike_trains()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 554,
   "metadata": {
    "collapsed": false,
    "outputHidden": false,
    "inputHidden": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.31904762 ms\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "ax1.plot(t_state/ms,v_state/ms,'C2')\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(t_state/ms,ih_state/ms,'C1')\n",
    "\n",
    "t_isi = get_mean_period(spike_train)\n",
    "print(t_isi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 555,
   "metadata": {
    "collapsed": false,
    "outputHidden": false,
    "inputHidden": false
   },
   "outputs": [],
   "source": [
    "n_simulations = 40\n",
    "pulse_strength = -20 * mV\n",
    "\n",
    "delay_list = np.linspace(0.1 * ms,t_isi - 0.1*ms,n_simulations)\n",
    "spike_trains_dict = {}\n",
    "\n",
    "for delay in delay_list:\n",
    "    run_sim(delay, pulse_strength, record_states=False, duration=100 * ms)\n",
    "    spike_train = spike_recorder.spike_trains()[0]\n",
    "#     print(spike_train)\n",
    "    spike_trains_dict[delay / ms] = spike_train #Careful, delay is not a Quantity anymore because in must be hashable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 556,
   "metadata": {
    "collapsed": false,
    "outputHidden": false,
    "inputHidden": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bNsH69fDrr+6xfj3s2AHHj599XOiEf801cNNN7lG8uHuceZ4tBL8y+zuL6HwRKQ2UxK0udub194MVzJjUasiQIbz66qs89NBD9O/fn8KFC3sdyZzn5El3Qt++/ezjxx+LMXy4O9lv2QI+39n333CDO4nfeqs7kWfJApkz//3n4cNnC8eSJfDRR38tFBUrwv33Q4sWULBgyv93X4hfRUBE+uHa/Uvi5gxqAHwHWBEwYe/UqVO8/fbblC1blpo1a/Lcc8/Rtm1bSpcu7XW0sBYf707I536b37ABfvsNfv/97+/Pnv06oqLcifrBB//6DT5HjivLcOIEbNzoMqxdC19+CT17ukfVqq4g3Hcf5M17df+tV8Pf5qD/A8oCP6vqIyJyPfBO8GIZE/p8Ph9Tp06lb9++bNmyhSeffJKaNWuSJ08em+IhhR05AosWwfz5sGqVO+Hv3PnX9xQo4E7ojRtD/vx/f/z00/fcdtttAc2VOTNER7tH8+bQty9s3gzTp8O0afD00/DMM1CzJjzwALRrBxkzBjTCJflbBE6oqk9EEkUkO/A7EBXEXMaEtLlz59KjRw9WrVpFuXLl+Oabb6hXr57XscLGyZPwww/w3//CvHnw00+QlASZMkGZMlCnzl/b5IsWdU02oSAqCp5/3j1+/dUVg2nToHNnWL0a3ngjZfP4WwRiReRa4G1gGXAM+CloqYwJUaqKiLB69Wri4+OZOnUqLVq0ICLCbqYPtt9/d9+gv/4a/vc/1zEbEQGVK7sTat26UK1ayn+TvhrFi8OLL7rHU0/Ba6+5JqLatVMug78dw08k//qWiHwLZFfVVcGLZUxoWblyJS+88AItWrSgTZs2dO7cmS5dupA+vd0rGUzHjsHnn8OUKTB3rvu2f/PN8Oij7qRfuzZce63XKQNj8GDXZ9CunWvSSqkrl0stL1nh/AfwLyBd8u/GpGmbNm2iVatWlC9fniVLlnD69GkAMmTIYAUgSE6fhlmzoFUruP56eOgh16nas6drLomLgzFjoEmTtFMAALJmhXfecUNTk28rSRGXuhJ45SJ/U6BOALMYE1KGDRtGnz59SJ8+Pc8//zzPPvssOXPm9DpWmnLihGsXX7vWndzXroXFi2HfPsiZ0xWAVq2gRg3X9JPW3X47dOoEo0a5UUNVqwZ/n5daXvL24EcwJnQcOnSI9OnTkzVrVm6++WY6duxInz59uPHGG72OluolJroRPHPmwJo17qS/ZcvZcfSRkVCkCNxxhxspU79+6mrfD5Rhw1y/x6OPwvLlrrM7mPydNiKLiPQRkQnJz4uJSMPgRjMm5Rw/fpzhw4cTFRXFK6+4C+AmTZowbtw4KwBX4eRJ17TTrp274apuXRg9GrZtg0qVoF8/19n7yy9uXP+vv8LUqa6pJxwLAED27PD2265IDhwY/P35OzroXdyooOrJz3cAHwNfBSKEiEQCscBOVbXiYlLM6dOnmTRpEi+99BK7d+/m7rvvpkmTJl7HStWOH4fZs2HGDNfReeSIO7E1bOjGytevHzrDNUNVvXrwyCPuqqB5c6gQxB5Yf4tAEVW9X0RaAqjqCQns5OfdgDggewC3acwlderUiUmTJlGjRg2mTZtGrVq1vI6Uapw65e7APdOef6ZN/9df3RVArlzwf//nTmJ33BG+3+yv1CuvwLffumIQEwMZMgRnP/4WgVMikhnXGYyIFAFOBiKAiOQD7gEGA88EYpvG/BNV5aeffiIqKooCBQrQtWtXmjVrxt13322LulzEyZPw889uPpylS13zzcaNbsgmgAgULgwlSrhvsfXqueGb6WwV8yuWMye89ZZrGhs2LHgjhvz9n6gf8C2QX0SmADWAtgHKMBroCVwToO0Zc0FLly6lV69eLFq0iN9//52RI0dStmxZypYt63W0kLN3rzvZL1niHrGxrhCAO9mXL+9Gr5QoASVLurtzrYkn8Bo3hpYtXd/AvfdCMKaj8vdmsbkishyoCgjQTVX3X+3OkzuXf1fVZSJy20Xe1xHoCFCgQIGr3a0JM2vWrKF3797MnDmT66+/nq5duzJkyBCvY4UUVXeD0owZ8OmnbvQOuCaIihWhSxeoXt3dkWv95Cnr9dfd1BiPPOIKc6Cvri66uQvcELY7+WcBESmgqsuvcv81gMYicjduiursIvKBqrY+902qOgGYAFCpUqVUtmSD8dro0aNZsGABgwcPplu3bsTExJAhWA2sqYgqrF17DbNmuRP/pk1uLH6tWjB8uBubX6FC8IcomovLnRvGjnX3S/zwg5tsLpD8vVksE1AJWIm7EigD/IhbXvKKqWovoBdA8pVAj/MLgDGXa+/evQwePJhWrVpRpUoVhgwZwtChQ8mVK5fX0Tx38KA7kcye7U78O3ZUJF0613H73HOu/fm667xOac53331uSG1UEKbt9OtmMRH5COioqr8kPy8N9Ah8HGOu3OHDhxk5ciSjRo0iISGBIkWKUKVKlbCd1lnVzWN/pl1/yRI3egfcSJ369aF16zh69iyB3Qgd2kSCUwDA/47hm88UAABVXS0i5QIZRFUXAgsDuU0TPt566y169+7NwYMHadGiBQMHDuSmm27yOlaKOn7cdeCee9I/cMD9LWdO16bfqpX7Wbmym6tm4cK95MxZwtvgxlP+FoE4EXkH+AA3TLQ1bly/MZ5JTEwkIiKCiIgIDhw4QOXKlRkyZAgVgnlnTQjZseOvJ/yff3ZTM4CborhJE3fCr17dPQ+HuXfM5fO3CDwCPI67qQvgf8CbQUlkzCWoKjNmzKBPnz4MHDiQ++67j169eoXFYu6bNsGkSW5q5d9+c69lyQK33OJm2axe3U06Zt0fxl/+DhFNAEYlP/5GRGaoavNABjPmQubNm8fzzz/PsmXLKFmy5J+zeqblRV1OnHCduBMnwoIF7ht9/frQvbs76ZcpAzartblSgRpxaktNmqBr164dkyZNokCBArz33nu0bt2ayMhIr2MFzYoV7sT/wQdw6JDrGBw0CNq29XZhcpO2BKoI2Nh9ExRxcXEUKlSIzJkzc88991CmTBk6depExjQ4Ec2xY24u/fnz3XTLq1a5UTzNm7tZOG+7zdr1TeDZzB4mJG3bto3+/fvzn//8h2HDhtGjRw+aNWvmdayASkhwY/bnz3ePH390HbsZMrhmntdfd6N5/vUvr5OatCxQRcBm3jIBsX//foYMGcK4ceMQEbp160abNm28jhUwGze6BUNmzXKLpSckuG/3lSrBs89CnTruTt3Mmb1OasKF30UgeRbRAqr66wX+/FzgIplw1rp1a+bOnUvbtm3p169fqp8r6tQpd7KfNcud/Nevd68XLw4dO7o7dWvXhhw5vM1pwpdfRUBEGgEjgQxA4eQbxQaoamMAVZ0TvIgmLTt58iTjx4+nRYsW3HDDDQwfPpz06dNTokTqvYHp9Gn47DP46COYO9e19WfM6Nr0u3SBu+92yygaEwr8vRLoD9xC8h29qrpCRAoFJZEJC0lJSUyePJl+/fqxbds2VJVu3bpRpkwZr6Ndsd273bKAb73lfs+XD1q3dif9OnXcHbrGhBp/i0Ciqh62RTdMIMycOZMXXniBtWvXUrFiRSZOnEjdunW9jnVFVN3dumPHwiefuI7d+vVdMahf3y2ebkwo87cIrBaRB4FIESkGdAWWBC+WScumT59OUlISn3zyCc2aNUuVK3odOeIWSB83zo3nz5EDnnwSHn8cihXzOp0x/vN31PGTQCnckpIfAkeAp4IVyqQty5Yto379+qxcuRKAsWPHsnr1apo3b56qCsCBA/Duu27B9Dx5oEMHt7zi+PGwcye8+qoVAJP6+HslcJ2q9gb+nJxFRCoDMUFJZdKE9evX07dvX6ZPn06uXLnYunUrZcuW/XOqh9Rgzx74/HO34taCBe6kX6AAdO7sFlGvVs1N82tMauVvEfhURBqp6k4AEbkVGAdEBy2ZSdWeeeYZXn/9dTJlykSfPn3o0aMHOVLJOEifD2bOhNdec8M7Vd03/GefdXfvVqxoJ36TdvhbBB4DPk8eKloBGALcHbRUJlU6dOgQOXLkQETIkSMHnTt3pnfv3lyXSpaqOn0aPvwQhg2DuDg3V0+/fu7EX6qUnfhN2uTvLKIxItIVmAMkAHeq6r6gJjOpRnx8PKNHj2bEiBFMmTKFe+65h379+nkdy2/Hj7vpmUeMgG3b3KycU6e65p5AL+ptTKi51ELzX/LXyeGyAIeBiSLCmZvFTHg6deoUb7/9NgMHDmTv3r00adKEqGCtgRcEhw7BG2/A6NGwb5+bruGNN9y4fvvWb8LFpb7njEyRFCZVqlu3LosXL6Z27dp89tlnVKtWzetIfklIiGDoUBg6FA4fduP5X3gBatXyOpkxKe9SC80vSqkgJvSpKrNnz6ZOnTpkyJCBZ555ht69e3PXXXeliqGeiYnw3nvQq1cV9u93Qz1fegnCZDVKYy7oovcJiMh3yT+PisiR83+mTEQTChYvXkytWrVo0KABU6ZMAaBJkybUq1cv5AuAqhvmGR3txvZfd10CixbBl19aATDmokVAVWsm/7xGVbOf/zNlIhovrVy5knvuuYdbb72VLVu2MH78eFq3bg0Q8id/cIu01KgB997rnn/2GYwd+zO33uptLmNCxeVMJV0BqInrKP5OVX8OWirjOVU3HqB9+/Zs3LiRYcOG0aVLF7JkyeJxsks7ftx983/3XZg3D/79bzeXT9u2brTPwoVeJzQmdPg1bYSIvAj8B8gF5AbeE5E+wQxmvLF7926eeuopDh48iIgwefJkNm/eTM+ePUO6APh8sGiRW4bxhhvcilwbNrjO3w0boH17G+5pzIX4+8+iJVBeVRMARGQosBwYFKxgJmUdOnSI4cOHM3r0aE6fPk3t2rW59957ufnmm72OdlEbNsDkye6xdStkywb33Qdt2rjRPrYmrzEX528R2Apkwt0oBpAR2BSMQCZlqSrDhw9n6NChHD58mAcffJCXXnqJIiG86smJE/Dxx66J57vv3Im+bl0YPBiaNoUQvmAxJuT4WwROAmtEZC6uT+BO4DsReR1AVbsGKZ8JElVFRBARli5dSo0aNRg8eDBly5b1Oto/+uUXd+KfPNnd6FWsmGvuad0a8ub1Op0xqZO/ReCz5McZCwOxcxHJD7wP3AD4gAmq+logtm0uzOfzMX36dF566SVmzpzJTTfdxLRp08iYMaPX0S4oPh6mTXMn/x9+gAwZ3Fw+HTu6tXlTwQAlY0Kav3MH/efM7yKSE8ivqqsCsP9EoLuqLheRa4BlIjJXVdcGYNvmHKrKt99+S69evVixYgXR0dEcPnwYICQLQEICjBzp5vM5cgRKlHDz9T/0EOTO7XU6Y9IOfxeaXwg0Tn7/CmCfiCxS1WeuZuequhvYnfz7URGJA/ICVgQCyOfz0bNnT2JjYylcuDAffPABLVu2JCJEe02/+gq6dYPNm6FZM3j6aTfW3771GxN4/p4FcqjqEaAZ8K6qVgQCuihs8sL15YEfL/C3jiISKyKx+/bZ5KX+2rx5MwARERFER0czbtw41q1bR6tWrUKyAGzaBI0auUfGjG6M/4wZULOmFQBjgsXfM0E6EbkRaAF8FegQIpINmAE8lVxs/kJVJ6hqJVWtlCdPnkDvPs3ZsmULDz/8MEWLFmX+/PkAPPzwwzzxxBNkyJDB43R/d/w49O0LJUu6G7lGjoSVK+GOO7xOZkza52/H8ABgNu5O4RgRiQI2BCKAiKTHFYApqvppILYZrvbu3cugQYMYP348kZGR9OjRI6RH+/h8bhqHZ55x8/i3agXDh7s7fI0xKcPfjuGPgY/Peb4ZaH7muYj0UtWXL3fn4iafmQjEqeqrl/t5c1ZSUhJVq1Zl+/bttGvXjhdffJG8ITpucs8eN5vnO++4JqDoaHe3r83nY0zKC1TD8H1X+LkawENAHRFZkfywZSv9dOLECSZMmEBSUhKRkZGMGzeOuLg4xo8fH3IFwOeD2bPd8M78+aFXL8iXDz74AJYvtwJgjFcCNZvKFXXbqep3V/rZcJaYmMh7771H//792blzJ/nz56dBgwbcfXfo1c9du9zSjRMnumkdcueGp55yc/kUL+51OmNMoK4E9IFG0aAAABACSURBVNJvMVfL5/Px8ccfU6pUKTp06ED+/PlZsGABDRo08Dra36xY4cb0FyzoOn2LFIGPPoIdO9zYfysAxoQGT68EzOXx+Xz069ePdOnS8fnnn9O4ceOQmtNfFebOdSf5efMga1bo0gU6d4aiRb1OZ4y5kEBdCXx86beYK/HTTz/RvHlzjhw5Qrp06fj2229ZtWoVTZo0CZkCcOoUvP8+lC0L9erBmjVuTp/t22HUKCsAxoQyf9cT+I+IXHvO85wiMunMc1UdEoxw4Wzt2rU0a9aMKlWqsHjxYtaudTdRFyhQgMjISI/TOaowfjwULuymbvb53EIuW7bAc89BzpxeJzTGXIq/VwJlVPXQmSeq+gfu7l4TYKdOneLRRx8lOjqaefPmMWDAADZt2kTVqlW9jvYXPp+bzqFTJ9fe/803bpbPtm3d3b7GmNTB3z6BCBHJmXzyR0T+dRmfNX5ISEggU6ZMZMiQgf379/PUU0/Rq1cvcofgbGknT8LDD8P06W6kzyuv2OItxqRW/p7IXwGWiMgnuJFALYDBQUsVRo4ePcqrr77K2LFjiY2NpWDBgsycOTNk2vvPd+iQW7R94ULXAdy9u83rY0xq5u8dw++LyDLgdtxIoGY23fPVSUhI4K233mLw4MHs37+fZs2a/fm3UC0AO3dCgwawbp27yatVK68TGWOulr9TSddV1XnAmnNea3PuOgPGfwkJCZQqVYrNmzdzxx138PLLL1O5cmWvY11UXJwb+fPHHzBrllvO0RiT+vnbkvuiiLwpIllE5HoR+RJoFMxgaY2qsnTpUgAyZcpEp06dmDt3LvPmzQv5AvD9924+/9On4X//swJgTFribxGojVtYfiXwHfChqv5f0FKlMQsWLKBatWpUr16d2NhYAJ599lnqhvjZ9OhRGDPGnfTz5IElS6C8jQkzJk3xtwjkBKrgCsFJoKCEasN1CFm2bBn16tWjTp067Ny5k4kTJ1KuXDmvY13SsmVuDd8bb4SuXeGWW9zVQOHCXiczxgSav6ODfgCGquokEckMDAO+B6oHLVkqFx8fT926dYmMjOTVV1/l8ccfJ1OmTF7H+kfHjrm5fcaPh9hYyJwZHnjA3QdQubKNADImrfK3CNRV1W0AqnoC6CoiNvnveXbs2MG7775L7969yZo1K59//jnly5cne/bsXkf7R7/+Cq+95kb7HD0KpUu7JqDWreHaay/9eWNM6uZvEdguIq2BKFUdICIFgIQg5kpVDhw4wNChQxkzZgw+n49GjRpRrlw5ateu7XW0f/Tzz/Dyy/DJJ5AhA9x/Pzz2GFSrZt/6jQkn/vYJvAFUA1omPz8KjAtKolQkISGBQYMGERUVxSuvvML999/P+vXrQ7rd//vv4Z57oEIFt8hLr15uorf//AeqV7cCYEy48fdKoIqqVhCRn8HNHSQiobdieQqLiIhg0qRJ3H777QwaNIjSpUt7HemCVCEmJif9+7tlHHPnhkGD3BTP1uRjTHjztwicFpFIkhePEZE8gC9oqUJUUlISU6dOZfz48cyZM4fMmTOzfPlyrg3hM+nChdCzJ8TElCVvXje1c4cObq5/Y4zxtznodeAz4DoRGYy7VyBspo9WVb766ivKly/PQw89RHx8PLt27QII2QKwdSvcdx/cfjvs3Qvdu//Kpk1uwjcrAMaYM/wqAqo6BegJvAzsBpqqalgsJPPHH39Qs2ZNGjVqREJCAtOmTSM2NpYiRYp4He2C4uPhxRehRAn4+msYMMDN9dOw4W6b4tkY8zd+TwetquuAdUHMElL2799P7ty5ufbaa8mbNy/jx4/nkUceIX369F5HuyBVmDrVNf3s3AktW8KwYZA/v9fJjDGhzGaBP8/GjRt58MEHKVy4MHv27EFEmD59Oh07dgzZAhATAzVrulk9r78eFi+GDz+0AmCMuTQrAsl27drF448/TokSJZg5cyZdu3Ylc+bMXsf6R0ePwttvQ9WqblqHjRvhnXfgp59cQTDGGH/Y6mC4pp+bbrqJkydP0rFjR/r06cONN97oday/UYWlS93Jfvp01/5fsqRb2atdO8iRw+uExpjUJmyLwPHjx5k3bx6NGzcmV65cjBw5kjvvvDMkO3x37nTt/RMnuk7erFndvD7t20OVKnaDlzHmyoVdETh9+jQTJ05kwIAB7Nmzh40bN1K4cGEiIjoxYMA/f07EzaJZtiyUKQOFCgVnXd39+90EbmceMTGQPBqVatVcIWjRArJlC/y+jTHhx/MiICL1gdeASOAdVR0ajP34fD6mTZtG37592bRpEzVr1mT69OlERUWRmAhPP+3m0PmnYf+Jie4buap7fs01EB3tCkLZsm5IZkQEnDp19nH69F+fX+z1HTvcCf+3387us3hxN86/cmW4807X9GOMMYHkaRFIvgt5HHAnsAOIEZEvgrF+8b59+2jfvj1Fixbl66+/pkGDBn+u5RsXB8ePu2mUW7f+523Ex8Pq1bBqFaxc6X5OnQpvvXVlmURc4cmQwS3aUqWKm8qhcmW3eIu18Rtjgs3rK4FbgI2quhlARD4CmgABLwLXX389S5cupXTp0kSc146TvNgXl1rlMWtWd6KuUuXsa6qwbRusX//Xk/r5j/Tp//5aZGSA/yONMeYyeV0E8gLbz3m+A7eCWVCUKVPmgq/HxED27FCs2OVvUwQKFnQPY4xJbby+T+BC41r0b28S6SgisSISu2/fvoCHiImBihWD09FrjDGhzOvT3g7g3Pta8wG7zn+Tqk5Q1UqqWilPnjwBDXDypGvfv1RTkDHGpEVeF4EYoJiIFE5en+AB4IuUDPDLL260jhUBY0w48rRPQFUTRaQLMBs3RHSSqq5JyQwxMe6nFQFjTDjyumMYVZ0FzPJq/zExbqWtAgW8SmCMMd7xujnIczEx7irApl4wxoSjsC4C8fGwdq01BRljwldYF4Gffwafz4qAMSZ8hXURONMpXKmStzmMMcYrYV0EYmMhXz644QavkxhjjDfCugic6RQ2xphwFbZF4NAh2LDBioAxJryFbRE4M3Oo9QcYY8KZFQErAsaYMBa2RSAmBooWhZw5vU5ijDHeCesiYFcBxphwF5ZFYO9e2L7dOoWNMSYsi4C/y0kaY0xaF5ZFICbGrSJWvrzXSYwxxlthWwRKlIBs2bxOYowx3gq7IqBqdwobY8wZYVcEtm+HffusCBhjDIRhEbDlJI0x5qywLALp00OZMl4nMcYY74VlEShTBjJm9DqJMcZ4L6yKgM8Hy5ZZU5AxxpwRVkVg40Y4fNimizDGmDPCqghYp7AxxvxVWBWB2FjInBlKlvQ6iTHGhIawKgIxMVChAqRL53USY4wJDWFTBBITYfly6w8wxphzhU0RWLsWTpyw/gBjjDmXZ0VAREaIyDoRWSUin4nItcHcn00fbYwxf+fllcBcoLSqlgHWA72CubOYGMiRwy0paYwxxvGsCKjqHFVNTH76A5AvmPvLlQuaNnXrCBhjjHFCZZzMo8C0f/qjiHQEOgIUKFDginYwaNAVfcwYY9K0oBYBEZkH3HCBP/VW1ZnJ7+kNJAJT/mk7qjoBmABQqVIlDUJUY4wJS0EtAqpa92J/F5E2QEPgDlW1k7sxxqQwz5qDRKQ+8BxQW1WPe5XDGGPCmZfdpGOBa4C5IrJCRN7yMIsxxoQlz64EVNUGaxpjjMdswKQxxoQxKwLGGBPGrAgYY0wYk9Q2MlNE9gG/XcZHcgP7gxQnNbHjcJYdi7PsWJyV1o9FQVXNc/6Lqa4IXC4RiVXVsJ9A2o7DWXYszrJjcVa4HgtrDjLGmDBmRcAYY8JYOBSBCV4HCBF2HM6yY3GWHYuzwvJYpPk+AWOMMf8sHK4EjDHG/AMrAsYYE8bSbBEQkfoi8quIbBSR573O4xURyS8iC0QkTkTWiEg3rzN5SUQiReRnEfnK6yxeE5FrReST5LW+40SkmteZvCAiTyf/21gtIlNFJJPXmVJSmiwCIhIJjAMaACWBliJS0ttUnkkEuqtqCaAq0DmMjwVANyDO6xAh4jXgW1W9GShLGB4XEckLdAUqqWppIBJ4wNtUKStNFgHgFmCjqm5W1VPAR0ATjzN5QlV3q+ry5N+P4v6h5/U2lTdEJB9wD/CO11m8JiLZgVuBiQCqekpVD3mbyjPpgMwikg7IAuzyOE+KSqtFIC+w/ZznOwjTE9+5RKQQUB740dsknhkN9AR8XgcJAVHAPuDd5Oaxd0Qkq9ehUpqq7gRGAtuA3cBhVZ3jbaqUlVaLgFzgtbAeCysi2YAZwFOqesTrPClNRBoCv6vqMq+zhIh0QAXgTVUtD8QDYdd3JiI5ca0EhYF/A1lFpLW3qVJWWi0CO4D85zzPR5hd4p1LRNLjCsAUVf3U6zweqQE0FpGtuObBOiLygbeRPLUD2KGqZ64KP8EVhXBTF9iiqvtU9TTwKVDd40wpKq0WgRigmIgUFpEMuI6eLzzO5AkREVy7b5yqvup1Hq+oai9VzaeqhXD/f5ivqmH1je9cqroH2C4ixZNfugNY62Ekr2wDqopIluR/K3cQZh3kni0vGUyqmigiXYDZuN7+Saq6xuNYXqkBPAT8IiIrkl97QVVneZjJhIYngSnJX5Q2A494nCfFqeqPIvIJsBw3ku5nwmz6CJs2whhjwlhabQ4yxhjjBysCxhgTxqwIGGNMGLMiYIwxYcyKgDHGhDErAsYYE8asCJiwkDxt8hOXeM+/k8eMX2pbx/zcZ1MRedHfjBfZzkgRqXO12zHmQuw+ARMWkifP+yp5uuCr3dYxVc3mx/uWAI1Vdf9V7q8g8Laq3nU12zHmQuxKwISLoUAREVkhIiMu9AYRKSQiq5N/bysin4rItyKyQUSGn/fewSKyUkR+EJHrL7Ctm4CTZwqAiLwnIm8mL/CzWURqi8ik5MVc3kt+T2Ty+1aLyC8i8jSAqv4G5BKRGwJ5QIwBKwImfDwPbFLVcqr6rJ+fKQfcD0QD94vImUkJswI/qGpZ4H9Ahwt8tgZuKoJz5QTqAE8DXwKjgFJAtIiUS95fXlUtrarRwLvnfHZ58jaNCSgrAsb8s/+q6mFVTcBNrlYw+fVTwJnlKZcBhS7w2Rtx8/Wf60t17a+/AHtV9RdV9QFrkrexGYgSkTEiUh84d8rv33FTHRsTUFYEjPlnJ8/5PYmzEy6e1rOdaee+fq4TwPlr1Z7Znu+8bfuAdKr6B26Zx4VAZ/66Alqm5G0aE1BWBEy4OApck4L7iwOKXs4HRCQ3EKGqM4C+/HV+/5uA1YGLZ4xjRcCEBVU9AHyf3Ol6wY7hAPsfUD55jnp/5QUWJk/5/R7QC/5cFKgoEBvokMbYEFFjgkREXsP1A8y7yu3cC1RQ1b6BSWbMWXYlYEzwDAGyBGA76YBXArAdY/7GrgRM2BGRaGDyeS+fVNUqXuQxxktWBIwxJoxZc5AxxoQxKwLGGBPGrAgYY0wYsyJgjDFh7P8Bdp5x7p8gO9sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_response_dict = get_mean_response(spike_trains_dict, t_isi)\n",
    "plot_mean_response(mean_response_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 557,
   "metadata": {
    "collapsed": false,
    "outputHidden": false,
    "inputHidden": false
   },
   "outputs": [],
   "source": [
    "# plot_phase_response_curve(mean_response_dict, t_isi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 558,
   "metadata": {
    "collapsed": false,
    "outputHidden": false,
    "inputHidden": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_spiking(spike_trains_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 559,
   "metadata": {
    "collapsed": false,
    "outputHidden": false,
    "inputHidden": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "micro_circuit_notebooks",
   "language": "python",
   "display_name": "micro_circuit_notebooks"
  },
  "language_info": {
   "name": "python",
   "version": "3.7.5",
   "mimetype": "text/x-python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "pygments_lexer": "ipython3",
   "nbconvert_exporter": "python",
   "file_extension": ".py"
  },
  "kernel_info": {
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