{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 81,
   "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": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "import brian2 as br\n",
    "from brian2.units import *\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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": [
    "# A simple winner-takes-it-all network\n",
    "\n",
    "- show that two excitatory LIF neurons coupled via a common interneuron can compete and implement a kind of winner-takes-it-all network, where the neuron with more input suppresses its competitor\n",
    "- implement a simple network in brian2 as a preparatory step to build the spatial network\n",
    "- show $I_1-I_2$ and $r_1-r2$, should be non-linear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "lif_model_eqs = \"\"\"\n",
    "dv/dt =1.0/tau* (-v + u_ext) :volt (unless refractory)\n",
    "tau :second\n",
    "u_ext = u_ext_const + u_ext_diff * sin(omega * t) : volt\n",
    "u_ext_const : volt\n",
    "u_ext_diff : volt\n",
    "v_threshold: volt\n",
    "v_reset: volt\n",
    "tau_refractory: second\n",
    "\"\"\"\n",
    "\n",
    "delta_synapse_model = 'w: volt'\n",
    "delta_synapse = 'v+=w'\n",
    "\n",
    "threshold = \"v>v_threshold\"\n",
    "reset = \"v=v_reset\"\n",
    "refractory = \"tau_refractory\"\n",
    "\n",
    "excitatory_neuron_properties = {\n",
    "    \"tau\": 10*ms,\n",
    "    \"v_threshold\": -40*mV,\n",
    "    \"v_reset\": -55*mV,\n",
    "    \"tau_refractory\": 5.5*ms\n",
    "}\n",
    "\n",
    "inhibitory_neuron_properties = {\n",
    "    \"tau\": 7*ms,\n",
    "    \"v_threshold\": -40*mV,\n",
    "    \"v_reset\": -75*mV,\n",
    "    \"tau_refractory\": 0.0*ms\n",
    "}\n",
    "\n",
    "duration = 1000*ms\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# in_ex_connectivity=np.array([[1, 1], [0,1]]) #a one indicates a connection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "excitatory_neurons = br.NeuronGroup(N=2, \\\n",
    "                                    name='excitatory_neurons',\\\n",
    "                                    model=lif_model_eqs, \\\n",
    "                                   threshold=threshold, \\\n",
    "                                   reset=reset,\\\n",
    "                                   refractory = refractory)\n",
    "\n",
    "set_parameters_from_dict(excitatory_neurons, excitatory_neuron_properties)\n",
    "excitatory_neurons.v = excitatory_neuron_properties[\"v_reset\"]\n",
    "\n",
    "inhibitory_neurons = br.NeuronGroup(N=1, \\\n",
    "                                    name='inhibitory_neurons',\\\n",
    "                                    model=lif_model_eqs, \\\n",
    "                                   threshold=threshold, \\\n",
    "                                   reset=reset,\\\n",
    "                                   refractory = refractory)\n",
    "\n",
    "set_parameters_from_dict(inhibitory_neurons, inhibitory_neuron_properties)\n",
    "inhibitory_neurons.v = inhibitory_neuron_properties[\"v_reset\"]\n",
    "\n",
    "excitatory_spike_recorder = br.SpikeMonitor(source=excitatory_neurons)\n",
    "inhibitory_spike_recorder = br.SpikeMonitor(source=inhibitory_neurons)\n",
    "exc_state_mon = br.StateMonitor(excitatory_neurons, 'v', record=True)\n",
    "inh_state_mon = br.StateMonitor(inhibitory_neurons, 'v', record=True)\n",
    "u_ext_mon = br.StateMonitor(excitatory_neurons, 'u_ext', record=True)\n",
    "\n",
    "ex_in_synapses = br.Synapses(source=excitatory_neurons, target=inhibitory_neurons, model=delta_synapse_model, on_pre = delta_synapse )\n",
    "ex_in_synapses.connect()\n",
    "\n",
    "in_ex_synapses = br.Synapses(source=inhibitory_neurons, target=excitatory_neurons, model=delta_synapse_model, on_pre = delta_synapse )\n",
    "in_ex_synapses.connect()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "net = br.Network(excitatory_neurons, inhibitory_neurons)\n",
    "net.add(ex_in_synapses, in_ex_synapses)\n",
    "net.add(excitatory_spike_recorder, inhibitory_spike_recorder, exc_state_mon, inh_state_mon, u_ext_mon)\n",
    "net.store()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "def experiment(ex_const, ex_diff_1, ex_diff_2, in_drive, w_ex_in, w_in_ex, duration=1000*ms):\n",
    "    net.restore()\n",
    "    omega = 4 * np.pi / duration\n",
    "    excitatory_neurons.u_ext_diff = [ex_diff_1, ex_diff_2]\n",
    "    excitatory_neurons.u_ext_const = ex_const\n",
    "    inhibitory_neurons.u_ext_const = in_drive\n",
    "    inhibitory_neurons.u_ext_diff = 0.0\n",
    "    ex_in_synapses.w = w_ex_in\n",
    "    in_ex_synapses.w = w_in_ex\n",
    "    net.run(duration=duration)\n",
    "    return {\n",
    "        \"excitatory_spikes\": excitatory_spike_recorder.spike_trains(),\n",
    "        \"inhibitory_spikes\": inhibitory_spike_recorder.spike_trains(),\n",
    "        \"exc_state\": exc_state_mon.v,\n",
    "        \"inh_state\": inh_state_mon.v,\n",
    "        \"u_ext_trace\": u_ext_mon.u_ext,\n",
    "        \"state_time\": exc_state_mon.t\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "u_ext_const = - 38 * mV\n",
    "u_ext_diff_1 = 1.5 * mV\n",
    "u_ext_diff_2 = -1.5 * mV\n",
    "\n",
    "inhibitory_drive = -42*mV\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO       No numerical integration method specified for group 'excitatory_neurons', using method 'euler' (took 0.01s, trying other methods took 0.01s). [brian2.stateupdaters.base.method_choice]\n",
      "INFO       No numerical integration method specified for group 'inhibitory_neurons', using method 'euler' (took 0.01s, trying other methods took 0.01s). [brian2.stateupdaters.base.method_choice]\n"
     ]
    }
   ],
   "source": [
    "ex_synaptic_strength = 10*mV\n",
    "in_synaptic_strength = -60*mV\n",
    "with_inhibitory_neuron = experiment(u_ext_const, u_ext_diff_1, u_ext_diff_2, inhibitory_drive, ex_synaptic_strength, in_synaptic_strength, duration) \n",
    "\n",
    "synaptic_strength = 0*mV\n",
    "without_inhibitory_neuron = experiment(u_ext_const, u_ext_diff_1, u_ext_diff_2, inhibitory_drive, synaptic_strength, -synaptic_strength, duration)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_excitatory_spike_count_difference(result_dict):\n",
    "    return len(result_dict[\"excitatory_spikes\"][0])-len(result_dict[\"excitatory_spikes\"][1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot rate difference against drive difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_spiking(result_dict):\n",
    "    ex_spike_trains = result_dict[\"excitatory_spikes\"]\n",
    "    in_spike_trains = result_dict[\"inhibitory_spikes\"]\n",
    "\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    for key, times in ex_spike_trains.items():\n",
    "        ax.plot(times/ms, key/2.0*np.ones(times.shape), 'b|')\n",
    "\n",
    "    offset=2\n",
    "    for key, times in in_spike_trains.items():\n",
    "        ax.plot(times/ms, (key+offset)/2.0*np.ones(times.shape), 'r|')\n",
    "\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": 95,
   "metadata": {
    "collapsed": false,
    "outputHidden": false,
    "inputHidden": false
   },
   "outputs": [],
   "source": [
    "def plot_states(result_dict):\n",
    "    ex_state_mon = result_dict[\"exc_state\"]\n",
    "    in_state_mon = result_dict[\"inh_state\"]\n",
    "    state_t = result_dict[\"state_time\"]\n",
    "    \n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    plt.plot(state_t/ms, in_state_mon[0],'r')\n",
    "    plt.plot(state_t/ms, ex_state_mon[0]-0.035*volt,'b')\n",
    "    plt.plot(state_t/ms, ex_state_mon[1]-0.07*volt,'g')\n",
    "    ax.grid(axis='x')\n",
    "    ax.set_xlabel(\"Time(ms)\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false,
    "outputHidden": false,
    "inputHidden": false
   },
   "outputs": [],
   "source": [
    "def get_firing_rate(spike_train, t_win, time_points):\n",
    "    firing_rate = np.zeros(time_points.shape)\n",
    "    for i, t in enumerate(time_points):\n",
    "        if t > 0.5 * t_win and t < time_points[-1] - 0.5 * t_win:\n",
    "            firing_rate[i] = len([t_spike for t_spike in spike_train if t - 0.5 * t_win < t_spike and t_spike < t + 0.5 * t_win]) / t_win\n",
    "#             firing_rate[i] = np.where(t - 0.5 * twin < spike_train and spike_train < t + 0.5 * twin).shape[0] / t_win\n",
    "    return firing_rate\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false,
    "outputHidden": false,
    "inputHidden": false
   },
   "outputs": [],
   "source": [
    "def plot_firing_rate(result_dict):\n",
    "    ex_spike_trains = result_dict[\"excitatory_spikes\"]\n",
    "    in_spike_trains = result_dict[\"inhibitory_spikes\"]\n",
    "    state_t = result_dict[\"state_time\"]\n",
    "    \n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    t_values = np.linspace(0.0,state_t[-1],500)\n",
    "    plt.plot(t_values, get_firing_rate(in_spike_trains[0], 0.05 * state_t[-1], t_values), 'r')\n",
    "    plt.plot(t_values, get_firing_rate(ex_spike_trains[0], 0.05 * state_t[-1], t_values), 'b')\n",
    "    plt.plot(t_values, get_firing_rate(ex_spike_trains[1], 0.05 * state_t[-1], t_values), 'g')\n",
    "#     plt.plot(state_t/ms, ex_state_mon[0]-0.035*volt,'b')\n",
    "#     plt.plot(state_t/ms, ex_state_mon[1]-0.07*volt,'g')\n",
    "    ax.grid(axis='x')\n",
    "    ax.set_xlabel(\"Time(ms)\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false,
    "outputHidden": false,
    "inputHidden": false
   },
   "outputs": [],
   "source": [
    "def plot_firing_rate_hysteresis(result_dict):\n",
    "    ex_spike_trains = result_dict[\"excitatory_spikes\"]\n",
    "    in_spike_trains = result_dict[\"inhibitory_spikes\"]\n",
    "    state_t = result_dict[\"state_time\"]\n",
    "    u_ext_list = result_dict['u_ext_trace']\n",
    "    \n",
    "    #Downsample\n",
    "    n_samples = 500\n",
    "    sample_ids = np.round(np.linspace(0, len(state_t) - 1, n_samples)).astype(int)\n",
    "    t_values = state_t[sample_ids]\n",
    "    u_ext_1 = u_ext_list[0]\n",
    "    u_ext_2 = u_ext_list[1]\n",
    "    u_ext_1_values = u_ext_1[sample_ids]\n",
    "    u_ext_2_values = u_ext_2[sample_ids]\n",
    "    u_ext_difference = u_ext_1_values - u_ext_2_values\n",
    "    \n",
    "    rate_1 = get_firing_rate(ex_spike_trains[0], 0.05 * t_values[-1], t_values)\n",
    "    rate_2 = get_firing_rate(ex_spike_trains[1], 0.05 * t_values[-1], t_values)\n",
    "    rate_diff = rate_1 - rate_2\n",
    "    \n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    plt.plot(u_ext_difference,rate_diff)\n",
    "#     plt.plot(state_t/ms,u_ext_list[1])\n",
    "#     plt.plot(state_t/ms, get_firing_rate(in_spike_trains[0], 500 * ms, state_t), 'r')\n",
    "#     plt.plot(state_t/ms, get_firing_rate(ex_spike_trains[0], 500 * ms, state_t), 'b')\n",
    "#     plt.plot(state_t/ms, get_firing_rate(ex_spike_trains[1], 500 * ms, state_t), 'g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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_firing_rate_hysteresis(with_inhibitory_neuron)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "outputHidden": false,
    "inputHidden": false,
    "outputExpanded": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "# plot_firing_rate(with_inhibitory_neuron)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qkO4fGlpnSWs10DcANw8t7+/ampNkE/BU4AvAI6vqVhiEPvCIblhL++M9wB8Adw+1TULdjwfmgL/pTjddkORIGq+9qr4FvAu4CbgV+F5VfYrG655nubVu6O7Pbx9prQb6QueLmru+MskDgYuBN1bV/y41dIG2w25/JPlN4LaquqLvKgu0HXZ1d9Yz+FP8A1X1VOAOBn9+L6aJ2rvzxdsZnFJ4DHBkklcstcoCbYdd3T0tVuuK98FaDfT9wDFDyxsZ/JnWjCS/wCDM/6GqLumav939uUX387auvZX98QzgRUm+weA02nOS/D3t1w2DWvZX1Re65Y8yCPjWa38ecGNVzVXVj4FLgKfTft3Dllvr/u7+/PaR1mqg7wGOTbI5yRHAacCuVZ7T2HSvWH8QuLaq/nyoaxdwenf/dODjQ+2nJblvks3AsQxeNDmsVNVbqmpjVW1i8Dv9dFW9gsbrBqiq/wJuTvLErum5wFdpv/abgJOSPKB73j+XwWtGrdc9bFm1dqdlvp/kpG6fvXJonaWt9qvCS7xafCqDqz9uAN662vMZc22/xuBPqCuBL3e3U4GHA/8GfL37+bChdd7a7Yvr6PmK91q+Adv42VUuE1E38BRgtvu9fwx46CTUDvwR8DXgauDvGFzV0WTdwIcZvFbwYwZH2q9ZSa3AdLe/bgDeR/eu/lE33/ovSY1Yq6dcJEnLZKBLUiMMdElqhIEuSY0w0CWpEetXewLSSiS551IwgEcBP2Hw1nqA/6uqp4/pcV4MPLmq/vgAt/MuYHdVfXoc85IW4mWLOuwlOQf4QVW96yBs+3PAi6rq9gPczuOAv66q549nZtK9ecpFzUnyg+7ntiSfSfKRJNcn+dMkL0/yxe6zpn+xGzeV5OIke7rbM7r244Af3RPmSXYm+UAGn2W/L8mzus+/vjbJzm7Mum7c1d1jvAmgqr4JPDzJo1Zhl2hCeMpFrTseeBKDjzTdB1xQVSdm8KUirwfeCPwl8BdV9e9JHgtc1q3zDOBL87b3UOA5wIuAT3RjXgvsSfIUBp/lv6GqfhkGX2oxtO6XuvEXH4xCJQNdrdtT3UeXJrkB+FTXfhXw7O7+84AtQ18K8+AkD2LwRSRz/LxPVFUluQr4dlVd1W37GmAT8Bng8Un+CvinoceDwYcyPWaMtUk/x0BX6340dP/uoeW7+dnz/z7A06rqh8MrJvkh8JBFtje8rZ9ur6q+m+R44AXA64DfBn63G3M/4OceQxonz6FLg6PoM+9Z6E6dwOBTAZ+wnA0lORq4T1VdDLyNwUfk3uM4Bh+4JB0UBroEvwdMJ7kyyVeBHV37Z4Gn9v6C3oENwOVJvgzsBN4CP/38+ycw+LRF6aDwskVpCUn+ksF58389wO28hMEXg79tPDOT7s0jdGlpf8Lgi40P1Hrg3WPYjrQoj9AlqREeoUtSIwx0SWqEgS5JjTDQJakRBrokNeL/AVNfSmlZlSKbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_spiking(with_inhibitory_neuron)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "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_firing_rate_hysteresis(without_inhibitory_neuron)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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(without_inhibitory_neuron)"
   ]
  }
 ],
 "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": {
   "name": "micro_circuit_notebooks"
  },
  "nteract": {
   "version": "0.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}