{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "066434e8-1519-4fdb-81fc-865dd87008a0",
   "metadata": {},
   "source": [
    "# Rates and Feedback into Oscillators\n",
    "\n",
    "Now that all of the above works and we can drive a neuronal population with the theta input from the ensemble of oscillators, let's take a look at how the ensemble is affected by the neurons using feedback.\n",
    "\n",
    "We will make a new network; HH neurons will be driven by a _constant_ input to produce spikes; these spikes will be turned into rates through a LPF and these rates will be fed back into the oscillators."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ad729266-d131-4ce4-9a97-705aedf0b9dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO       Cache size for target \"cython\": 13528 MB.\n",
      "You can call \"clear_cache('cython')\" to delete all files from the cache or manually delete files in the \"/home/nikos/.cython/brian_extensions\" directory. [brian2]\n"
     ]
    }
   ],
   "source": [
    "from brian2 import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76186b06-35f6-4191-8cb7-81666ed4ca55",
   "metadata": {},
   "source": [
    "Below we have the equations that we will use in the model. The Kuramoto oscillator equations have been adapted (now _X_ is `(linked)` to the rates)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5e0caea8-f481-4b6e-bcfe-601e31d296af",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Kuramoto oscillators\n",
    "kuramoto_eqs_stim = '''\n",
    "    dTheta/dt = ((omega + (kN * PIF) - G*X*sin(Theta)) * second**-1) : 1\n",
    "    PIF = .5 * (sin(ThetaPreInput - Theta)) : 1\n",
    "    Vm = sin(Theta)*mV : volt\n",
    "    ThetaPreInput : 1\n",
    "    omega : 1\n",
    "    kN : 1\n",
    "    G : 1\n",
    "    X : 1 (linked) # this is linked to the firing rates\n",
    "'''\n",
    "\n",
    "# synapses\n",
    "syn_kuramoto_eqs = '''\n",
    "    ThetaPreInput_post = Theta_pre\n",
    "'''\n",
    "\n",
    "# Order parameter calculation equations\n",
    "pop_avg_eqs = '''\n",
    "    x : 1\n",
    "    y : 1\n",
    "    coherence = sqrt(x**2 + y**2) : 1\n",
    "    phase = arctan(y/x) + int(x<0 and y>0)*pi - int(x<0 and y<0)*pi: 1\n",
    "    rhythm = coherence * sin(phase) : 1\n",
    "    rhythm_test = coherence * (sin(phase)+1)/2 : 1\n",
    "    rhythm_simple = rhythm*nA : amp\n",
    "    rhythm_abs = abs(rhythm)*nA : amp\n",
    "    rhythm_rect = rhythm_test*nA : amp\n",
    "'''\n",
    "\n",
    "syn_avg_eqs = '''\n",
    "    x_post = cos(Theta_pre)/N_incoming : 1 (summed)\n",
    "    y_post = sin(Theta_pre)/N_incoming : 1 (summed)\n",
    "'''\n",
    "\n",
    "\n",
    "# The neuronal model\n",
    "eqs_HH = '''\n",
    "dv/dt = (gl*(El-v) - g_na*(m*m*m)*h*(v-ENa) - g_kd*(n*n*n*n)*(v-EK) + I_ext)/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_ext : amp #(linked)\n",
    "'''\n",
    "\n",
    "# Spikes-2-Rates\n",
    "eqs_FiringRateFilter = '''\n",
    "    dY/dt = -Y/tauFR : 1/second\n",
    "    drive = Y/Hz : 1\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2e62387-25f1-4d2e-bfd3-32ea4e1453cb",
   "metadata": {},
   "source": [
    "Global parameters for the simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f297d04c-5ffb-4a56-9a62-ee61d2677f08",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameters\n",
    "duration = 1*second\n",
    "\n",
    "# Oscillators\n",
    "seed(42)\n",
    "N = 50\n",
    "f0 = 4 # center freq [Hz]\n",
    "sigma = 0.5 # normal std\n",
    "\n",
    "# HH model\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",
    "N = 1e3\n",
    "\n",
    "filter_params = {\n",
    "    'tauFR' : 50*ms\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52810c37-a487-404d-8424-007078d27d69",
   "metadata": {},
   "source": [
    "Let's make the groups one by one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "072759d8-dc1e-49ba-a433-82dde21b616e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Kuramoto oscillators group\n",
    "oscillators = NeuronGroup(N, kuramoto_eqs_stim, threshold='True', method='euler', name='Kuramoto_N_%d' %N)\n",
    "oscillators.Theta = '2*pi*rand()' # uniform U~[0,2π]\n",
    "oscillators.omega = '2*pi*(f0+sigma*randn())' # normal N~(f0,σ)\n",
    "oscillators.kN = 10\n",
    "oscillators.G = 15\n",
    "osc_synapses = Synapses(oscillators, oscillators, on_pre=syn_kuramoto_eqs)\n",
    "osc_synapses.connect(condition='i!=j')\n",
    "\n",
    "# mean phase output\n",
    "population = NeuronGroup(1, pop_avg_eqs)\n",
    "r0 = 1/N * sum(exp(1j*oscillators.Theta))\n",
    "population.x = real(r0)  # avoid division by zero\n",
    "population.y = imag(r0)\n",
    "average = Synapses(oscillators, population, syn_avg_eqs)\n",
    "average.connect()\n",
    "\n",
    "# --------------------------------------------------------------------------\n",
    "# HH Neurons\n",
    "neurons_HH = NeuronGroup(N, eqs_HH, threshold='v > -40*mV', refractory='v > -40*mV', method='exponential_euler')\n",
    "neurons_HH.v = 'El+10*randn()*mV'\n",
    "\n",
    "# Low-Pass Filter group\n",
    "LPF = NeuronGroup(1, eqs_FiringRateFilter, namespace=filter_params, method='exponential_euler')\n",
    "LPF.Y = 0\n",
    "\n",
    "S_LPF = Synapses(neurons_HH, LPF, on_pre='Y_post += (1/tauFR)/%d'%N, namespace=filter_params)\n",
    "S_LPF.connect()\n",
    "# --------------------------------------------------------------------------\n",
    "\n",
    "\n",
    "# Link Kuramoto oscillators' input X to firing rates (drive)\n",
    "oscillators.X = linked_var(LPF, 'drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "81eb7f47-37de-4cc3-a039-9d520582ae4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add a few monitors\n",
    "mon_osc = StateMonitor(oscillators, 'Theta', True)\n",
    "mon_avg = StateMonitor(population, ['coherence', 'phase', 'rhythm', 'rhythm_rect'], record=True)\n",
    "\n",
    "mon_spikes_HH = SpikeMonitor(neurons_HH)\n",
    "mon_v_HH = StateMonitor(neurons_HH, ['I_ext', 'v'], record=True)\n",
    "\n",
    "mon_LPF_drive = StateMonitor(LPF, ['drive', 'Y'], record=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "effb2738-cdf2-43da-8eb1-a5140c2feebe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a network and add components\n",
    "net_kur = Network()\n",
    "\n",
    "# Oscillators\n",
    "net_kur.add(oscillators)\n",
    "net_kur.add(osc_synapses)\n",
    "net_kur.add(population)\n",
    "net_kur.add(average)\n",
    "\n",
    "# Neurons\n",
    "net_kur.add(neurons_HH)\n",
    "net_kur.add(LPF)\n",
    "net_kur.add(S_LPF)\n",
    "\n",
    "# Monitors\n",
    "net_kur.add(mon_osc)\n",
    "net_kur.add(mon_avg)\n",
    "net_kur.add(mon_spikes_HH)\n",
    "net_kur.add(mon_v_HH)\n",
    "net_kur.add(mon_LPF_drive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f3bffb50-c849-4202-a32f-984c5ed20ce9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING    Came across an abstract code block that may not be well-defined: the outcome may depend on the order of execution. You can ignore this warning if you are sure that the order of operations does not matter. Abstract code: \"ThetaPreInput_post = Theta_pre\"\n",
      " [brian2.codegen.generators.base]\n",
      "WARNING    Came across an abstract code block that may not be well-defined: the outcome may depend on the order of execution. You can ignore this warning if you are sure that the order of operations does not matter. Abstract code: \"ThetaPreInput_post = Theta_pre\"\n",
      " [brian2.codegen.generators.base]\n",
      "WARNING    Came across an abstract code block that may not be well-defined: the outcome may depend on the order of execution. You can ignore this warning if you are sure that the order of operations does not matter. Abstract code: \"ThetaPreInput_post = Theta_pre\"\n",
      " [brian2.codegen.generators.base]\n"
     ]
    }
   ],
   "source": [
    "# Run for a while\n",
    "neurons_HH.I_ext = 0*nA\n",
    "net_kur.run(500*ms)\n",
    "neurons_HH.I_ext = 5*nA\n",
    "net_kur.run(10*ms)\n",
    "neurons_HH.I_ext = 0*nA\n",
    "net_kur.run(duration-510*ms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b1f608fc-6669-43cc-97b0-55d91144ee21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Time (ms)')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot stuff\n",
    "raster_fig, raster_ax = plt.subplots(nrows=5, ncols=1)\n",
    "raster_fig.set_figheight(8)\n",
    "raster_fig.set_figwidth(16)\n",
    "\n",
    "title('raster plot')\n",
    "raster_ax[0].plot(mon_spikes_HH.t/second, mon_spikes_HH.i, '.g', markersize=.5,alpha=0.5)\n",
    "raster_ax[0].set_xlim([0,1])\n",
    "raster_ax[0].set_ylabel('Neuron index')\n",
    "raster_ax[0].grid()\n",
    "\n",
    "raster_ax[1].plot(mon_v_HH.t/second, mon_v_HH.I_ext[0])\n",
    "#raster_ax[1].set_ylim([-1,1])\n",
    "raster_ax[1].set_ylabel('Ext. Input')\n",
    "raster_ax[1].grid()\n",
    "\n",
    "raster_ax[2].plot(mon_LPF_drive.t/second, mon_LPF_drive.drive[0], label='LPF Output')\n",
    "raster_ax[2].set_ylabel('Population Firing Rates')\n",
    "raster_ax[2].legend()\n",
    "raster_ax[2].grid()\n",
    "\n",
    "raster_ax[3].plot(mon_avg.t/second, mon_avg.coherence[0], '-', label='Order Param')\n",
    "raster_ax[3].set_ylabel('Order Parameter')\n",
    "raster_ax[3].legend()\n",
    "raster_ax[3].grid()\n",
    "\n",
    "raster_ax[4].plot(mon_avg.t/second, mon_avg.rhythm_rect[0], '-', label='Theta Rhythm (Ensemble)')\n",
    "raster_ax[4].set_ylabel('Theta Rhythm (corr)')\n",
    "raster_ax[4].legend()\n",
    "raster_ax[4].grid()\n",
    "\n",
    "raster_ax[4].set_xlabel('Time (ms)')\n"
   ]
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}