{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "64b6f08c-01f1-4eb0-aada-e352a3ecfcc4",
   "metadata": {},
   "source": [
    "# PHASE RESPONSE CA3\n",
    "\n",
    "We will test the phase response of neurons firing at a fixed rate. To test this, we will make a network of 100 HH neurons, which are going to be stimulated with a fixed input current, that forces them to produce spikes steadily. Then, we will stimulate them in different times and record the delays between the inter-spike intervals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "feb78354-4f3e-4920-9b08-b81fe2d1579b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import brian2\n",
    "from brian2 import *\n",
    "\n",
    "# HH equations\n",
    "# Pyramidal CAN\n",
    "py_CAN_inp_eqs = '''\n",
    "    dv/dt = (- I_CAN - I_M - I_leak - I_K - I_Na - I_Ca + I_exc + I_stim) / ((1.*ufarad*cm**-2) * (size)) + noise: volt\n",
    "    I_CAN = ((gCAN) * (size)) * mCAN**2 * (v + 20.*mV) : amp\n",
    "        dmCAN/dt = (mCANInf - mCAN) / mCANTau : 1\n",
    "            mCANInf = alpha2 / (alpha2 + (0.0002*ms**-1)) : 1\n",
    "            mCANTau = 1. / (alpha2 + (0.0002*ms**-1)) / (3.0**((36. - 22.) / 10.)) : second\n",
    "            alpha2 = (0.0002*ms**-1) * (Ca_i / (5e-4*mole*metre**-3))**2 : Hz\n",
    "    I_M = ((gM) * (size)) * p * (v - Ek) : amp\n",
    "        dp/dt = (pInf - p) / pTau : 1\n",
    "            pInf = 1. / (1. + exp(- (v + 35.*mV) / (10.*mV))) : 1\n",
    "            pTau = (1000.*ms) / (3.3 * exp((v + 35.*mV) / (20.*mV)) + exp(- (v + 35.*mV) / (20.*mV))) : second\n",
    "    I_leak = ((1e-5*siemens*cm**-2) * (size)) * (v - (-70.*mV)) : amp\n",
    "    I_K = ((5*msiemens*cm**-2) * (size)) * (n**4) * (v - Ek) : amp\n",
    "        dn/dt = alphan * (1 - n) - betan * n : 1\n",
    "            alphan = 0.032 * (mV**-1) * (5.*mV) / exprel(-(v + 40.*mV) / (5.*mV)) / ms : Hz\n",
    "            betan = 0.5 * exp(- (v + 45.*mV) / (40.*mV)) / ms : Hz\n",
    "    I_Na = ((50*msiemens*cm**-2) * (size)) * (m**3) * h * (v - 50.*mV) : amp\n",
    "        dm/dt = alpham * (1 - m) - betam * m : 1\n",
    "            alpham = 0.32 * (mV**-1) * (4.*mV) / exprel(-(v + 42.*mV) / (4.*mV)) / ms : Hz\n",
    "            betam = 0.28 * (mV**-1) * (5.*mV) / exprel( (v + 15.*mV) / (5.*mV)) / ms : Hz\n",
    "        dh/dt = alphah * (1 - h) - betah * h : 1\n",
    "            alphah = 0.128 * exp(- (v + 38.*mV) / (18.*mV)) / ms : Hz\n",
    "            betah = 4. / (1. + exp(- (v + 15.*mV) / (5.*mV))) / ms : Hz\n",
    "    I_Ca = ((1e-4 * siemens*cm**-2) * (size)) * (mCaL**2) * hCaL * (v - 120.*mV) : amp\n",
    "        dmCaL/dt = (alphamCaL * (1. - mCaL)) - (betamCaL * mCaL) : 1\n",
    "            alphamCaL = 0.055 * (mV**-1) * (3.8*mV) / exprel(-(v + 27.*mV) / (3.8*mV)) / ms : Hz\n",
    "            betamCaL = 0.94 * exp(-(v + 75.*mV) / (17.*mV)) / ms : Hz\n",
    "        dhCaL/dt = (alphahCaL * (1. - hCaL)) - (betahCaL * hCaL) : 1\n",
    "            alphahCaL = 0.000457 * exp(-(v + 13.*mV) / (50.*mV)) / ms : Hz\n",
    "            betahCaL = 0.0065 / (exp(-(v + 15.*mV) / (28.*mV)) + 1.) / ms : Hz\n",
    "        dCa_i/dt = driveChannel + ((2.4e-4*mole*metre**-3) - Ca_i) / (200.*ms) : mole*meter**-3\n",
    "            driveChannel = (-(1e4) * I_Ca / (cm**2)) / (2. * (96489*coulomb*mole**-1) * (1*umetre)) : mole*meter**-3*Hz\n",
    "\n",
    "    dCl/dt = - Cl / tau_Cl : 1\n",
    "    \n",
    "\n",
    "    noise = sigma_noise_exc * (2. * (0.1e-3*siemens) / (1.*ufarad))**.5 * randn() / sqrt(tstep) : volt/second (shared, constant over dt)\n",
    "\n",
    "\n",
    "    I_exc : amp\n",
    "    r : 1\n",
    "    I_stim = inputs_stim(t-i*10*tstep) : amp\n",
    "    size : metre**2 (shared)\n",
    "'''\n",
    "\n",
    "inh_inp_eqs = '''\n",
    "    dv/dt = ( - I_leak - I_K - I_Na - I_SynE - I_SynExt - I_SynHipp - I_SynI + I_exc + r*I_stim) / ((1.*ufarad*cm**-2) * (size)) + noise: volt\n",
    "    Vm = (- I_leak - I_K - I_Na) / ((1*ufarad*cm**-2) * (size))*tstep : volt\n",
    "    I_leak = ((0.1e-3*siemens*cm**-2) * (size)) * (v - (-65.*mV)) : amp\n",
    "    I_K = ((9e-3*siemens*cm**-2) * (size)) * (n**4) * (v - (-90.*mV)) : amp\n",
    "        dn/dt = (alphan * (1 - n) - betan * n) / 0.2: 1\n",
    "            alphan = 0.1 / exprel(-0.1*(mV**-1)*(v + 34.*mV)) /ms : Hz\n",
    "            betan = 0.125 * exp( - (v + 44.*mV) / (80.*mV)) / ms : Hz\n",
    "    I_Na = ((35e-3*siemens*cm**-2) * (size)) * (m**3) * h * (v - (55.*mV)) : amp\n",
    "        dm/dt = (alpham * (1 - m) - betam * m) / 0.2 : 1\n",
    "            alpham = 1. / exprel(-(v + 35.*mV) / (10.*mV)) / ms : Hz\n",
    "            betam = 4. * exp(- (v + 60.*mV) / (18.*mV)) / ms : Hz\n",
    "        dh/dt = (alphah * (1 - h) - betah * h) / 0.2 : 1\n",
    "            alphah = 0.07 * exp(- (v + 58.*mV) / (20.*mV)) / ms : Hz\n",
    "            betah = 1. / (exp((- 0.1 * (mV**-1)) * (v + 28.*mV)) + 1.) / ms : Hz\n",
    "    I_SynE = + ge * (v - 0.*mV) : amp\n",
    "        dge/dt = (-ge+he) * (1. / (0.3*ms)) : siemens\n",
    "        dhe/dt = -he/(5.*ms) : siemens\n",
    "    I_SynExt = + ge_ext * (v - 0.*mV) : amp\n",
    "        dge_ext/dt = (-ge_ext+he_ext) * (1. / (0.3*ms)) : siemens\n",
    "        dhe_ext/dt = -he_ext/(5.*ms) : siemens\n",
    "    I_SynHipp = + ge_hipp * (v - 0.*mV) : amp\n",
    "        dge_hipp/dt = (-ge_hipp+he_hipp) * (1. / (0.3*ms)) : siemens\n",
    "        dhe_hipp/dt = -he_hipp/(5.*ms) : siemens\n",
    "    I_SynI = + gi * (v - (-80.*mV)) : amp\n",
    "        dgi/dt = (-gi+hi) * (1. / (1.*ms)) : siemens\n",
    "        dhi/dt = -hi/(10.*ms) : siemens\n",
    "\n",
    "\n",
    "    noise = sigma_noise_inh * (2. * (0.1e-3*siemens ) / (1*ufarad))**.5 * randn() / sqrt(tstep) : volt/second (constant over dt)\n",
    "\n",
    "\n",
    "    I_exc : amp\n",
    "    r : 1\n",
    "    I_stim = inputs_stim(t-i*10*tstep) : amp\n",
    "    size : metre**2 (shared)\n",
    "'''\n",
    "\n",
    "\n",
    "# Spike and reset\n",
    "reset_eqs = '''\n",
    "    Cl = Cl + 0.2\n",
    "'''\n",
    "\n",
    "# Simulation\n",
    "duration = 2*second\n",
    "dt = 0.1*ms\n",
    "defaultclock.dt = dt\n",
    "\n",
    "\n",
    "# Model parameters\n",
    "tau_Cl = 0.1*second # removal rate of chloride ions in excitatory cells; healthy 0.1s\n",
    "Ek = -100.*mV # resting potential of potassium channels in excitatory cells; healthy -100mV\n",
    "gM = 90.*usiemens*cmetre**-2 # conductance for M-current\n",
    "gCAN = 0.25*usiemens*cmeter**-2 # wakefulness -> table 3.6, page 59, Aussel\n",
    "\n",
    "cell_size_py = 29.e3*umetre**2\n",
    "cell_size_inh = 14.e3*umetre**2\n",
    "\n",
    "sigma_noise_exc = 0* 100.*uvolt # NOISE OFF\n",
    "sigma_noise_inh = 0.*uvolt\n",
    "\n",
    "tstep = defaultclock.dt\n",
    "V_th = -20.*mvolt # spiking threshold\n",
    "refractory_time = 3.*ms # refractory time after a spike\n",
    "\n",
    "# Make a group of neurons\n",
    "G_E = NeuronGroup(N=100,\n",
    "    model=py_CAN_inp_eqs,\n",
    "    threshold='v>V_th',\n",
    "    reset=reset_eqs,\n",
    "    refractory=refractory_time,\n",
    "    method='exponential_euler',\n",
    "    name='E_pyCAN')\n",
    "G_E.size = cell_size_py\n",
    "G_E.v = -60.*mV\n",
    "G_E.I_exc = 0.5*nA\n",
    "G_E.r = 0\n",
    "G_E.r[0] = 0\n",
    "\n",
    "# Make a group of neurons\n",
    "G_I = NeuronGroup(N=100,\n",
    "    model=inh_inp_eqs,\n",
    "    threshold='v>V_th',\n",
    "    refractory=refractory_time,\n",
    "    method='exponential_euler',\n",
    "    name='EC_inh')\n",
    "G_I.size = cell_size_inh\n",
    "G_I.v = -60.*mV\n",
    "G_I.I_exc = 0.5*nA\n",
    "G_I.r = 0\n",
    "G_I.r[0] = 0\n",
    "\n",
    "# Make the input\n",
    "inputs_array = zeros((100,1))\n",
    "inputs_stim = TimedArray(([0.]*10 + [5.] + [0.])*nA, dt=10*tstep)\n",
    "\n",
    "# Create monitors\n",
    "Mv_E = StateMonitor(G_E, 'v', record=True)\n",
    "Mv_I = StateMonitor(G_I, 'v', record=True)\n",
    "\n",
    "Ms_E = SpikeMonitor(G_E)\n",
    "Ms_I = SpikeMonitor(G_I)\n",
    "\n",
    "# Make the network\n",
    "net = Network()\n",
    "net.add(G_E, G_I, Mv_E, Ms_E, Mv_I, Ms_I)\n",
    "\n",
    "# Test the E network\n",
    "G_E.I_exc = 0.5*nA\n",
    "G_I.I_exc = 0.5*nA\n",
    "net.run(2*second)\n",
    "\n",
    "G_E.I_exc = 0.*nA\n",
    "G_I.I_exc = 0.*nA\n",
    "net.run(1*second)\n",
    "\n",
    "G_E.I_exc = 0.5*nA\n",
    "G_I.I_exc = 0.5*nA\n",
    "net.run(2*second)\n",
    "\n",
    "# Plotting\n",
    "fig = figure(figsize=(16,8))\n",
    "plot(Ms_E.t/ms, Ms_E.i, '.C0', ms=3)\n",
    "plot(Ms_I.t/ms, Ms_I.i+100, '.C1', ms=3)\n",
    "ylim(0, 2*G_E.N)\n",
    "xlabel('Time (ms)')\n",
    "ylabel('Neuron index');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a20c2968-d0ca-4b58-92e7-adb69d67ac07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2, ax2 = plt.subplots(1, figsize=(12, 12))\n",
    "idx1 = 52\n",
    "idx2 = 55\n",
    "ax2.plot(Mv_E.t/ms, Mv_E.v[idx1]/mV, c='k', alpha=0.5)\n",
    "ax2.plot(Mv_E.t/ms, Mv_E.v[idx2]/mV, c='k', alpha=0.5)\n",
    "\n",
    "ax2.vlines(x=Ms_E.t[np.where(Ms_E.i==idx1)[0][10:11]]/ms, ymin=-80, ymax=50, colors='red', linestyles='--')\n",
    "ax2.vlines(x=Ms_E.t[np.where(Ms_E.i==idx1)[0][11:12]]/ms, ymin=-80, ymax=50, colors='red', linestyles='--')\n",
    "\n",
    "ax2.set_xlabel('Time (ms)')\n",
    "ax2.set_ylabel('Membrane voltage (mV)')\n",
    "\n",
    "ax2.set_ylim(-75, 50)\n",
    "ax2.set_xlim(350, 650)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b78a74c6-e075-48cc-9fee-46c8118b5fe2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx_E = np.where(Ms_E.i == 15)[0]\n",
    "tdiff_E = (Ms_E.t[idx_E[1:]] - Ms_E.t[idx_E[0:-1]])/ms\n",
    "tdiff_E[where(tdiff_E>75)] = 0\n",
    "tdiff_E = np.insert(tdiff_E, 0, 0)\n",
    "#print(Ms_E.t[idx])\n",
    "\n",
    "idx_I = np.where(Ms_I.i == 15)[0]\n",
    "tdiff_I = (Ms_I.t[idx_I[1:]] - Ms_I.t[idx_I[0:-1]])/ms\n",
    "tdiff_I[where(tdiff_I>75)] = 0\n",
    "tdiff_I = np.insert(tdiff_I, 0, 0)\n",
    "\n",
    "fig3,ax3 = plt.subplots(figsize=(8,5))\n",
    "ax3.plot(Ms_E.t[idx_E[1:]]/ms, tdiff_E[0:-1], 'C0o-', markevery=1, markersize=6.0, markerfacecolor='C0', mec='1.0', label='PyCAN')\n",
    "ax3.plot(Ms_I.t[idx_I[1:]]/ms, tdiff_I[0:-1], 'C1o-', markevery=5, markersize=6.0, markerfacecolor='C1', mec='1.0', label='Inh')\n",
    "ax3.set_title('ISI vs time')\n",
    "ax3.set_xlabel('Time [ms]')\n",
    "ax3.set_ylabel('Time difference [ms]')\n",
    "ax3.set_xlim(0,2000)\n",
    "\n",
    "ax3.legend(loc=5)\n",
    "ax3.grid(ls='-', linewidth=0.5, alpha=0.5)\n",
    "\n",
    "fig3.savefig('SpikeTimeDiff.png')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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
}