{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "333ff534",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# _(Wang & Buzsáki, 1996)_ Gamma Oscillation\n",
    "\n",
    "[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/brainpy/examples/blob/main/oscillation_synchronization/Wang_1996_gamma_oscillation.ipynb)\n",
    "[![Open in Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/brainpy/examples/blob/main/oscillation_synchronization/Wang_1996_gamma_oscillation.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddf7a551",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Here we show the implementation of gamma oscillation proposed by Xiao-Jing Wang and György Buzsáki (1996). They demonstrated that the GABA$_A$ synaptic transmission provides a suitable mechanism for synchronized gamma oscillations in a network of fast-spiking interneurons. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "527b272f",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Let's first import brainpy and set profiles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9d2c247c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-22T03:54:16.787029600Z",
     "start_time": "2023-07-22T03:54:16.778174800Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import brainpy as bp\n",
    "import brainpy.math as bm\n",
    "\n",
    "bp.math.set_dt(0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adc251f7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-22T03:59:22.372542100Z",
     "start_time": "2023-07-22T03:59:22.358272300Z"
    },
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "The network is constructed with Hodgkin–Huxley (HH) type neurons and GABA$_A$ synapses. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c3c361d",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "The dynamics of the HH type neurons is given by:\n",
    "\n",
    "$$ C \\frac {dV} {dt} = -(I_{Na} + I_{K} + I_L) + I(t) $$\n",
    "\n",
    "where $I(t)$ is the injected current, the leak current $ I_L = g_L (V - E_L) $, and the transient sodium current \n",
    "\n",
    "$$ I_{Na} = g_{Na} m_{\\infty}^3 h (V - E_{Na}) $$\n",
    "\n",
    "where the activation variable $m$ is assumed fast and substituted by its steady-state function \n",
    "$m_{\\infty} = \\alpha_m / (\\alpha_m + \\beta_m)$.\n",
    "And the inactivation variable $h$ obeys a first=order kinetics:\n",
    "$$ \\frac {dh} {dt} = \\phi (\\alpha_h (1-h) - \\beta_h h)$$\n",
    "\n",
    "$$ I_K = g_K n^4 (V - E_K) $$\n",
    "\n",
    "where the activation variable $n$ also obeys a first=order kinetics:\n",
    "$$ \\frac {dn} {dt} = \\phi (\\alpha_n (1-n) - \\beta_n n)$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ce513907",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-22T03:54:16.818281300Z",
     "start_time": "2023-07-22T03:54:16.787029600Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "class HH(bp.dyn.NeuDyn):\n",
    "  def __init__(self, size, ENa=55., EK=-90., EL=-65, C=1.0, gNa=35.,\n",
    "               gK=9., gL=0.1, V_th=20., phi=5.0, method='exp_auto'):\n",
    "    super(HH, self).__init__(size=size)\n",
    "\n",
    "    # parameters\n",
    "    self.ENa = ENa\n",
    "    self.EK = EK\n",
    "    self.EL = EL\n",
    "    self.C = C\n",
    "    self.gNa = gNa\n",
    "    self.gK = gK\n",
    "    self.gL = gL\n",
    "    self.V_th = V_th\n",
    "    self.phi = phi\n",
    "\n",
    "    # variables\n",
    "    self.V = bm.Variable(bm.ones(size) * -65.)\n",
    "    self.h = bm.Variable(bm.ones(size) * 0.6)\n",
    "    self.n = bm.Variable(bm.ones(size) * 0.32)\n",
    "    self.spike = bm.Variable(bm.zeros(size, dtype=bool))\n",
    "    self.input = bm.Variable(bm.zeros(size))\n",
    "    self.t_last_spike = bm.Variable(bm.ones(size) * -1e7)\n",
    "\n",
    "    # integral\n",
    "    self.integral = bp.odeint(bp.JointEq([self.dV, self.dh, self.dn]), method=method)\n",
    "\n",
    "  def dh(self, h, t, V):\n",
    "    alpha = 0.07 * bm.exp(-(V + 58) / 20)\n",
    "    beta = 1 / (bm.exp(-0.1 * (V + 28)) + 1)\n",
    "    dhdt = alpha * (1 - h) - beta * h\n",
    "    return self.phi * dhdt\n",
    "\n",
    "  def dn(self, n, t, V):\n",
    "    alpha = -0.01 * (V + 34) / (bm.exp(-0.1 * (V + 34)) - 1)\n",
    "    beta = 0.125 * bm.exp(-(V + 44) / 80)\n",
    "    dndt = alpha * (1 - n) - beta * n\n",
    "    return self.phi * dndt\n",
    "\n",
    "  def dV(self, V, t, h, n, Iext):\n",
    "    m_alpha = -0.1 * (V + 35) / (bm.exp(-0.1 * (V + 35)) - 1)\n",
    "    m_beta = 4 * bm.exp(-(V + 60) / 18)\n",
    "    m = m_alpha / (m_alpha + m_beta)\n",
    "    INa = self.gNa * m ** 3 * h * (V - self.ENa)\n",
    "    IK = self.gK * n ** 4 * (V - self.EK)\n",
    "    IL = self.gL * (V - self.EL)\n",
    "    dVdt = (- INa - IK - IL + Iext) / self.C\n",
    "\n",
    "    return dVdt\n",
    "\n",
    "  def update(self):\n",
    "    tdi = bp.share.get_shargs()\n",
    "    V, h, n = self.integral(self.V, self.h, self.n, tdi.t, self.input, tdi.dt)\n",
    "    self.spike.value = bm.logical_and(self.V < self.V_th, V >= self.V_th)\n",
    "    self.t_last_spike.value = bm.where(self.spike, tdi.t, self.t_last_spike)\n",
    "    self.V.value = V\n",
    "    self.h.value = h\n",
    "    self.n.value = n\n",
    "    self.input[:] = 0."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35fb5f10",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Let's run a simulation of a network with 100 neurons with constant inputs (1 $\\mu$A/cm$^2$)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "01abdb0e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-22T03:54:17.132505600Z",
     "start_time": "2023-07-22T03:54:16.802686700Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f1ccf529d02943e7abdfe0a2b6d0a332",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\codes\\projects\\brainpy\\brainpy\\_src\\dynsys.py:248: UserWarning: \n",
      "From brainpy>=2.4.3, update() function no longer needs to receive a global shared argument.\n",
      "\n",
      "Instead of using:\n",
      "\n",
      "  def update(self, tdi, *args, **kwagrs):\n",
      "     t = tdi['t']\n",
      "     ...\n",
      "\n",
      "Please use:\n",
      "\n",
      "  def update(self, *args, **kwagrs):\n",
      "     t = bp.share['t']\n",
      "     ...\n",
      "\n",
      "  warnings.warn(_update_deprecate_msg, UserWarning)\n"
     ]
    }
   ],
   "source": [
    "num = 100\n",
    "neu = HH(num)\n",
    "neu.V[:] = -70. + bm.random.normal(size=num) * 20\n",
    "\n",
    "syn = bp.synapses.GABAa(pre=neu, post=neu, conn=bp.connect.All2All(include_self=False))\n",
    "syn.g_max = 0.1 / num\n",
    "\n",
    "net = bp.Network(neu=neu, syn=syn)\n",
    "runner = bp.DSRunner(net, monitors=['neu.spike', 'neu.V'], inputs=['neu.input', 1.])\n",
    "runner.run(duration=500.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ce41e1b2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-22T03:54:17.260163800Z",
     "start_time": "2023-07-22T03:54:17.135525800Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, gs = bp.visualize.get_figure(2, 1, 3, 8)\n",
    "\n",
    "fig.add_subplot(gs[0, 0])\n",
    "bp.visualize.line_plot(runner.mon.ts, runner.mon['neu.V'], ylabel='Membrane potential (N0)')\n",
    "\n",
    "fig.add_subplot(gs[1, 0])\n",
    "bp.visualize.raster_plot(runner.mon.ts, runner.mon['neu.spike'], show=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e32b8643",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We can see the result of this simulation that cells starting at random and asynchronous initial conditions quickly become synchronized and their spiking times are perfectly in-phase within 5-6 oscillatory cycles."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8073af28",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "**Reference**:\n",
    "\n",
    "- Wang, Xiao-Jing, and György Buzsáki. “Gamma oscillation by synaptic inhibition in a hippocampal interneuronal network model.” Journal of neuroscience 16.20 (1996): 6402-6413."
   ]
  }
 ],
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