{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "caac29dd",
   "metadata": {},
   "source": [
    "# Post-Inhibitory Rebound Bursting\n",
    "\n",
    "Thalamic relay neurons can fire a **rebound burst** after a period of hyperpolarization. The low-threshold T-type calcium current inactivates at rest, but a sustained hyperpolarizing input removes that inactivation. When the hyperpolarization ends, the T-current activates transiently and drives a brief burst of spikes — even though the net input is now zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cc8777b3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-25T10:28:38.913070Z",
     "iopub.status.busy": "2026-05-25T10:28:38.912917Z",
     "iopub.status.idle": "2026-05-25T10:28:42.131665Z",
     "shell.execute_reply": "2026-05-25T10:28:42.130563Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n"
     ]
    }
   ],
   "source": [
    "import brainstate\n",
    "import braintools\n",
    "import brainunit as u\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import braincell"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4956c7a",
   "metadata": {},
   "source": [
    "## A cell with a T-type calcium current\n",
    "\n",
    "The key ingredient is the low-threshold T-type calcium channel (`CaT_HM1992`); `HCN_HM1992` (the H-current) sets a realistic resting potential, and the high-threshold calcium current shapes the burst."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2980dd79",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-25T10:28:42.134635Z",
     "iopub.status.busy": "2026-05-25T10:28:42.134047Z",
     "iopub.status.idle": "2026-05-25T10:28:42.139950Z",
     "shell.execute_reply": "2026-05-25T10:28:42.138848Z"
    }
   },
   "outputs": [],
   "source": [
    "class ThalamicCell(braincell.SingleCompartment):\n",
    "    \"\"\"Thalamic-relay-style cell with a low-threshold T-type Ca current.\"\"\"\n",
    "    def __init__(self, size, solver='ind_exp_euler'):\n",
    "        super().__init__(size, V_initializer=braintools.init.Constant(-65. * u.mV),\n",
    "                         V_th=20. * u.mV, solver=solver)\n",
    "        self.na = braincell.ion.SodiumFixed(size, E=50. * u.mV)\n",
    "        self.na.add(INa=braincell.channel.Na_Ba2002(size, V_sh=-30 * u.mV))\n",
    "        self.k = braincell.ion.PotassiumFixed(size, E=-90. * u.mV)\n",
    "        self.k.add(IKL=braincell.channel.K_Leak(size, g_max=0.01 * (u.mS / u.cm ** 2)))\n",
    "        self.k.add(IDR=braincell.channel.KDR_Ba2002(size, V_sh=-30. * u.mV, q10=2.0, temp=u.celsius2kelvin(16.)))\n",
    "        self.ca = braincell.ion.CalciumDetailed(size, C_rest=5e-5 * u.mM,\n",
    "                                                tau=10. * u.ms, d=0.5 * u.um)\n",
    "        self.ca.add(ICaT=braincell.channel.CaT_HM1992(size, g_max=2.1 * (u.mS / u.cm ** 2)))\n",
    "        self.ca.add(ICaHT=braincell.channel.CaHT_HM1992(size, g_max=3.0 * (u.mS / u.cm ** 2)))\n",
    "        self.Ih = braincell.channel.HCN_HM1992(size, g_max=0.01 * (u.mS / u.cm ** 2), E=-43 * u.mV)\n",
    "        self.IL = braincell.channel.IL(size, g_max=0.0075 * (u.mS / u.cm ** 2), E=-70 * u.mV)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a16f6ab0",
   "metadata": {},
   "source": [
    "## Apply a hyperpolarizing step, then release\n",
    "\n",
    "We inject a hyperpolarizing current density of -2 uA/cm^2 for the first 200 ms, then switch to zero. The current is a function of time `t`, selected with `u.math.where`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "74490b08",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-25T10:28:42.143773Z",
     "iopub.status.busy": "2026-05-25T10:28:42.143579Z",
     "iopub.status.idle": "2026-05-25T10:28:45.974994Z",
     "shell.execute_reply": "2026-05-25T10:28:45.974254Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "spikes after release (ms): [266.5, 277.79998779296875, 282.6000061035156, 289.20001220703125, 300.20001220703125]\n"
     ]
    }
   ],
   "source": [
    "cell = ThalamicCell(1)\n",
    "cell.init_state()\n",
    "\n",
    "def I_of_t(t):\n",
    "    return u.math.where(t < 200. * u.ms,\n",
    "                        -2. * u.uA / u.cm ** 2,\n",
    "                        0. * u.uA / u.cm ** 2)\n",
    "\n",
    "def step(t):\n",
    "    with brainstate.environ.context(t=t):\n",
    "        cell.update(I_of_t(t))\n",
    "    return cell.V.value, cell.spike.value\n",
    "\n",
    "with brainstate.environ.context(dt=0.01 * u.ms):\n",
    "    times = u.math.arange(0. * u.ms, 500. * u.ms, brainstate.environ.get_dt())\n",
    "    vs, spikes = brainstate.transform.for_loop(step, times)\n",
    "\n",
    "spike_times = np.asarray(times[u.math.squeeze(spikes) > 0] / u.ms)\n",
    "rebound = spike_times[spike_times > 200.]\n",
    "print('spikes after release (ms):', np.round(rebound[:10], 1).tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2541f9b",
   "metadata": {},
   "source": [
    "## Visualize the rebound burst\n",
    "\n",
    "During the hyperpolarizing step the cell is silent and below rest; on release it overshoots and fires a short, high-frequency burst before settling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fa0fd7f4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-25T10:28:45.977219Z",
     "iopub.status.busy": "2026-05-25T10:28:45.976727Z",
     "iopub.status.idle": "2026-05-25T10:28:46.132794Z",
     "shell.execute_reply": "2026-05-25T10:28:46.130858Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 3))\n",
    "plt.plot(times / u.ms, u.math.squeeze(vs) / u.mV, linewidth=0.8)\n",
    "plt.axvline(200., color='k', linestyle=':', label='release')\n",
    "plt.xlabel('Time (ms)'); plt.ylabel('Membrane potential (mV)')\n",
    "plt.title('Post-inhibitory rebound burst (T-type Ca current)')\n",
    "plt.legend(); plt.tight_layout(); plt.show()"
   ]
  }
 ],
 "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": "3.13.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
