{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8ea29b8f",
   "metadata": {},
   "source": [
    "# How to choose a neuron model\n",
    "\n",
    "**Task.** Pick the right spiking neuron model for your network and see how a few\n",
    "of them respond to the same input.\n",
    "\n",
    "**Audience.** Simulation. Assumes you've done {doc}`../tutorials/01-first-neuron`.\n",
    "\n",
    "`brainpy.state` ships a family of point-neuron models, from the minimal\n",
    "integrate-and-fire to biophysical Hodgkin–Huxley. They all share the same\n",
    "interface — construct with a size and parameters, advance with a call, read\n",
    "spikes with `get_spike()` — so swapping one for another is a one-line change.\n",
    "This guide drives several under an identical current so you can compare their\n",
    "firing behavior, then points you to the full list in the API reference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "66c25924",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-17T09:07:28.141038Z",
     "iopub.status.busy": "2026-06-17T09:07:28.140766Z",
     "iopub.status.idle": "2026-06-17T09:07:32.644538Z",
     "shell.execute_reply": "2026-06-17T09:07:32.643058Z"
    }
   },
   "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 brainpy\n",
    "import brainstate\n",
    "import braintools\n",
    "import brainunit as u\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e131f43",
   "metadata": {},
   "source": [
    "## Drive several models with the same current\n",
    "\n",
    "We build one neuron of each kind, give them all the same step current, and\n",
    "record their membrane potentials. Each model has its own natural parameter\n",
    "units and scales, so we keep parameters close to each model's defaults."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c4d8a371",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-17T09:07:32.647784Z",
     "iopub.status.busy": "2026-06-17T09:07:32.647100Z",
     "iopub.status.idle": "2026-06-17T09:07:35.786508Z",
     "shell.execute_reply": "2026-06-17T09:07:35.785682Z"
    }
   },
   "outputs": [],
   "source": [
    "def run(neuron, current, t_stop=200. * u.ms):\n",
    "    brainstate.nn.init_all_states(neuron)\n",
    "\n",
    "    def step(t):\n",
    "        with brainstate.environ.context(t=t):\n",
    "            neuron(current)\n",
    "            return neuron.V.value\n",
    "\n",
    "    times = u.math.arange(0. * u.ms, t_stop, brainstate.environ.get_dt())\n",
    "    return times, brainstate.transform.for_loop(step, times)\n",
    "\n",
    "\n",
    "with brainstate.environ.context(dt=0.05 * u.ms):\n",
    "    models = {\n",
    "        'LIFRef': brainpy.state.LIFRef(\n",
    "            1, tau=20. * u.ms, tau_ref=5. * u.ms, V_rest=-60. * u.mV,\n",
    "            V_th=-50. * u.mV, V_reset=-60. * u.mV),\n",
    "        'ALIF': brainpy.state.ALIF(\n",
    "            1, tau=20. * u.ms, tau_a=200. * u.ms, V_rest=-60. * u.mV,\n",
    "            V_th=-50. * u.mV, V_reset=-60. * u.mV),\n",
    "        'ExpIF': brainpy.state.ExpIF(\n",
    "            1, tau=20. * u.ms, V_rest=-60. * u.mV, V_reset=-60. * u.mV),\n",
    "    }\n",
    "    traces = {name: run(m, 25. * u.mA) for name, m in models.items()}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00bad654",
   "metadata": {},
   "source": [
    "## Compare the responses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e1d5e610",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-17T09:07:35.789275Z",
     "iopub.status.busy": "2026-06-17T09:07:35.788850Z",
     "iopub.status.idle": "2026-06-17T09:07:35.981916Z",
     "shell.execute_reply": "2026-06-17T09:07:35.981034Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 700x420 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, gs = braintools.visualize.get_figure(len(traces), 1, 1.4, 7.0)\n",
    "for i, (name, (times, vs)) in enumerate(traces.items()):\n",
    "    ax = fig.add_subplot(gs[i, 0])\n",
    "    ax.plot(times.to_decimal(u.ms), vs.to_decimal(u.mV)[:, 0])\n",
    "    ax.set_ylabel(name + '\\nV (mV)')\n",
    "ax.set_xlabel('Time (ms)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e1480aa",
   "metadata": {},
   "source": [
    "### Choosing a model\n",
    "\n",
    "`LIFRef` (leaky integrate-and-fire with a refractory period) fires regularly;\n",
    "`ALIF` adds spike-frequency adaptation (the inter-spike interval grows over\n",
    "time); `ExpIF` has a sharper, exponential spike onset. Choose based on the\n",
    "phenomenon you need:\n",
    "\n",
    "- **Minimal cost:** `IF`, `LIF`, `LIFRef`.\n",
    "- **Adaptation:** `ALIF`, `Gif`, `AdExIF`, `AdQuaIF`.\n",
    "- **Realistic spike onset:** `ExpIF`, `AdExIF`, `QuaIF`.\n",
    "- **Rich single-neuron dynamics:** `Izhikevich`, `HindmarshRose`.\n",
    "- **Full biophysics:** `HH`, `WangBuzsakiHH`, `MorrisLecar`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9153e988",
   "metadata": {},
   "source": [
    "## Where to find every model\n",
    "\n",
    "This is only a sample. The authoritative directory — with every neuron's full\n",
    "signature, parameters, and equations — is the API reference:\n",
    "\n",
    "- {doc}`/apis/brainpy-neurons` — all BrainPy-style neuron models.\n",
    "\n",
    "## See also\n",
    "\n",
    "- {doc}`sim-coba-cuba-synapses` — connect your chosen neurons with synapses.\n",
    "- {doc}`/concepts/model-anatomy` — the shared `Dynamics → Neuron` interface.\n",
    "- {doc}`../tutorials/03-ei-balanced-network` — a full network built from LIFRef."
   ]
  }
 ],
 "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
}
