{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b7041049",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T08:12:09.211470Z",
     "iopub.status.busy": "2026-06-19T08:12:09.211320Z",
     "iopub.status.idle": "2026-06-19T08:12:14.439267Z",
     "shell.execute_reply": "2026-06-19T08:12:14.438333Z"
    },
    "tags": [
     "remove-cell"
    ]
   },
   "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": [
    "%matplotlib inline\n",
    "import brainmass\n",
    "import brainstate\n",
    "import brainunit as u\n",
    "import jax.numpy as jnp\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "brainstate.random.seed(0)\n",
    "brainstate.environ.set(dt=0.1 * u.ms)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5812e4e1",
   "metadata": {},
   "source": [
    "# Montbrio-Pazo-Roxin Model\n",
    "\n",
    "The **Montbrio-Pazo-Roxin (MPR) model** is an *exact* mean-field reduction of an all-to-all network of quadratic integrate-and-fire (QIF) neurons, derived via the Ott-Antonsen ansatz. The two macroscopic variables are the population firing rate $r$ and the mean membrane potential $v$:\n",
    "\n",
    "$$\\tau\\dot r = \\tfrac{\\Delta}{\\pi\\tau} + 2 r v,\\qquad \\tau\\dot v = v^2 + \\eta + J\\tau r - (\\pi\\tau r)^2.$$\n",
    "\n",
    "Unlike heuristic rate models, MPR exactly tracks the spiking network and supports next-generation neural-mass whole-brain modeling.\n",
    "\n",
    "**Reference:** Montbrio, Pazo & Roxin (2015), *Macroscopic description for networks of spiking neurons*, Physical Review X 5:021028."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3959da7",
   "metadata": {},
   "source": [
    "## Build the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e7820c68",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T08:12:14.442246Z",
     "iopub.status.busy": "2026-06-19T08:12:14.441763Z",
     "iopub.status.idle": "2026-06-19T08:12:14.483193Z",
     "shell.execute_reply": "2026-06-19T08:12:14.482080Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MontbrioPazoRoxinStep(\n",
       "  in_size=(1,),\n",
       "  out_size=(1,),\n",
       "  tau=Const(\n",
       "    fit=False,\n",
       "    t=IdentityT(),\n",
       "    reg=None,\n",
       "    val=Quantity(1., \"ms\")\n",
       "  ),\n",
       "  eta=Const(\n",
       "    fit=False,\n",
       "    t=IdentityT(),\n",
       "    reg=None,\n",
       "    val=Array(-5., dtype=float32)\n",
       "  ),\n",
       "  delta=Const(\n",
       "    fit=False,\n",
       "    t=IdentityT(),\n",
       "    reg=None,\n",
       "    val=Quantity(1., \"Hz\")\n",
       "  ),\n",
       "  J=Const(\n",
       "    fit=False,\n",
       "    t=IdentityT(),\n",
       "    reg=None,\n",
       "    val=Array(15., dtype=float32)\n",
       "  ),\n",
       "  init_r=Uniform(low=0 Hz, high=0.05 Hz),\n",
       "  init_v=Uniform(low=0, high=0.05),\n",
       "  method=exp_euler\n",
       ")"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "node = brainmass.MontbrioPazoRoxinStep(in_size=1, eta=-5.0, J=15.0)\n",
    "node"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68fd78bf",
   "metadata": {},
   "source": [
    "## Run a simulation\n",
    "\n",
    "With a transient depolarizing drive `v_inp` the population emits a burst that the firing rate `r` tracks exactly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0da970ca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T08:12:14.485593Z",
     "iopub.status.busy": "2026-06-19T08:12:14.485410Z",
     "iopub.status.idle": "2026-06-19T08:12:14.816201Z",
     "shell.execute_reply": "2026-06-19T08:12:14.815165Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((4000, 1), Unit(\"Hz\"))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim = brainmass.Simulator(node, dt=0.01 * u.ms)\n",
    "res = sim.run(40. * u.ms, inputs=lambda i, t: (None, 3.0),\n",
    "              monitors=['r', 'v'])\n",
    "res['r'].shape, u.get_unit(res['r'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b53b0964",
   "metadata": {},
   "source": [
    "## Visualize\n",
    "\n",
    "The firing rate is unit-aware (`Hz`); `viz` strips units automatically. The phase portrait shows the $(r, v)$ trajectory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7b262105",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T08:12:14.818475Z",
     "iopub.status.busy": "2026-06-19T08:12:14.818174Z",
     "iopub.status.idle": "2026-06-19T08:12:15.006811Z",
     "shell.execute_reply": "2026-06-19T08:12:15.005896Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n",
    "brainmass.viz.plot_timeseries(res['r'], ts=res['ts'], ax=axes[0])\n",
    "axes[0].set_title('MPR firing rate r (Hz)')\n",
    "brainmass.viz.plot_phase_portrait(res['r'], res['v'], ax=axes[1])\n",
    "axes[1].set_xlabel('r (Hz)'); axes[1].set_ylabel('v')\n",
    "axes[1].set_title('Phase portrait')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be397178",
   "metadata": {},
   "source": [
    "## Try it: vary the excitability `eta`\n",
    "\n",
    "The mean external current `eta` moves the population between a low-rate resting state and a high-rate active state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "146b9b7e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T08:12:15.009506Z",
     "iopub.status.busy": "2026-06-19T08:12:15.009196Z",
     "iopub.status.idle": "2026-06-19T08:12:15.371654Z",
     "shell.execute_reply": "2026-06-19T08:12:15.370324Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "for eta in [-5.0, -3.0, 0.0]:\n",
    "    m = brainmass.MontbrioPazoRoxinStep(in_size=1, eta=eta, J=15.0)\n",
    "    r = brainmass.Simulator(m, dt=0.01 * u.ms).run(\n",
    "        40. * u.ms, inputs=lambda i, t: (None, 3.0), monitors=['r'])\n",
    "    ax.plot(u.get_magnitude(r['ts']), u.get_magnitude(r['r'])[:, 0], label=f'eta = {eta}')\n",
    "ax.set_xlabel('time (ms)'); ax.set_ylabel('r (Hz)'); ax.legend()\n",
    "ax.set_title('Excitability sweep')\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.13.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}