{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e949aefa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T08:45:09.013170Z",
     "iopub.status.busy": "2026-06-19T08:45:09.013011Z",
     "iopub.status.idle": "2026-06-19T08:45:14.060482Z",
     "shell.execute_reply": "2026-06-19T08:45:14.059507Z"
    },
    "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": [
    "# Hidden setup cell (imports, deterministic seed, global dt).\n",
    "%matplotlib inline\n",
    "import brainmass\n",
    "import brainstate\n",
    "import braintools\n",
    "import brainunit as u\n",
    "import jax\n",
    "import jax.numpy as jnp\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "brainstate.random.seed(0)\n",
    "brainstate.environ.set(dt=0.1 * u.ms)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "242994a8",
   "metadata": {},
   "source": [
    "# Fitting an EEG Model with Gradients\n",
    "\n",
    "Whole-brain models have free parameters — coupling strengths, time constants, drive levels —\n",
    "that must be **fit** so the model reproduces a measured signal. Historically this meant a\n",
    "hand-rolled training loop copy-pasted into every script. This case study consolidates that\n",
    "pattern into the one {class}`~brainmass.Fitter` API: *write the objective once, swap optimizer\n",
    "backends.*\n",
    "\n",
    "We fit a {class}`~brainmass.JansenRitStep` cortical-column model — the canonical generator of\n",
    "EEG rhythms — so that the **oscillation amplitude** of its EEG output matches a target.\n",
    "\n",
    ":::{important}\n",
    "**Fit a scalar summary, not the raw waveform.** A gradient fitter that targets a raw\n",
    "oscillatory time series fails: the model and target drift out of phase, the point-by-point RMSE\n",
    "becomes flat/degenerate, and the gradient collapses to `nan`. The robust target is a **scalar\n",
    "feature** of the signal — here the settled EEG oscillation amplitude. (The same lesson holds for\n",
    "FC, spectral peak, or any phase-invariant summary.)\n",
    ":::\n",
    "\n",
    "**Source:** consolidates the hand-rolled `ModelFitting` EEG/MEG fitting scripts (Jansen-Rit\n",
    "column driven to a recorded target) into {class}`~brainmass.Fitter`. Self-contained: the\n",
    "\"recorded\" target is generated synthetically from a known ground-truth parameter, so the fit has\n",
    "a checkable answer and needs no downloads."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0265d920",
   "metadata": {},
   "source": [
    "## 1. The Jansen-Rit EEG model\n",
    "\n",
    "A {class}`~brainmass.JansenRitStep` is a three-population (pyramidal / excitatory / inhibitory)\n",
    "cortical column whose `eeg()` observable (`E − I`, in mV) oscillates in the alpha band under\n",
    "constant excitatory drive. The dimensionless **connectivity constant `C`** scales the\n",
    "intracortical excitatory/inhibitory loops and strongly controls the amplitude of the rhythm; we\n",
    "treat it as the unknown to recover. (We fit a dimensionless parameter so the loss stays a plain\n",
    "scalar — the same reason the gradient case studies fit `a` or `k`.)\n",
    "\n",
    "First, a quick look at the EEG the model produces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "abd4ea59",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T08:45:14.062913Z",
     "iopub.status.busy": "2026-06-19T08:45:14.062559Z",
     "iopub.status.idle": "2026-06-19T08:45:14.373895Z",
     "shell.execute_reply": "2026-06-19T08:45:14.372989Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 900x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def run_jr(C, *, duration=600.0 * u.ms, seed=0):\n",
    "    \"\"\"Simulate one Jansen-Rit column and return (time, EEG trace in mV).\"\"\"\n",
    "    brainstate.random.seed(seed)\n",
    "    col = brainmass.JansenRitStep(in_size=1, C=C)\n",
    "    sim = brainmass.Simulator(col, dt=0.1 * u.ms)\n",
    "    res = sim.run(\n",
    "        duration,\n",
    "        inputs=lambda i, t: (0. * u.mV, 220. * u.Hz, 0. * u.mV),  # constant excitatory drive\n",
    "        monitors=lambda m: m.eeg(),\n",
    "        transient=200.0 * u.ms,\n",
    "    )\n",
    "    return res['ts'], res['output']\n",
    "\n",
    "ts, eeg = run_jr(C=135.0)   # 135 is the canonical Jansen-Rit value\n",
    "fig, ax = plt.subplots(figsize=(9, 3.5))\n",
    "brainmass.viz.plot_timeseries(eeg, ts=ts, ax=ax)\n",
    "ax.set_title('Jansen-Rit EEG output (C = 135)')\n",
    "ax.set_ylabel('EEG (mV)')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "696a398e",
   "metadata": {},
   "source": [
    "## 2. The target and the scalar objective\n",
    "\n",
    "Our \"recording\" is a Jansen-Rit EEG generated with a **ground-truth** `C* = 150`. We reduce both\n",
    "prediction and target to a single phase-invariant feature — the **RMS amplitude** of the settled\n",
    "oscillation — and fit `C` to match it. Because the amplitude grows smoothly and monotonically\n",
    "with `C`, the loss surface is well conditioned for gradient descent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e5b69ea0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T08:45:14.376143Z",
     "iopub.status.busy": "2026-06-19T08:45:14.375894Z",
     "iopub.status.idle": "2026-06-19T08:45:15.408932Z",
     "shell.execute_reply": "2026-06-19T08:45:15.408190Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "target RMS amplitude (C*=150.0): 4.3312 mV\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def rms_amplitude(eeg_trace):\n",
    "    \"\"\"Phase-invariant scalar feature: RMS of the (de-meaned) EEG, in mV (dimensionless number).\"\"\"\n",
    "    x = u.get_magnitude(eeg_trace)\n",
    "    x = x - jnp.mean(x)\n",
    "    return jnp.sqrt(jnp.mean(x ** 2))\n",
    "\n",
    "C_TRUE = 150.0\n",
    "_, eeg_target = run_jr(C=C_TRUE, seed=7)\n",
    "target_amp = rms_amplitude(eeg_target)\n",
    "print(f\"target RMS amplitude (C*={C_TRUE}): {float(target_amp):.4f} mV\")\n",
    "\n",
    "# Amplitude grows smoothly with C -> a well-conditioned scalar loss surface.\n",
    "c_grid = np.linspace(110, 170, 7)\n",
    "amps = [float(rms_amplitude(run_jr(C=c)[1])) for c in c_grid]\n",
    "fig, ax = plt.subplots(figsize=(6, 3.5))\n",
    "ax.plot(c_grid, amps, 'o-')\n",
    "ax.axhline(float(target_amp), color='crimson', ls='--', label='target')\n",
    "ax.axvline(C_TRUE, color='grey', ls=':')\n",
    "ax.set_xlabel('C (connectivity constant)'); ax.set_ylabel('RMS EEG amplitude (mV)')\n",
    "ax.set_title('Scalar loss feature is smooth & monotone'); ax.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83091926",
   "metadata": {},
   "source": [
    "## 3. Fit with the gradient backend\n",
    "\n",
    "We mark `C` trainable with a {class}`~brainstate.nn.Param` and hand {class}`~brainmass.Fitter`\n",
    "a `loss_fn(model) -> (scalar_loss, aux)`. The loss simulates the column, reduces the EEG to its\n",
    "RMS amplitude, and squares the gap to the target — a scalar that brainmass differentiates\n",
    "straight through the simulation (backprop-through-the-solve). The default `backend='grad'` drives\n",
    "a `braintools.optim` Adam optimizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "52b0e483",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T08:45:15.410934Z",
     "iopub.status.busy": "2026-06-19T08:45:15.410702Z",
     "iopub.status.idle": "2026-06-19T08:45:15.925510Z",
     "shell.execute_reply": "2026-06-19T08:45:15.924598Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "true  C : 150.00\n",
      "fitted C: 151.51\n",
      "best loss: 2.347e-04\n"
     ]
    }
   ],
   "source": [
    "from brainstate.nn import Param\n",
    "\n",
    "# Trainable column: C starts away from the truth (150) at 115.\n",
    "brainstate.random.seed(0)\n",
    "column = brainmass.JansenRitStep(in_size=1, C=Param(115.0, fit=True))\n",
    "\n",
    "def loss_fn(model):\n",
    "    sim = brainmass.Simulator(model, dt=0.1 * u.ms)\n",
    "    res = sim.run(\n",
    "        600.0 * u.ms,\n",
    "        inputs=lambda i, t: (0. * u.mV, 220. * u.Hz, 0. * u.mV),\n",
    "        monitors=lambda m: m.eeg(),\n",
    "        transient=200.0 * u.ms,\n",
    "    )\n",
    "    amp = rms_amplitude(res['output'])\n",
    "    loss = (amp - target_amp) ** 2\n",
    "    return loss, amp\n",
    "\n",
    "fitter = brainmass.Fitter(\n",
    "    column,\n",
    "    braintools.optim.Adam(lr=2.0),   # C lives on a ~100 scale, so a larger lr\n",
    "    loss_fn=loss_fn,\n",
    "    backend='grad',\n",
    ")\n",
    "result = fitter.fit(n_steps=40, verbose=False)\n",
    "\n",
    "fitted_C = float(result.best_params['C'])\n",
    "print(f\"true  C : {C_TRUE:.2f}\")\n",
    "print(f\"fitted C: {fitted_C:.2f}\")\n",
    "print(f\"best loss: {result.best_loss:.3e}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e8a521f",
   "metadata": {},
   "source": [
    "## 4. Convergence and the recovered EEG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b4736a84",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T08:45:15.927600Z",
     "iopub.status.busy": "2026-06-19T08:45:15.927385Z",
     "iopub.status.idle": "2026-06-19T08:45:16.421543Z",
     "shell.execute_reply": "2026-06-19T08:45:16.419089Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 4))\n",
    "ax1.plot(result.history, marker='.')\n",
    "ax1.set_yscale('log'); ax1.set_xlabel('optimizer step'); ax1.set_ylabel('loss')\n",
    "ax1.set_title('Gradient-fit convergence'); ax1.grid(alpha=0.3)\n",
    "\n",
    "_, eeg_fit = run_jr(C=fitted_C, seed=7)\n",
    "brainmass.viz.plot_timeseries(eeg_target, ts=ts, ax=ax2, labels=['target (C*=150)'])\n",
    "brainmass.viz.plot_timeseries(eeg_fit, ts=ts, ax=ax2, labels=[f'fitted (C={fitted_C:.1f})'])\n",
    "ax2.set_title('Target vs fitted EEG'); ax2.set_ylabel('EEG (mV)'); ax2.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc450ee6",
   "metadata": {},
   "source": [
    "## 5. Swapping the backend (no objective rewrite)\n",
    "\n",
    "The payoff of the {class}`~brainmass.Fitter` API: the *same* objective runs under a\n",
    "derivative-free optimizer just by changing `backend`. Gradient-free search is the fallback when a\n",
    "loss is non-differentiable or rugged. Here we give the search an explicit box for `C` and let\n",
    "nevergrad's differential evolution find it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "692faa1e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T08:45:16.424346Z",
     "iopub.status.busy": "2026-06-19T08:45:16.424014Z",
     "iopub.status.idle": "2026-06-19T08:45:22.758817Z",
     "shell.execute_reply": "2026-06-19T08:45:22.757935Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gradient   : C = 151.51\n",
      "nevergrad  : C = 150.01\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    import nevergrad  # noqa: F401\n",
    "    has_ng = True\n",
    "except ImportError:\n",
    "    has_ng = False\n",
    "\n",
    "if has_ng:\n",
    "    brainstate.random.seed(0)\n",
    "    column_ng = brainmass.JansenRitStep(in_size=1, C=Param(115.0, fit=True))\n",
    "\n",
    "    def loss_fn_ng(model):\n",
    "        sim = brainmass.Simulator(model, dt=0.1 * u.ms)\n",
    "        res = sim.run(\n",
    "            600.0 * u.ms,\n",
    "            inputs=lambda i, t: (0. * u.mV, 220. * u.Hz, 0. * u.mV),\n",
    "            monitors=lambda m: m.eeg(), transient=200.0 * u.ms)\n",
    "        amp = rms_amplitude(res['output'])\n",
    "        return (amp - target_amp) ** 2, amp\n",
    "\n",
    "    fitter_ng = brainmass.Fitter(\n",
    "        column_ng, {'method': 'DE', 'n_sample': 6},\n",
    "        loss_fn=loss_fn_ng, backend='nevergrad',\n",
    "        search_space={'C': (110.0, 170.0)},\n",
    "    )\n",
    "    result_ng = fitter_ng.fit(n_steps=8, verbose=False)\n",
    "    print(f\"gradient   : C = {fitted_C:.2f}\")\n",
    "    print(f\"nevergrad  : C = {float(result_ng.best_params['C']):.2f}\")\n",
    "else:\n",
    "    print(\"nevergrad not installed; gradient backend gave C =\", f\"{fitted_C:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ed6c811",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "We fit a Jansen-Rit EEG model to a target by:\n",
    "\n",
    "1. picking a **scalar, phase-invariant feature** (RMS amplitude) of the EEG — *never* the raw\n",
    "   oscillatory waveform, which makes the gradient degenerate,\n",
    "2. wrapping the unknown connectivity constant `C` in a trainable {class}`~brainstate.nn.Param`\n",
    "   (a dimensionless knob, so the loss stays a plain scalar),\n",
    "3. handing one `loss_fn` to {class}`~brainmass.Fitter` with `backend='grad'`, which\n",
    "   backpropagates through the whole simulation, and\n",
    "4. recovering the ground-truth `C` and confirming the convergence curve.\n",
    "\n",
    "The same objective ran under a derivative-free backend with a one-line change — the consolidation\n",
    "that replaced the hand-rolled fitting scripts. For fitting a *network* to functional connectivity\n",
    "instead of a single column, swap the scalar feature for\n",
    "{func}`brainmass.objectives.fc_corr(as_loss=True) <brainmass.objectives.fc_corr>` and fit the\n",
    "coupling strength `k`."
   ]
  }
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