{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "30330c3c",
   "metadata": {},
   "source": [
    "# Firing Rates and Population Synchrony\n",
    "\n",
    "This tutorial covers the population-level measures of `braintools.analysis`, applied to synthetic data whose ground truth is known. Spike-matrix measures live in `braintools.analysis.spike` and measures of continuous signals in `braintools.analysis.population`:\n",
    "\n",
    "| Function | Input | Output |\n",
    "|---|---|---|\n",
    "| `spike.firing_rate(spikes, width, dt)` | spike matrix `(T, N)` | population rate in Hz, smoothed with a box of width `width` |\n",
    "| `spike.cross_correlation(spikes, bin, dt)` | spike matrix | Wang-Buzsaki coherence $\\kappa$, averaged over pairs |\n",
    "| `population.voltage_fluctuation(potentials)` | membrane potentials `(T, N)` | Golomb synchrony $\\chi^2 \\in [0, 1]$ |\n",
    "| `population.functional_connectivity(activities)` | signals `(T, N)` | `(N, N)` Pearson correlation matrix |\n",
    "| `population.functional_connectivity_dynamics(activities, window_size, step_size)` | signals | correlation between sliding-window FC matrices |\n",
    "\n",
    "**Time units.** Pass times as `brainunit` quantities (`10 * u.ms`) and they are converted. Plain numbers follow each function's documented convention: `firing_rate` treats them as seconds, `cross_correlation` as milliseconds. Using quantities avoids having to remember which."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a130ca9c",
   "metadata": {},
   "source": [
    "## 1. Setup and Synthetic Populations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ad9e6747",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-15T08:09:40.024495Z",
     "iopub.status.busy": "2026-10-15T08:09:40.024495Z",
     "iopub.status.idle": "2026-10-15T08:09:41.562502Z",
     "shell.execute_reply": "2026-10-15T08:09:41.562502Z"
    }
   },
   "outputs": [],
   "source": [
    "import brainunit as u\n",
    "import jax.numpy as jnp\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import braintools.analysis as ba\n",
    "\n",
    "rng = np.random.default_rng(0)\n",
    "dt = 0.1 * u.ms\n",
    "T = 20_000  # 2 s\n",
    "t_ms = np.arange(T) * 0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c5f5b68",
   "metadata": {},
   "source": [
    "Three groups of 20 neurons, all firing at about 10 Hz on average:\n",
    "\n",
    "- **independent**: homogeneous Poisson trains;\n",
    "- **common input**: each neuron's rate is modulated by the same slow signal, so the trains are correlated on a 50 ms time scale but not precisely synchronized;\n",
    "- **synchronous events**: background Poisson spikes plus 20 population events in which each neuron fires with probability 0.5 within about 1 ms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "34405ef7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-15T08:09:41.563514Z",
     "iopub.status.busy": "2026-10-15T08:09:41.563514Z",
     "iopub.status.idle": "2026-10-15T08:09:41.577895Z",
     "shell.execute_reply": "2026-10-15T08:09:41.577895Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "independent   mean rate  10.4 Hz\n",
      "common input  mean rate   9.7 Hz\n",
      "synchronous   mean rate  10.4 Hz\n"
     ]
    }
   ],
   "source": [
    "def poisson(rate_hz, shape):\n",
    "    return (rng.random(shape) < rate_hz * 1e-4).astype(float)  # dt = 0.1 ms\n",
    "\n",
    "\n",
    "independent = poisson(10.0, (T, 20))\n",
    "\n",
    "shared = np.convolve(rng.normal(size=T), np.ones(500) / np.sqrt(500), mode=\"same\")  # ~50 ms correlated\n",
    "modulation = np.exp(1.2 * shared)\n",
    "rate = 10.0 * modulation / modulation.mean()  # mean exactly 10 Hz\n",
    "common = (rng.random((T, 20)) < (rate * 1e-4)[:, None]).astype(float)\n",
    "\n",
    "events = rng.choice(np.arange(500, T - 500), 20, replace=False)\n",
    "synchronous = poisson(5.0, (T, 20))\n",
    "for e in events:\n",
    "    fire = rng.random(20) < 0.5\n",
    "    synchronous[e + rng.integers(-10, 11, fire.sum()), np.flatnonzero(fire)] = 1\n",
    "\n",
    "spikes = np.concatenate([independent, common, synchronous], axis=1)\n",
    "groups = {\"independent\": slice(0, 20), \"common input\": slice(20, 40), \"synchronous\": slice(40, 60)}\n",
    "for name, g in groups.items():\n",
    "    print(f\"{name:13s} mean rate {spikes[:, g].sum() / 20 / 2.0:5.1f} Hz\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38bcd4f5",
   "metadata": {},
   "source": [
    "## 2. Raster Plot and Population Rate\n",
    "\n",
    "For a raster, `np.nonzero` on the spike matrix gives the time step and neuron index of every spike (the deprecated `braintools.metric.raster_plot` did the same; `braintools.spike.SpikeTrains` replaces it). `firing_rate` averages the spike matrix over neurons and smooths it with a box of width `width`, giving the population rate in Hz; a wider box trades temporal detail for less noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cb410b98",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-15T08:09:41.578898Z",
     "iopub.status.busy": "2026-10-15T08:09:41.578898Z",
     "iopub.status.idle": "2026-10-15T08:09:41.931067Z",
     "shell.execute_reply": "2026-10-15T08:09:41.931067Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1219 spikes, neuron indices 0..59\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1100x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "independent group: time-averaged rate 10.34 Hz (spike count gives 10.38 Hz)\n",
      "same result with seconds: 10.343284606933594\n"
     ]
    }
   ],
   "source": [
    "t_idx, idx = np.nonzero(spikes)  # time step and neuron of every spike\n",
    "times = t_ms[t_idx]\n",
    "print(f\"{len(times)} spikes, neuron indices {int(idx.min())}..{int(idx.max())}\")\n",
    "\n",
    "fig, axes = plt.subplots(2, 1, figsize=(11, 5), sharex=True)\n",
    "axes[0].plot(times, idx, \"|\", ms=3, color=\"k\")\n",
    "for name, g in groups.items():\n",
    "    axes[0].axhspan(g.start - 0.5, g.stop - 0.5, alpha=0.08)\n",
    "axes[0].set_ylabel(\"neuron\")\n",
    "for width in [2 * u.ms, 20 * u.ms]:\n",
    "    r = ba.spike.firing_rate(spikes[:, 40:60], width, dt)\n",
    "    axes[1].plot(t_ms, r, lw=0.8, label=f\"synchronous group, width {width}\")\n",
    "axes[1].set_ylabel(\"rate (Hz)\")\n",
    "axes[1].set_xlabel(\"time (ms)\")\n",
    "axes[1].legend(fontsize=8)\n",
    "fig.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "r = ba.spike.firing_rate(spikes[:, :20], 20 * u.ms, dt)\n",
    "print(f\"independent group: time-averaged rate {float(r.mean()):.2f} Hz (spike count gives \"\n",
    "      f\"{spikes[:, :20].sum() / 20 / 2.0:.2f} Hz)\")\n",
    "print(\"same result with seconds:\", float(ba.spike.firing_rate(spikes[:, :20], 0.02 * u.second, 1e-4 * u.second).mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6021b0c",
   "metadata": {},
   "source": [
    "## 3. Spike-Count Coherence $\\kappa$ (`cross_correlation`)\n",
    "\n",
    "The spike trains are cut into bins of width `bin`; $X_i(l) \\in \\{0, 1\\}$ marks whether neuron $i$ fired in bin $l$, and for each pair\n",
    "\n",
    "$$\\kappa_{ij} = \\frac{\\sum_l X_i(l) X_j(l)}{\\sqrt{\\sum_l X_i(l) \\sum_l X_j(l)}},$$\n",
    "\n",
    "averaged over all pairs (Wang & Buzsaki, 1996). $\\kappa$ is **not corrected for chance**: for independent trains, $\\kappa$ is roughly the probability that a bin contains a spike, so it grows with firing rate and bin width. Always compare against a chance level, for example obtained by circularly shifting each train by a random amount, which keeps rates and spike patterns but destroys their alignment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6b663062",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-15T08:09:41.932572Z",
     "iopub.status.busy": "2026-10-15T08:09:41.932572Z",
     "iopub.status.idle": "2026-10-15T08:09:42.771771Z",
     "shell.execute_reply": "2026-10-15T08:09:42.771771Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "group           kappa  shuffled   (bin = 5 ms)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "independent     0.056     0.053\n",
      "common input    0.103     0.043\n",
      "synchronous     0.272     0.045\n",
      "\n",
      "independent Poisson trains: kappa grows with rate\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    5 Hz: kappa 0.023, P(spike in a 5 ms bin) = 0.025\n",
      "   20 Hz: kappa 0.095, P(spike in a 5 ms bin) = 0.095\n",
      "   80 Hz: kappa 0.330, P(spike in a 5 ms bin) = 0.330\n"
     ]
    }
   ],
   "source": [
    "def shuffled(s):\n",
    "    return np.stack([np.roll(s[:, i], rng.integers(T)) for i in range(s.shape[1])], axis=1)\n",
    "\n",
    "\n",
    "print(f\"{'group':13s} {'kappa':>7s} {'shuffled':>9s}   (bin = 5 ms)\")\n",
    "for name, g in groups.items():\n",
    "    k = float(ba.spike.cross_correlation(spikes[:, g], 5 * u.ms, dt))\n",
    "    k0 = float(ba.spike.cross_correlation(shuffled(spikes[:, g]), 5 * u.ms, dt))\n",
    "    print(f\"{name:13s} {k:7.3f} {k0:9.3f}\")\n",
    "\n",
    "print(\"\\nindependent Poisson trains: kappa grows with rate\")\n",
    "for rate_hz in [5, 20, 80]:\n",
    "    k = float(ba.spike.cross_correlation(poisson(rate_hz, (T, 20)), 5 * u.ms, dt))\n",
    "    print(f\"  {rate_hz:3d} Hz: kappa {k:.3f}, P(spike in a 5 ms bin) = {1 - np.exp(-rate_hz * 0.005):.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c5d2975",
   "metadata": {},
   "source": [
    "The synchronous group stands far above its shuffled level, and the common-input group is above it as well. The independent group is at chance, whose value depends on the rate. A raw $\\kappa$ is only interpretable next to its chance level.\n",
    "\n",
    "## 4. Membrane-Potential Synchrony $\\chi^2$ (`voltage_fluctuation`)\n",
    "\n",
    "For membrane potentials $V_i(t)$ and their population average $\\bar V(t)$, Golomb's measure is\n",
    "\n",
    "$$\\chi^2 = \\frac{\\operatorname{Var}_t[\\bar V]}{\\frac1N\\sum_i \\operatorname{Var}_t[V_i]} \\in [0, 1],$$\n",
    "\n",
    "equal to 1 for identical traces and about $1/N$ for independent ones. If every trace is a shared signal with variance fraction $c$ plus independent noise, $\\chi^2 \\approx c + (1 - c)/N$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "93847869",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-15T08:09:42.772774Z",
     "iopub.status.busy": "2026-10-15T08:09:42.772774Z",
     "iopub.status.idle": "2026-10-15T08:09:43.180681Z",
     "shell.execute_reply": "2026-10-15T08:09:43.180681Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " shared fraction c   chi^2  c + (1-c)/N\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               0.0   0.025        0.025\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               0.2   0.215        0.220\n",
      "               0.5   0.508        0.512\n",
      "               0.9   0.901        0.902\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               1.0   1.000        1.000\n"
     ]
    }
   ],
   "source": [
    "N = 40\n",
    "n_t = 5000\n",
    "common_v = rng.normal(size=n_t)\n",
    "print(f\"{'shared fraction c':>18s} {'chi^2':>7s} {'c + (1-c)/N':>12s}\")\n",
    "for c in [0.0, 0.2, 0.5, 0.9, 1.0]:\n",
    "    V = np.sqrt(c) * common_v[:, None] + np.sqrt(1 - c) * rng.normal(size=(n_t, N))\n",
    "    print(f\"{c:18.1f} {float(ba.population.voltage_fluctuation(V)):7.3f} {c + (1 - c) / N:12.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02fce3da",
   "metadata": {},
   "source": [
    "## 5. Functional Connectivity\n",
    "\n",
    "`functional_connectivity` is the Pearson correlation matrix of the columns of its input (`np.corrcoef` of the transposed array). For spikes, bin them first; the bin width sets the time scale of the correlations you detect. With 50 ms bins both correlated groups stand out from the independent one, which sits near zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "42c1c6cb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-15T08:09:43.181684Z",
     "iopub.status.busy": "2026-10-15T08:09:43.181684Z",
     "iopub.status.idle": "2026-10-15T08:09:43.456329Z",
     "shell.execute_reply": "2026-10-15T08:09:43.456329Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "matches np.corrcoef: True\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 450x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "independent   mean within-group correlation +0.038\n",
      "common input  mean within-group correlation +0.259\n",
      "synchronous   mean within-group correlation +0.286\n"
     ]
    }
   ],
   "source": [
    "counts = spikes.reshape(-1, 500, 60).sum(axis=1)  # 50 ms bins\n",
    "fc = np.asarray(ba.population.functional_connectivity(counts))\n",
    "print(\"matches np.corrcoef:\", np.allclose(fc, np.corrcoef(counts.T), atol=1e-5))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(4.5, 4))\n",
    "im = ax.imshow(fc, cmap=\"RdBu_r\", vmin=-1, vmax=1)\n",
    "ax.set_title(\"FC of 50 ms spike counts\")\n",
    "fig.colorbar(im, ax=ax)\n",
    "plt.show()\n",
    "\n",
    "off = ~np.eye(20, dtype=bool)\n",
    "for name, g in groups.items():\n",
    "    print(f\"{name:13s} mean within-group correlation {fc[g, g][off].mean():+.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbe7513c",
   "metadata": {},
   "source": [
    "`functional_connectivity_dynamics` slides a window along the signals, computes an FC matrix in each window, and correlates the FC matrices with each other. Blocks in the resulting matrix are periods with a stable connectivity pattern. Below, 8 signals switch halfway from one correlation structure (channels 0-3 coupled) to another (channels 4-7 coupled)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bb41bf31",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-15T08:09:43.457335Z",
     "iopub.status.busy": "2026-10-15T08:09:43.457335Z",
     "iopub.status.idle": "2026-10-15T08:09:44.607498Z",
     "shell.execute_reply": "2026-10-15T08:09:44.607498Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x350 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "77 windows; mean FCD within halves 0.96, between halves -0.28\n"
     ]
    }
   ],
   "source": [
    "n_t = 4000\n",
    "x = rng.normal(size=(n_t, 8))\n",
    "drive = rng.normal(size=n_t)\n",
    "x[: n_t // 2, :4] += 1.5 * drive[: n_t // 2, None]\n",
    "x[n_t // 2:, 4:] += 1.5 * drive[n_t // 2:, None]\n",
    "\n",
    "fcd = np.asarray(ba.population.functional_connectivity_dynamics(x, window_size=200, step_size=50))\n",
    "fig, ax = plt.subplots(figsize=(4, 3.5))\n",
    "im = ax.imshow(fcd, cmap=\"viridis\")\n",
    "ax.set_xlabel(\"window\")\n",
    "ax.set_ylabel(\"window\")\n",
    "fig.colorbar(im, ax=ax)\n",
    "plt.show()\n",
    "h = fcd.shape[0] // 2\n",
    "print(f\"{fcd.shape[0]} windows; mean FCD within halves {np.mean([fcd[:h - 2, :h - 2].mean(), fcd[h + 2:, h + 2:].mean()]):.2f}, \"\n",
    "      f\"between halves {fcd[:h - 2, h + 2:].mean():.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be45760d",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "- A raster (`np.nonzero` on the spike matrix) and `firing_rate` describe activity; pass times as quantities so `firing_rate` returns Hz unambiguously.\n",
    "- $\\kappa$ (`cross_correlation`) is not chance-corrected and grows with rate and bin width; compare with shuffled data.\n",
    "- `voltage_fluctuation` returns Golomb's $\\chi^2 \\in [0, 1]$: about $1/N$ for independent and 1 for identical traces.\n",
    "- `functional_connectivity` is the correlation matrix of binned activity; the bin width selects the time scale. `functional_connectivity_dynamics` shows when that structure changes."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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