{
 "cells": [
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    "tags": [
     "remove-cell"
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     "text": [
      "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n"
     ]
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   "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": "8af47cb9",
   "metadata": {},
   "source": [
    "# Coupling and Delays\n",
    "\n",
    "A whole-brain model is more than a collection of independent regions — it is a\n",
    "*network*. This page explains the three ingredients that wire regions together:\n",
    "the **structural connectivity** (who connects to whom), the **conduction delays**\n",
    "(how long a signal takes to travel), and the **coupling function** (how a\n",
    "neighbour's activity enters a region's dynamics). It covers the maths and the\n",
    "intuition; the hands-on recipe is {doc}`/howto/custom_coupling`, and the API is\n",
    "``brainmass.Network`` and the ``*Coupling`` classes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5cdc87b",
   "metadata": {},
   "source": [
    "## Structural connectivity (SC)\n",
    "\n",
    "The wiring diagram of a brain network is a **structural connectivity matrix**\n",
    "$W \\in \\mathbb{R}^{N \\times N}$ for $N$ regions. Each entry $W_{ij}$ is the strength\n",
    "of the anatomical connection from region $j$ to region $i$, typically estimated\n",
    "from diffusion-MRI **tractography** (streamline counts between parcels) or taken\n",
    "from a tract-tracing atlas.\n",
    "\n",
    "Conventions that matter in practice:\n",
    "\n",
    "- **No self-connections.** The diagonal $W_{ii}$ is usually zeroed — a region's\n",
    "  recurrent dynamics live *inside* its own neural mass model, not in the SC.\n",
    "  ``brainmass.Network`` zeroes the diagonal for you (unless you opt in with\n",
    "  ``self_connection=True``).\n",
    "- **Weights are non-negative** and often normalised (e.g. to $[0,1]$, or row-/\n",
    "  Laplacian-normalised). A global scalar $k$ (TVB calls it $G$; ``brainmass`` keeps\n",
    "  the convention $G \\equiv k$) sets the overall coupling strength on top of $W$.\n",
    "- **Symmetry** is common for diffusion-MRI SC (tractography is undirected) but not\n",
    "  required; directed connectomes are supported.\n",
    "\n",
    "The bundled ``datasets.load_dataset('example_connectome')`` provides a small\n",
    "symmetric $8 \\times 8$ $W$ (plus a distance matrix) to experiment with."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e94f2ed7",
   "metadata": {},
   "source": [
    "## Conduction delays\n",
    "\n",
    "Axons conduct at a finite speed, so a signal leaving region $j$ arrives at region\n",
    "$i$ only after a **conduction delay**\n",
    "\n",
    "$$\n",
    "\\tau_{ij} \\;=\\; \\frac{d_{ij}}{v},\n",
    "$$\n",
    "\n",
    "where $d_{ij}$ is the fibre length between the regions (a **distance matrix**,\n",
    "often in mm) and $v$ is the conduction speed (mm/ms, typically a few m/s for\n",
    "myelinated white-matter tracts). Delays are essential: they are what let a network\n",
    "of identical oscillators produce travelling waves, metastable switching, and\n",
    "realistic resting-state dynamics rather than collapsing to global synchrony.\n",
    "\n",
    "With delays, the coupling that region $i$ feels at time $t$ reads the *past* state\n",
    "of its sources:\n",
    "\n",
    "$$\n",
    "\\text{input}_i(t) \\;\\propto\\; \\sum_j W_{ij}\\, x_j\\!\\left(t - \\tau_{ij}\\right).\n",
    "$$\n",
    "\n",
    "Numerically this needs a **delay buffer** — a ring buffer of recent history sized\n",
    "from $\\max_{ij}\\tau_{ij} / \\Delta t$ steps. ``brainmass`` builds this for you when\n",
    "you pass ``distance`` and ``speed`` to ``Network``; if either is omitted the\n",
    "coupling is **instantaneous** (zero delays). Units are handled automatically: if\n",
    "``distance`` carries length units and ``speed`` carries length/time units, the\n",
    "delay is unit-correct; plain numbers are assumed to be milliseconds. The\n",
    "self-delay (the diagonal of $\\tau$) is always zeroed.\n",
    "\n",
    "> One practical wrinkle: ``Network`` sizes its delay buffer from the global ``dt``\n",
    "> at *construction* time, so set ``brainstate.environ.set(dt=…)`` before building a\n",
    "> delay-coupled network. The ``Simulator`` then drives it with that same ``dt``."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d4686a2",
   "metadata": {},
   "source": [
    "## How activity enters a region: three coupling functions\n",
    "\n",
    "Given the (possibly delayed) source activity and the SC, a **coupling function**\n",
    "turns them into the current that drives each region. ``brainmass`` provides three\n",
    "families. The choice is a modelling decision with real dynamical consequences.\n",
    "\n",
    "### Diffusive coupling\n",
    "\n",
    "Diffusive coupling drives a region by the **difference** between its neighbours'\n",
    "activity and its own:\n",
    "\n",
    "$$\n",
    "\\text{current}_i \\;=\\; k \\sum_j W_{ij}\\,\\bigl(x_j^{(\\tau)} - y_i\\bigr),\n",
    "$$\n",
    "\n",
    "where $x_j^{(\\tau)}$ is the delayed source state of region $j$ and $y_i$ is the\n",
    "target region's own state. This is the discrete graph-**Laplacian** form: it is\n",
    "*relative*, vanishes when all regions agree ($x_j = y_i$), and tends to pull\n",
    "connected regions toward a common value — it is the natural model of an electrotonic\n",
    "/ gap-junction-like interaction and the most common choice for whole-brain models.\n",
    "It is ``brainmass``'s default (``coupling='diffusive'``)."
   ]
  },
  {
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   "execution_count": 2,
   "id": "5a120300",
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    "execution": {
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    {
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     "output_type": "stream",
     "text": [
      "diffusive current per region: [ 0.5 -0.5]\n"
     ]
    }
   ],
   "source": [
    "import brainmass\n",
    "import jax.numpy as jnp\n",
    "\n",
    "# Diffusive coupling current for a tiny 2-region example, by hand.\n",
    "W = jnp.array([[0.0, 1.0],\n",
    "               [1.0, 0.0]])      # symmetric, zero diagonal\n",
    "x = jnp.array([[1.0, 2.0],       # source-as-seen-by-target matrix (N_out, N_in)\n",
    "               [1.0, 2.0]])\n",
    "y = jnp.array([1.0, 2.0])        # each region's own state\n",
    "k = 0.5\n",
    "\n",
    "current = brainmass.diffusive_coupling(x, y, W, k)\n",
    "print('diffusive current per region:', current)   # k * sum_j W_ij (x_j - y_i)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25c44cff",
   "metadata": {},
   "source": [
    "### Additive (linear) coupling\n",
    "\n",
    "Additive coupling simply injects a **weighted sum** of the neighbours' activity,\n",
    "with no subtraction of the target's own state:\n",
    "\n",
    "$$\n",
    "\\text{current}_i \\;=\\; k \\sum_j W_{ij}\\,x_j^{(\\tau)} \\;+\\; b.\n",
    "$$\n",
    "\n",
    "This is *absolute* rather than relative: a region keeps receiving drive even when\n",
    "it already matches its neighbours, so additive coupling does **not** vanish at\n",
    "consensus. It corresponds to TVB's ``Linear`` coupling; the optional bias $b$\n",
    "shifts the operating point. Use it when a neighbour's activity should act as a\n",
    "direct synaptic input (e.g. excitatory drive) rather than a pull toward agreement.\n",
    "Select it with ``coupling='additive'``."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33a82f17",
   "metadata": {},
   "source": [
    "### Nonlinear coupling\n",
    "\n",
    "Real synaptic transmission **saturates**: a presynaptic rate maps to a bounded\n",
    "postsynaptic effect through a sigmoid. Nonlinear couplings apply a saturating\n",
    "function and split on *where* the nonlinearity sits.\n",
    "\n",
    "**Post-nonlinearity** (the function is applied to the *summed* network input):\n",
    "\n",
    "$$\n",
    "c_i \\;=\\; k\\,\\sigma\\!\\Bigl(\\text{slope}\\cdot\\bigl(a\\textstyle\\sum_j W_{ij}x_j + b - \\text{midpoint}\\bigr)\\Bigr)\n",
    "\\quad\\text{(sigmoidal)},\n",
    "\\qquad\n",
    "c_i \\;=\\; k\\,\\tanh\\!\\Bigl(\\text{scale}\\textstyle\\sum_j W_{ij}x_j\\Bigr)\n",
    "\\quad\\text{(hyperbolic tangent)}.\n",
    "$$\n",
    "\n",
    "**Pre-nonlinearity** (each *source* is passed through the nonlinearity *before*\n",
    "weighting — the Jansen–Rit convention):\n",
    "\n",
    "$$\n",
    "c_i \\;=\\; k\\sum_j W_{ij}\\,\\sigma_{\\mathrm{JR}}(x_j),\n",
    "\\qquad\n",
    "\\sigma_{\\mathrm{JR}}(x) = c_{\\min} + \\frac{c_{\\max}-c_{\\min}}{1 + e^{\\,r(\\text{midpoint}-x)}}.\n",
    "$$\n",
    "\n",
    "The post-/pre- distinction is the key design axis: post-forms saturate the *total*\n",
    "incoming drive (the output carries $k$'s unit); the pre-form saturates each source\n",
    "*firing rate* into a synaptic effect before summation. ``brainmass`` exposes these\n",
    "as ``coupling='sigmoidal' | 'tanh' | 'sigmoidal_jansen_rit'``, and the\n",
    "``tanh`` saturation is easy to see directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "adf22a33",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-06-19T07:56:54.493370Z"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input level  0.1: coupling current 0.1900  (saturates toward k=0.5)\n",
      "input level  1.0: coupling current 0.4997  (saturates toward k=0.5)\n",
      "input level  5.0: coupling current 0.5000  (saturates toward k=0.5)\n"
     ]
    }
   ],
   "source": [
    "import brainmass\n",
    "import jax.numpy as jnp\n",
    "\n",
    "W = jnp.ones((2, 2))\n",
    "k, scale = 0.5, 2.0\n",
    "\n",
    "# tanh coupling saturates to +/- k as the network input grows.\n",
    "for level in (0.1, 1.0, 5.0):\n",
    "    x = jnp.full((2, 2), level)\n",
    "    c = brainmass.hyperbolic_tangent_coupling(x, W, k=k, scale=scale)\n",
    "    print(f'input level {level:>4}: coupling current {c[0]:.4f}  (saturates toward k={k})')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84e96507",
   "metadata": {},
   "source": [
    "Notice how the current flattens toward $k = 0.5$ as the input grows — the bounded\n",
    "synaptic response that linear coupling lacks. (Through ``Network``, the nonlinear\n",
    "kernels use their default shape parameters; to tune ``slope`` / ``scale`` /\n",
    "``midpoint`` build the coupling class directly, e.g.\n",
    "``HyperbolicTangentCoupling(...)``.)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aedd91b",
   "metadata": {},
   "source": [
    "## A small illustrative network\n",
    "\n",
    "Putting it together: an 8-region network of Hopf oscillators, wired by the bundled\n",
    "example connectome, with distance-dependent conduction delays and diffusive\n",
    "coupling. This is the full **SC → coupling + delays → activity** pipeline in a few\n",
    "lines. The ``Simulator`` drives the ``Network`` exactly like a single node."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3a916469",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "network output (time x regions): (3000, 8)\n"
     ]
    }
   ],
   "source": [
    "import brainmass\n",
    "import brainstate\n",
    "import brainunit as u\n",
    "\n",
    "brainstate.random.seed(0)\n",
    "brainstate.environ.set(dt=0.1 * u.ms)   # set BEFORE building a delayed Network\n",
    "\n",
    "conn = brainmass.datasets.load_dataset('example_connectome')\n",
    "node = brainmass.HopfStep(in_size=conn.weights.shape[0], a=0.2, w=0.3)\n",
    "\n",
    "net = brainmass.Network(\n",
    "    node,\n",
    "    conn=conn.weights,            # structural connectivity W (diagonal zeroed)\n",
    "    distance=conn.distances,      # fibre lengths (mm)\n",
    "    speed=10.0 * u.mm / u.ms,     # conduction speed -> delays = distance / speed\n",
    "    coupling='diffusive',\n",
    "    coupled_var='x',              # coupling current drives the x variable\n",
    "    k=0.5,                        # global coupling strength (== TVB's G)\n",
    ")\n",
    "\n",
    "res = brainmass.Simulator(net, dt=0.1 * u.ms).run(\n",
    "    400 * u.ms, monitors=lambda m: m.node.x.value, transient=100 * u.ms)\n",
    "print('network output (time x regions):', res['output'].shape)"
   ]
  },
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",
      "text/plain": [
       "<Figure size 1100x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 4))\n",
    "\n",
    "# The structural connectivity that wired the network.\n",
    "brainmass.viz.plot_connectivity(conn.weights, labels=list(conn.labels), ax=ax1)\n",
    "ax1.set_title('Structural connectivity $W$')\n",
    "\n",
    "# The resulting coupled regional activity.\n",
    "brainmass.viz.plot_timeseries(res['output'], ts=res['ts'], ax=ax2)\n",
    "ax2.set_title('Delay-coupled regional activity')\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bafc8400",
   "metadata": {},
   "source": [
    "The left panel is the anatomy ($W$); the right is the dynamics it produces once the\n",
    "regions are coupled with conduction delays. Change $k$, the speed, or the coupling\n",
    "function and the collective behaviour — synchrony, metastability, travelling\n",
    "patterns — changes with it. Exploring that map from *wiring* to *dynamics* is much\n",
    "of what whole-brain modelling is about."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0477ea05",
   "metadata": {},
   "source": [
    "## Key takeaways\n",
    "\n",
    "- **Structural connectivity** $W$ is the wiring diagram; zero its diagonal and\n",
    "  scale it by a global strength $k$ ($\\equiv$ TVB's $G$).\n",
    "- **Conduction delays** $\\tau_{ij} = d_{ij}/v$ come from a distance matrix and a\n",
    "  finite speed; they are essential for realistic large-scale dynamics and need a\n",
    "  delay buffer (built automatically by ``Network``).\n",
    "- **Diffusive** coupling is *relative* (a Laplacian pull toward consensus),\n",
    "  **additive** is *absolute* (direct injected drive), and **nonlinear** couplings\n",
    "  add synaptic saturation, split into post- and pre-nonlinearity forms.\n",
    "\n",
    "## See also\n",
    "\n",
    "- {doc}`/howto/custom_coupling` — swapping coupling functions in practice.\n",
    "- {doc}`/tutorials/04_building_a_network` — building a connectome network step by step.\n",
    "- {doc}`/concepts/architecture_overview` — where ``Network`` sits in the package.\n",
    "- {doc}`/concepts/what_is_a_neural_mass_model` — the per-region nodes being coupled.\n",
    "\n",
    "## References\n",
    "\n",
    "- Sanz-Leon, P., Knock, S. A., Spiegler, A., & Jirsa, V. K. (2015). Mathematical\n",
    "  framework for large-scale brain network modeling in The Virtual Brain.\n",
    "  *NeuroImage*, 111, 385–430.\n",
    "- Deco, G., Jirsa, V., McIntosh, A. R., Sporns, O., & Kötter, R. (2009). Key role\n",
    "  of coupling, delay, and noise in resting brain fluctuations. *PNAS*, 106(25),\n",
    "  10302–10307.\n",
    "- Honey, C. J., et al. (2009). Predicting human resting-state functional\n",
    "  connectivity from structural connectivity. *PNAS*, 106(6), 2035–2040."
   ]
  }
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