{ "cells": [ { "metadata": { "collapsed": true }, "cell_type": "markdown", "source": [ "# The Wilson-Cowan model\n", "\n", "The Wilson–Cowan model considers a homogeneous population of interconnected neurons of excitatory and inhibitory subtypes. All cells receive the same number of excitatory and inhibitory afferents, that is, all cells receive the same average excitation, $x(t)$. The target is to analyze the evolution in time of number of excitatory and inhibitory cells firing at time $t$,\n", "$E(t)$ and $I(t)$ respectively.\n" ], "id": "a4c4a59c9cb55b01" }, { "metadata": {}, "cell_type": "markdown", "source": [ "## Background / Theory\n", "\n", "### The Wilson-Cowan Model\n", "\n", "The **Wilson-Cowan model** (1972) is a foundational neural mass model describing the interaction between excitatory (E) and inhibitory (I) neural populations. It was one of the first models to capture emergent oscillatory dynamics from E/I balance.\n", "\n", "### The Equations\n", "\n", "$$\n", "\\begin{aligned}\n", "\\tau_E \\frac{dr_E}{dt} &= -r_E + (1 - r \\cdot r_E) \\cdot F_E(w_{EE} r_E - w_{EI} r_I + I_E) \\\\\n", "\\tau_I \\frac{dr_I}{dt} &= -r_I + (1 - r \\cdot r_I) \\cdot F_I(w_{IE} r_E - w_{II} r_I + I_I)\n", "\\end{aligned}\n", "$$\n", "\n", "where:\n", "- $r_E, r_I$: Excitatory and inhibitory population activities\n", "- $\\tau_E, \\tau_I$: Time constants\n", "- $w_{XY}$: Connection weights from population Y to X\n", "- $r$: Refractory parameter (limits maximum firing)\n", "- $I_E, I_I$: External inputs\n", "\n", "### The Sigmoid Transfer Function\n", "\n", "$$\n", "F(x; a, \\theta) = \\frac{1}{1 + e^{-a(x - \\theta)}} - \\frac{1}{1 + e^{a\\theta}}\n", "$$\n", "\n", "| Parameter | Role |\n", "|-----------|------|\n", "| $a$ | Gain (steepness of response) |\n", "| $\\theta$ | Threshold (input level for half-activation) |\n", "\n", "### Connection Weights\n", "\n", "| Weight | Meaning | Typical Effect |\n", "|--------|---------|----------------|\n", "| $w_{EE}$ | E→E recurrence | Positive feedback, bistability |\n", "| $w_{EI}$ | I→E inhibition | Negative feedback, stabilization |\n", "| $w_{IE}$ | E→I excitation | Drives inhibition |\n", "| $w_{II}$ | I→I recurrence | Self-inhibition |\n", "\n", "### Dynamical Regimes\n", "\n", "The Wilson-Cowan model exhibits rich dynamics:\n", "\n", "1. **Stable fixed points**: E/I balance at rest\n", "2. **Limit cycle oscillations**: Rhythmic E/I alternation\n", "3. **Bistability**: Multiple stable states\n", "4. **Excitable dynamics**: Transient responses to perturbations\n", "\n", "### Parameter \"Knobs\" - Biological Interpretation\n", "\n", "Each parameter in the Wilson-Cowan model corresponds to a biological or functional \"knob\" that can be tuned to produce different neural dynamics. Understanding these mappings is crucial for relating model behavior to neuroscience.\n", "\n", "\n", "| Parameter | Knob Name | Biological Correspondence | Effect Range |\n", "|-----------|-----------|---------------------------|--------------|\n", "| $\\tau_E, \\tau_I$ | Response Speed | Membrane time constant, ion channel kinetics | Fast (1ms) → Slow (10ms) |\n", "| $a_E, a_I$ | Neural Gain | Synaptic efficacy, neurotransmitter sensitivity | 0.5 (sluggish) → 2.0 (sensitive) |\n", "| $\\theta_E, \\theta_I$ | Activation Threshold | Firing threshold voltage | 1.0 (easy to activate) → 5.0 (hard to activate) |\n", "| $w_{EE}$ | Recurrent Excitation | AMPA/NMDA strength within E population | 0-20 |\n", "| $w_{EI}$ | Feedback Inhibition | GABAergic strength I→E | 0-20 |\n", "| $w_{IE}$ | Feedforward Excitation | Glutamatergic drive to inhibition | 0-10 |\n", "| $w_{II}$ | Recurrent Inhibition | GABAergic self-inhibition within I | 0-15 |\n", "| $r$ | Refractory Period | Sodium channel inactivation | 0 (none) → 2 (strong) |\n", "\n", "\n", "These combinations of parameters control higher-level system properties:\n", "\n", "**1. E/I Balance Knob** ($w_{EE} / w_{EI}$ ratio):\n", "- **High ratio (>1.0)**: Excitation-dominant system, risk of runaway activity (epileptiform)\n", "- **Balanced (~0.9)**: Stable limit-cycle oscillations (gamma rhythms)\n", "- **Low ratio (<0.8)**: Over-stabilized system, suppressed dynamics\n", "\n", "**2. Timescale Separation Knob** ($\\tau_I / \\tau_E$):\n", "- **$\\tau_I \\gg \\tau_E$**: Slow inhibition relative to excitation → beta/gamma rhythms\n", "- **$\\tau_I \\approx \\tau_E$**: Symmetric dynamics → alpha rhythms\n", "- **$\\tau_I \\ll \\tau_E$**: Fast inhibition → high-frequency filtering\n", "\n", "**3. Excitability Knob** ($a_E, \\theta_E$):\n", "- **High $a_E$, low $\\theta_E$**: Hyperexcitable regime (epilepsy models)\n", "- **Low $a_E$, high $\\theta_E$**: Hypoexcitable regime (anesthesia models)\n", "- **Intermediate**: Normal awake cortical states\n", "\n", "These \"knobs\" provide an intuitive interface for parameter exploration while maintaining biological interpretability.\n", "\n" ], "id": "bdd2213899c6efde" }, { "metadata": { "ExecuteTime": { "end_time": "2026-03-21T13:51:40.951899Z", "start_time": "2026-03-21T13:51:37.796304600Z" } }, "cell_type": "code", "source": [ "import brainmass\n", "import brainstate\n", "import braintools\n", "import brainunit as u\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "plt.rcParams['image.cmap'] = 'plasma'" ], "id": "15280ef24e6c2609", "outputs": [], "execution_count": 1 }, { "metadata": { "ExecuteTime": { "end_time": "2026-03-21T13:51:40.969992600Z", "start_time": "2026-03-21T13:51:40.952907500Z" } }, "cell_type": "code", "source": "brainstate.environ.set(dt=0.1 * u.ms)", "id": "490827454cb93b52", "outputs": [], "execution_count": 2 }, { "metadata": {}, "cell_type": "markdown", "source": [ "Environment:\n", "- `dt = 0.1 ms` sets the integration time step for all updates.\n", "- We will rely on `brainstate.transform.for_loop` to advance the model efficiently." ], "id": "explain-env-wc" }, { "metadata": {}, "cell_type": "markdown", "source": "## Single node simulation", "id": "4aeece38ee6642f2" }, { "metadata": {}, "cell_type": "markdown", "source": [ "We instantiate one Wilson–Cowan node with OU noise on both E and I. The `step_run(i)` helper advances the model one time step with a proper `(i, t)` context. The node returns the current excitatory rate `rE`." ], "id": "explain-single-node-wc" }, { "metadata": { "ExecuteTime": { "end_time": "2026-03-21T13:51:41.570322200Z", "start_time": "2026-03-21T13:51:40.970999Z" } }, "cell_type": "code", "source": [ "node = brainmass.WilsonCowanStep(\n", " 1,\n", " noise_E=brainmass.OUProcess(1, sigma=0.01, init=braintools.init.ZeroInit()),\n", " noise_I=brainmass.OUProcess(1, sigma=0.01, init=braintools.init.ZeroInit()),\n", ")\n", "node.init_all_states()\n", "\n", "\n", "def step_run(i):\n", " node.update(0.1)\n", " return node.rE.value, node.rI.value\n", "\n", "\n", "\n", "indices = np.arange(1000)\n", "exes, inhs = brainstate.transform.for_loop(step_run, indices)\n", "\n", "plt.plot(indices * brainstate.environ.get_dt(), exes, lw=2, label='Excitation')\n", "plt.plot(indices * brainstate.environ.get_dt(), inhs, lw=2, label='Inhibition')\n", "plt.title(\"Wilson-Cowan Oscillator Dynamics\")\n", "plt.legend()\n", "plt.xlabel(\"t [ms]\")\n", "plt.ylabel(\"Activity\")\n", "plt.show()" ], "id": "1dea6ca509deb85e", "outputs": [ { "data": { "text/plain": [ "
" ], "image/png": "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" }, "metadata": {}, "output_type": "display_data", "jetTransient": { "display_id": null } } ], "execution_count": 3 }, { "metadata": {}, "cell_type": "markdown", "source": "The trace shows `rE(t)` over time. With small noise and constant drive, the oscillator may settle to a fixed point or a limit cycle depending on parameters.", "id": "11dd84d6bd08b436" }, { "metadata": { "ExecuteTime": { "end_time": "2026-03-21T13:51:41.836138600Z", "start_time": "2026-03-21T13:51:41.615872600Z" } }, "cell_type": "code", "source": [ "node = brainmass.WilsonCowanStep(\n", " 1,\n", " wEE=10.,\n", " wEI=12.,\n", " wIE=8.,\n", " wII=3.0,\n", " a_E=0.2,\n", " a_I=4.0,\n", " rE_init=braintools.init.Constant(1.),\n", " rI_init=braintools.init.Constant(1.),\n", " # noise_E=brainmass.OUProcess(1, sigma=0.01, init=braintools.init.ZeroInit()),\n", " # noise_I=brainmass.OUProcess(1, sigma=0.01, init=braintools.init.ZeroInit()),\n", ")\n", "node.init_all_states()\n", "\n", "\n", "def step_run(i):\n", " node.update()\n", " return node.rE.value, node.rI.value\n", "\n", "\n", "indices = np.arange(1000)\n", "exes, inhs = brainstate.transform.for_loop(step_run, indices)\n", "\n", "plt.plot(indices * brainstate.environ.get_dt(), exes, lw=2, label='Excitation')\n", "plt.plot(indices * brainstate.environ.get_dt(), inhs, lw=2, label='Inhibition')\n", "plt.title(\"Wilson-Cowan Oscillator Dynamics\")\n", "plt.legend()\n", "plt.xlabel(\"t [ms]\")\n", "plt.ylabel(\"Activity\")\n", "plt.show()" ], "id": "5f55edd2d914781c", "outputs": [ { "data": { "text/plain": [ "
" ], "image/png": "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" }, "metadata": {}, "output_type": "display_data", "jetTransient": { "display_id": null } } ], "execution_count": 4 }, { "metadata": { "ExecuteTime": { "end_time": "2026-03-21T13:51:42.081260100Z", "start_time": "2026-03-21T13:51:41.846205300Z" } }, "cell_type": "code", "source": [ "node = brainmass.WilsonCowanStep(\n", " 1,\n", " wEE=20.,\n", " wEI=21.,\n", " wIE=16.,\n", " wII=6.,\n", " a_E=1.6,\n", " a_I=7.0,\n", " rE_init=braintools.init.Constant(1.),\n", " rI_init=braintools.init.Constant(1.),\n", " # noise_E=brainmass.OUProcess(1, sigma=0.01, init=braintools.init.ZeroInit()),\n", " # noise_I=brainmass.OUProcess(1, sigma=0.01, init=braintools.init.ZeroInit()),\n", ")\n", "node.init_all_states()\n", "\n", "\n", "def step_run(i):\n", " node.update()\n", " return node.rE.value, node.rI.value\n", "\n", "\n", "indices = np.arange(1000)\n", "exes, inhs = brainstate.transform.for_loop(step_run, indices)\n", "\n", "plt.plot(indices * brainstate.environ.get_dt(), exes, lw=2, label='Excitation')\n", "plt.plot(indices * brainstate.environ.get_dt(), inhs, lw=2, label='Inhibition')\n", "plt.title(\"Wilson-Cowan Oscillator Dynamics\")\n", "plt.legend()\n", "plt.xlabel(\"t [ms]\")\n", "plt.ylabel(\"Activity\")\n", "plt.show()" ], "id": "588ca9709b07660", "outputs": [ { "data": { "text/plain": [ "
" ], "image/png": "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" }, "metadata": {}, "output_type": "display_data", "jetTransient": { "display_id": null } } ], "execution_count": 5 }, { "metadata": {}, "cell_type": "markdown", "source": [ "## Bifurcation diagram\n", "\n", "Let's draw a simple one-dimensional bifurcation diagram of this model to orient ourselves in the parameter space.\n" ], "id": "a7ef35846f8cf513" }, { "metadata": { "ExecuteTime": { "end_time": "2026-03-21T13:51:42.095070900Z", "start_time": "2026-03-21T13:51:42.082577900Z" } }, "cell_type": "code", "source": [ "# these are the different input values that we want to scan\n", "exc_inputs = np.arange(0, 5.5, 0.05)" ], "id": "a873d22103044d93", "outputs": [], "execution_count": 6 }, { "metadata": {}, "cell_type": "markdown", "source": [ "We sweep a constant excitatory input across multiple independent nodes (one input per node). For each input value we simulate, then record the minimum and maximum of `rE` over time. Plotting min/max against input visualizes fixed points (lines collapsing) versus oscillatory regimes (min/max bands).\n", "\n", "Notes:\n", "- Increase simulation duration to better capture oscillation envelopes.\n", "- You can reduce `exc_inputs` resolution for speed during exploration." ], "id": "a1acbc6aacd6cb9a" }, { "metadata": { "ExecuteTime": { "end_time": "2026-03-21T13:51:42.305202800Z", "start_time": "2026-03-21T13:51:42.098781200Z" } }, "cell_type": "code", "source": [ "nodes = brainmass.WilsonCowanStep(exc_inputs.size)\n", "nodes.init_all_states()\n", "\n", "\n", "def step_run(i):\n", " return nodes.update(exc_inputs)\n", "\n", "\n", "indices = np.arange(10000)\n", "exec_activity = brainstate.transform.for_loop(step_run, indices)" ], "id": "889a2c773b338ae3", "outputs": [], "execution_count": 7 }, { "metadata": { "ExecuteTime": { "end_time": "2026-03-21T13:51:42.420575200Z", "start_time": "2026-03-21T13:51:42.309203900Z" } }, "cell_type": "code", "source": [ "max_exc = exec_activity.max(axis=0)\n", "min_exc = exec_activity.min(axis=0)" ], "id": "9fcf66400bde2973", "outputs": [], "execution_count": 8 }, { "metadata": {}, "cell_type": "markdown", "source": [ "The resulting diagram shows how steady‑state activity or oscillation amplitude varies with the external excitatory drive." ], "id": "explain-bif-plot-wc" }, { "metadata": { "ExecuteTime": { "end_time": "2026-03-21T13:51:42.531018700Z", "start_time": "2026-03-21T13:51:42.429575900Z" } }, "cell_type": "code", "source": [ "plt.plot(exc_inputs, max_exc, c='k', lw=2)\n", "plt.plot(exc_inputs, min_exc, c='k', lw=2)\n", "plt.title(\"Bifurcation diagram of the Wilson-Cowan model\")\n", "plt.xlabel(\"Input to exc\")\n", "plt.ylabel(\"Min / max exc\")\n", "plt.show()" ], "id": "9e7992338c7bb08b", "outputs": [ { "data": { "text/plain": [ "
" ], "image/png": "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" }, "metadata": {}, "output_type": "display_data", "jetTransient": { "display_id": null } } ], "execution_count": 9 }, { "metadata": {}, "cell_type": "markdown", "source": [ "## Input-Driven Transient Response\n", "\n", "Neural systems must respond to external stimuli. The **transient response** characterizes how the system reacts to inputs of different profiles:\n", "\n", "1. **Pulse Input**: Brief stimulation (e.g., sensory flash) → tests **excitability**\n", "2. **Step Input**: Sustained drive onset (e.g., attention signal) → tests **adaptation**\n", "3. **Ramp Input**: Gradually increasing signal (e.g., accumulating evidence) → tests **tracking**\n", "\n", "These response patterns reveal:\n", "- **Latency**: How quickly the system responds\n", "- **Gain**: Amplitude of response relative to input\n", "- **Damping**: Whether oscillations persist or decay\n", "- **Steady-state error**: Difference between input and equilibrium response" ], "id": "cccad6e1c759f0b6" }, { "metadata": { "ExecuteTime": { "end_time": "2026-03-21T13:51:43.154932500Z", "start_time": "2026-03-21T13:51:42.532363300Z" } }, "cell_type": "code", "source": [ "# Define input functions\n", "def pulse_input(t):\n", " \"\"\"Brief 10ms pulse at t=100ms\"\"\"\n", " return u.math.where(\n", " u.math.logical_and(t > 100. * u.ms, t < 110. * u.ms),\n", " 2.0,\n", " 0.0\n", " )\n", "\n", "\n", "def step_input(t):\n", " \"\"\"Step increase at t=200ms\"\"\"\n", " return u.math.where(\n", " t > 200. * u.ms,\n", " 1.0,\n", " 0.0,\n", " )\n", "\n", "\n", "def ramp_input(t):\n", " \"\"\"Ramp from 0 to 2.0 over 500ms\"\"\"\n", " return u.math.where(\n", " t > 0. * u.ms,\n", " u.math.minimum(t / (500. * u.ms), 2.0),\n", " 0.0\n", " )\n", "\n", "\n", "# Input types to test\n", "input_functions = [pulse_input, step_input, ramp_input]\n", "input_names = ['Pulse (10ms)', 'Step (at 200ms)', 'Ramp (500ms)']\n", "input_colors = ['purple', 'orange', 'green']\n", "\n", "# Simulate each input type\n", "responses_E = []\n", "responses_I = []\n", "input_traces = []\n", "\n", "print(\"Simulating input-driven responses...\")\n", "\n", "for input_func, name in zip(input_functions, input_names):\n", " # Create fresh model\n", " model_transient = brainmass.WilsonCowanStep(1)\n", " model_transient.init_all_states()\n", "\n", "\n", " def step_transient(i):\n", " t = i * brainstate.environ.get_dt()\n", " inp = input_func(t)\n", " model_transient.update(rE_inp=inp)\n", " return model_transient.rE.value, model_transient.rI.value, inp\n", "\n", "\n", " indices_trans = np.arange(6000) # 600 ms\n", " rE_resp, rI_resp, inp_trace = brainstate.transform.for_loop(step_transient, indices_trans)\n", "\n", " responses_E.append(rE_resp)\n", " responses_I.append(rI_resp)\n", " input_traces.append(inp_trace)\n", "\n", " print(f\" {name}: Complete\")\n", "\n", "print(\"Done!\")" ], "id": "6b0cd7d6b327ad35", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Simulating input-driven responses...\n", " Pulse (10ms): Complete\n", " Step (at 200ms): Complete\n", " Ramp (500ms): Complete\n", "Done!\n" ] } ], "execution_count": 10 }, { "metadata": { "ExecuteTime": { "end_time": "2026-03-21T13:51:45.090513Z", "start_time": "2026-03-21T13:51:43.189746900Z" } }, "cell_type": "code", "source": [ "# Visualization: 3x3 grid (input type × measurement)\n", "fig, axes = plt.subplots(3, 3, figsize=(15, 10))\n", "\n", "t_trans = indices_trans * brainstate.environ.get_dt()\n", "\n", "for row, (rE_resp, rI_resp, inp_trace, name, color) in enumerate(\n", " zip(responses_E, responses_I, input_traces, input_names, input_colors)\n", "):\n", "\n", " # Column 1: Input trace\n", " ax = axes[row, 0]\n", " ax.plot(t_trans, inp_trace, color=color, linewidth=2.5)\n", " ax.set_ylabel('Input Amplitude')\n", " ax.set_title(f'{name}: Input')\n", " ax.grid(True, alpha=0.3)\n", " if row == 2:\n", " ax.set_xlabel('Time (ms)')\n", "\n", " # Column 2: E/I Response\n", " ax = axes[row, 1]\n", " ax.plot(t_trans, rE_resp[:, 0], 'b-', linewidth=2, label='E', alpha=0.8)\n", " ax.plot(t_trans, rI_resp[:, 0], 'r-', linewidth=2, label='I', alpha=0.8)\n", " ax.set_ylabel('Activity')\n", " ax.set_title(f'{name}: E/I Response')\n", " ax.legend()\n", " ax.grid(True, alpha=0.3)\n", " if row == 2:\n", " ax.set_xlabel('Time (ms)')\n", "\n", " # Column 3: Phase Portrait\n", " ax = axes[row, 2]\n", " ax.plot(rE_resp[:, 0], rI_resp[:, 0], 'k-', linewidth=1.5, alpha=0.7)\n", " ax.plot(rE_resp[0, 0], rI_resp[0, 0], 'go', markersize=10, markeredgecolor='black', markeredgewidth=1.5)\n", " ax.plot(rE_resp[-1, 0], rI_resp[-1, 0], 'r*', markersize=14, markeredgecolor='black', markeredgewidth=1.5)\n", " ax.set_xlabel('E activity')\n", " ax.set_ylabel('I activity')\n", " ax.set_title(f'{name}: Phase Portrait')\n", " ax.grid(True, alpha=0.3)\n", "\n", "plt.suptitle('Input-Driven Transient Responses', fontsize=14, y=0.995)\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "# Quantitative metrics\n", "print(\"\\nResponse Metrics:\")\n", "print(\"=\" * 60)\n", "\n", "for i, (rE_resp, name) in enumerate(zip(responses_E, input_names)):\n", " # Find peak\n", " peak_idx = np.argmax(rE_resp[:, 0])\n", " peak_time = float(t_trans[peak_idx] / u.ms)\n", " peak_amplitude = rE_resp[peak_idx, 0]\n", "\n", " # Steady state (last 100 samples)\n", " steady_state = np.mean(rE_resp[-100:, 0])\n", "\n", " # Initial response time (when crosses 10% of peak)\n", " threshold = 0.1 * peak_amplitude\n", " response_idx = np.where(rE_resp[:, 0] > threshold)[0]\n", " response_time = float(t_trans[response_idx[0]] / u.ms) if len(response_idx) > 0 else 0\n", "\n", " print(f\"{name}:\")\n", " print(f\" Peak amplitude: {peak_amplitude:.4f} at t={peak_time:.1f} ms\")\n", " print(f\" Response latency: {response_time:.1f} ms\")\n", " print(f\" Steady state: {steady_state:.4f}\")\n", " print(f\" Overshoot: {(peak_amplitude - steady_state) / steady_state * 100:.1f}%\")\n", " print()" ], "id": "465b790ce8c49374", "outputs": [ { "data": { "text/plain": [ "
" ], "image/png": "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" }, "metadata": {}, "output_type": "display_data", "jetTransient": { "display_id": null } }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Response Metrics:\n", "============================================================\n", "Pulse (10ms):\n", " Peak amplitude: 0.4894 at t=108.3 ms\n", " Response latency: 100.2 ms\n", " Steady state: 0.4625\n", " Overshoot: 5.8%\n", "\n", "Step (at 200ms):\n", " Peak amplitude: 0.4843 at t=209.7 ms\n", " Response latency: 200.7 ms\n", " Steady state: 0.4843\n", " Overshoot: 0.0%\n", "\n", "Ramp (500ms):\n", " Peak amplitude: 0.4859 at t=599.9 ms\n", " Response latency: 145.9 ms\n", " Steady state: 0.4858\n", " Overshoot: 0.0%\n", "\n" ] } ], "execution_count": 11 }, { "metadata": {}, "cell_type": "markdown", "source": [ "\n", "## Bistability Demonstration\n", "\n", "One of the most fascinating phenomena in neural dynamics is **bistability**: the coexistence of two stable states. Depending on the initial condition, the system can settle into different attractors, even with the same parameters and inputs.\n", "\n", "This property is crucial for modeling:\n", "- **Cortical up/down states** (transitions between high and low firing)\n", "- **Working memory** (persistent activity states)\n", "- **Decision-making** (commitment to one of two choices)\n", "\n", "We'll demonstrate bistability by:\n", "1. Finding parameter regime where two stable fixed points coexist\n", "2. Showing trajectories from different initial conditions converge to different attractors\n", "3. Computing the **basin of attraction** for each stable state" ], "id": "ebbd224022a71865" }, { "metadata": { "ExecuteTime": { "end_time": "2026-03-21T13:51:45.177375900Z", "start_time": "2026-03-21T13:51:45.167156Z" } }, "cell_type": "code", "source": [ "@brainstate.transform.jit(static_argnums=2, static_argnames='n_steps')\n", "def bistable_simulation(rE0, rI0, n_steps: int = 20000):\n", " # Create model in bistable parameter regime\n", " # Key: High recurrent excitation (wEE) creates positive feedback\n", " model_bistable = brainmass.WilsonCowanStep(\n", " 1,\n", " wEE=16.0, # Increased from default 12\n", " wEI=12.0, # Decreased from default 13\n", " wII=8.0, # Decreased from default 11\n", " )\n", "\n", " # Reset and set initial condition\n", " model_bistable.init_all_states()\n", " model_bistable.rE.value = u.math.array([rE0])\n", " model_bistable.rI.value = u.math.array([rI0])\n", "\n", " # Simulate\n", " def step_bistable(i):\n", " model_bistable.update(rE_inp=0.5) # Constant input\n", " return model_bistable.rE.value, model_bistable.rI.value\n", "\n", " indices_bistable = np.arange(n_steps) # 2000 ms\n", " rE_traj, rI_traj = brainstate.transform.for_loop(step_bistable, indices_bistable)\n", " return rE_traj, rI_traj" ], "id": "ea49cf7ce5165e8e", "outputs": [], "execution_count": 12 }, { "metadata": { "ExecuteTime": { "end_time": "2026-03-21T13:51:46.149064600Z", "start_time": "2026-03-21T13:51:45.178362500Z" } }, "cell_type": "code", "source": [ "# Different initial conditions\n", "initial_conditions = [\n", " (0.05, 0.05), # Low state\n", " (0.65, 0.35), # High state\n", " (0.35, 0.20), # Intermediate (will go to one or the other)\n", "]\n", "\n", "colors_ic = ['blue', 'red', 'purple']\n", "labels_ic = ['Low IC', 'High IC', 'Mid IC']\n", "\n", "# Storage for trajectories\n", "trajectories_rE = []\n", "trajectories_rI = []\n", "\n", "print(\"Simulating bistability from different initial conditions...\")\n", "\n", "for (rE0, rI0), color, label in zip(initial_conditions, colors_ic, labels_ic):\n", " rE_traj, rI_traj = bistable_simulation(rE0, rI0, n_steps=20000)\n", "\n", " trajectories_rE.append(rE_traj)\n", " trajectories_rI.append(rI_traj)\n", "\n", " print(\n", " f\" {label}: \"\n", " f\"Started at ({rE0:.2f}, {rI0:.2f}), \"\n", " f\"ended at ({float(rE_traj[-1, 0]):.4f}, {float(rI_traj[-1, 0]):.4f})\"\n", " )\n", "\n", "print(\"Done!\")" ], "id": "95a28deacdcc1e3c", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Simulating bistability from different initial conditions...\n" ] }, { "ename": "ValueError", "evalue": "static_argnums contains index 2, but only 2 positional arguments were provided.", "output_type": "error", "traceback": [ "\u001B[31m---------------------------------------------------------------------------\u001B[39m", "\u001B[31mValueError\u001B[39m Traceback (most recent call last)", "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[13]\u001B[39m\u001B[32m, line 18\u001B[39m\n\u001B[32m 15\u001B[39m \u001B[38;5;28mprint\u001B[39m(\u001B[33m\"\u001B[39m\u001B[33mSimulating bistability from different initial conditions...\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m 17\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m (rE0, rI0), color, label \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mzip\u001B[39m(initial_conditions, colors_ic, labels_ic):\n\u001B[32m---> \u001B[39m\u001B[32m18\u001B[39m rE_traj, rI_traj = bistable_simulation(rE0, rI0, n_steps=\u001B[32m20000\u001B[39m)\n\u001B[32m 20\u001B[39m trajectories_rE.append(rE_traj)\n\u001B[32m 21\u001B[39m trajectories_rI.append(rI_traj)\n", "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\brainstate\\transform\\_jit.py:103\u001B[39m, in \u001B[36m_get_jitted_fun..jitted_fn\u001B[39m\u001B[34m(*args, **params)\u001B[39m\n\u001B[32m 100\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m fun.fun(*args, **params)\n\u001B[32m 102\u001B[39m \u001B[38;5;66;03m# compile the function and get the state trace\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m103\u001B[39m state_trace = fun.get_state_trace(*args, **params, compile_if_miss=\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[32m 104\u001B[39m read_state_vals = state_trace.get_read_state_values(\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[32m 106\u001B[39m \u001B[38;5;66;03m# call the jitted function\u001B[39;00m\n", "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\brainstate\\transform\\_make_jaxpr.py:594\u001B[39m, in \u001B[36mStatefulFunction.get_state_trace\u001B[39m\u001B[34m(self, compile_if_miss, *args, **kwargs)\u001B[39m\n\u001B[32m 576\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mget_state_trace\u001B[39m(\u001B[38;5;28mself\u001B[39m, *args, compile_if_miss: \u001B[38;5;28mbool\u001B[39m = \u001B[38;5;28;01mTrue\u001B[39;00m, **kwargs) -> StateTraceStack:\n\u001B[32m 577\u001B[39m \u001B[38;5;250m \u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m 578\u001B[39m \u001B[33;03m Read the state trace of the function.\u001B[39;00m\n\u001B[32m 579\u001B[39m \n\u001B[32m (...)\u001B[39m\u001B[32m 592\u001B[39m \u001B[33;03m The state trace of the function.\u001B[39;00m\n\u001B[32m 593\u001B[39m \u001B[33;03m \"\"\"\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m594\u001B[39m cache_key = \u001B[38;5;28mself\u001B[39m.get_arg_cache_key(*args, **kwargs, compile_if_miss=compile_if_miss)\n\u001B[32m 595\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m.get_state_trace_by_cache(cache_key)\n", "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\brainstate\\transform\\_make_jaxpr.py:418\u001B[39m, in \u001B[36mStatefulFunction.get_arg_cache_key\u001B[39m\u001B[34m(self, compile_if_miss, *args, **kwargs)\u001B[39m\n\u001B[32m 411\u001B[39m cache_key = get_arg_cache_key(\n\u001B[32m 412\u001B[39m \u001B[38;5;28mself\u001B[39m.static_argnums,\n\u001B[32m 413\u001B[39m \u001B[38;5;28mself\u001B[39m.static_argnames,\n\u001B[32m 414\u001B[39m args,\n\u001B[32m 415\u001B[39m kwargs,\n\u001B[32m 416\u001B[39m )\n\u001B[32m 417\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m cache_key \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m._compilation_cache \u001B[38;5;129;01mand\u001B[39;00m compile_if_miss:\n\u001B[32m--> \u001B[39m\u001B[32m418\u001B[39m \u001B[38;5;28mself\u001B[39m.make_jaxpr(*args, **kwargs)\n\u001B[32m 419\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m cache_key\n", "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\brainstate\\transform\\_make_jaxpr.py:845\u001B[39m, in \u001B[36mStatefulFunction.make_jaxpr\u001B[39m\u001B[34m(self, *args, **kwargs)\u001B[39m\n\u001B[32m 843\u001B[39m max_idx = \u001B[38;5;28mmax\u001B[39m(\u001B[38;5;28mself\u001B[39m.static_argnums)\n\u001B[32m 844\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m max_idx >= \u001B[38;5;28mlen\u001B[39m(args):\n\u001B[32m--> \u001B[39m\u001B[32m845\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[32m 846\u001B[39m \u001B[33mf\u001B[39m\u001B[33m'\u001B[39m\u001B[33mstatic_argnums contains index \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mmax_idx\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m, but only \u001B[39m\u001B[33m'\u001B[39m\n\u001B[32m 847\u001B[39m \u001B[33mf\u001B[39m\u001B[33m'\u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mlen\u001B[39m(args)\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m positional arguments were provided.\u001B[39m\u001B[33m'\u001B[39m\n\u001B[32m 848\u001B[39m )\n\u001B[32m 850\u001B[39m \u001B[38;5;66;03m# Fast path: already compiled\u001B[39;00m\n\u001B[32m 851\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m cache_key \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m._compilation_cache:\n", "\u001B[31mValueError\u001B[39m: static_argnums contains index 2, but only 2 positional arguments were provided." ] } ], "execution_count": 13 }, { "metadata": {}, "cell_type": "code", "source": [ "# Sigmoid function\n", "def F(x, a, theta):\n", " return 1 / (1 + np.exp(-a * (x - theta))) - 1 / (1 + np.exp(a * theta))\n" ], "id": "b90926528b6ff10e", "outputs": [], "execution_count": null }, { "metadata": {}, "cell_type": "code", "source": "indices_bistable = np.arange(20000) # 2000 ms", "id": "83d0227f61f1476b", "outputs": [], "execution_count": null }, { "metadata": {}, "cell_type": "code", "source": [ "# Visualization: 3-panel bistability figure\n", "fig, axes = plt.subplots(1, 3, figsize=(16, 5))\n", "\n", "# Panel 1: Phase portrait with trajectories\n", "ax = axes[0]\n", "\n", "# Plot nullclines for reference\n", "rE_range_bis = np.linspace(0, 1, 200)\n", "rI_range_bis = np.linspace(0, 0.8, 200)\n", "RE_bis, RI_bis = np.meshgrid(rE_range_bis, rI_range_bis)\n", "\n", "wEE_bis, wEI_bis = 16.0, 12.0\n", "wIE_bis, wII_bis = 4.0, 8.0\n", "I_E_bis = 0.5\n", "\n", "drE_bis = -RE_bis + (1 - 1.0 * RE_bis) * F(wEE_bis * RE_bis - wEI_bis * RI_bis + I_E_bis, 1.2, 2.8)\n", "drI_bis = -RI_bis + (1 - 1.0 * RI_bis) * F(wIE_bis * RE_bis - wII_bis * RI_bis, 1.0, 4.0)\n", "\n", "ax.contour(RE_bis, RI_bis, drE_bis, levels=[0], colors=['blue'], linewidths=1.5, alpha=0.3)\n", "ax.contour(RE_bis, RI_bis, drI_bis, levels=[0], colors=['red'], linewidths=1.5, alpha=0.3)\n", "\n", "# Plot trajectories\n", "for (rE_traj, rI_traj, color, label) in zip(trajectories_rE, trajectories_rI, colors_ic, labels_ic):\n", " ax.plot(rE_traj[:, 0], rI_traj[:, 0], '-', color=color, linewidth=1.5, alpha=0.7, label=label)\n", " ax.plot(rE_traj[0, 0], rI_traj[0, 0], 'o', color=color, markersize=12, markeredgecolor='black', markeredgewidth=2)\n", " ax.plot(rE_traj[-1, 0], rI_traj[-1, 0], '*', color=color, markersize=16, markeredgecolor='black', markeredgewidth=2)\n", "\n", "ax.set_xlabel('E activity (rE)')\n", "ax.set_ylabel('I activity (rI)')\n", "ax.set_title('Phase Portrait: Bistability')\n", "ax.legend()\n", "ax.grid(True, alpha=0.3)\n", "\n", "# Panel 2: Time series overlay\n", "ax = axes[1]\n", "t_bis = indices_bistable * brainstate.environ.get_dt()\n", "\n", "for (rE_traj, color, label) in zip(trajectories_rE, colors_ic, labels_ic):\n", " ax.plot(t_bis, rE_traj[:, 0], '-', color=color, linewidth=2, label=label, alpha=0.8)\n", "\n", "ax.set_xlabel('Time (ms)')\n", "ax.set_ylabel('E activity (rE)')\n", "ax.set_title('Time Series: Different Steady States')\n", "ax.legend()\n", "ax.grid(True, alpha=0.3)\n", "ax.axhline(y=trajectories_rE[0][-1, 0], color=colors_ic[0], linestyle='--', alpha=0.5)\n", "ax.axhline(y=trajectories_rE[1][-1, 0], color=colors_ic[1], linestyle='--', alpha=0.5)\n", "\n", "# Panel 3: Basin of attraction\n", "ax = axes[2]\n", "\n", "# Grid of initial conditions\n", "rE_init_grid = np.linspace(0, 1, 40)\n", "rI_init_grid = np.linspace(0, 0.8, 40)\n", "basin_map = np.zeros((len(rI_init_grid), len(rE_init_grid)))\n", "\n", "print(\"Computing basin of attraction (this may take ~30-60 seconds)...\")\n", "\n", "for i, rE_init in enumerate(rE_init_grid):\n", " for j, rI_init in enumerate(rI_init_grid):\n", " rE_final = bistable_simulation(rE_init, rI_init, n_steps=15000)[0]\n", "\n", " # Classify final state (low vs high)\n", " final_value = rE_final[-1, 0]\n", " if final_value < 0.3:\n", " basin_map[j, i] = 0 # Low state\n", " else:\n", " basin_map[j, i] = 1 # High state\n", "\n", "print(\"Basin computation complete!\")\n", "\n", "# Plot basin\n", "im = ax.imshow(basin_map, extent=[0, 1, 0, 0.8], origin='lower', cmap='RdBu_r', alpha=0.6, aspect='auto')\n", "ax.set_xlabel('Initial E activity')\n", "ax.set_ylabel('Initial I activity')\n", "ax.set_title('Basin of Attraction')\n", "\n", "# Mark the initial conditions we tested\n", "for (rE0, rI0), color in zip(initial_conditions, colors_ic):\n", " ax.plot(rE0, rI0, 'o', color=color, markersize=12, markeredgecolor='black', markeredgewidth=2)\n", "\n", "# Colorbar\n", "cbar = plt.colorbar(im, ax=ax, ticks=[0, 1])\n", "cbar.set_label('Final State')\n", "cbar.ax.set_yticklabels(['Low', 'High'])\n", "\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "print(\"\\nBiological Significance:\")\n", "print(\"- Blue region → Low state (cortical 'down' state)\")\n", "print(\"- Red region → High state (cortical 'up' state)\")\n", "print(\"- Boundary separates the two basins of attraction\")" ], "id": "acc0fdf2783cbf44", "outputs": [], "execution_count": null }, { "metadata": {}, "cell_type": "markdown", "source": "## Brain network", "id": "e3921ae7b0d413c7" }, { "metadata": {}, "cell_type": "code", "source": [ "import os.path\n", "import kagglehub\n", "\n", "path = kagglehub.dataset_download(\"oujago/hcp-gw-data-samples\")\n", "data = braintools.file.msgpack_load(os.path.join(path, \"hcp-data-sample.msgpack\"))" ], "id": "bad70eb403763bce", "outputs": [], "execution_count": null }, { "metadata": {}, "cell_type": "markdown", "source": [ "We now couple many Wilson–Cowan nodes according to structural connectivity (weights `Cmat`) and distances `Dmat` from the `hcp` dataset. Delays are derived from distances and a `signal_speed`. We use diffusive coupling on `rE` with per‑edge delays and scale the interaction by a global gain `k`." ], "id": "explain-brainnet-wc" }, { "metadata": {}, "cell_type": "code", "source": [ "class Network(brainstate.nn.Module):\n", " def __init__(self, signal_speed=2., k=1.):\n", " super().__init__()\n", "\n", " conn_weight = data['Cmat'].copy()\n", " np.fill_diagonal(conn_weight, 0)\n", " delay_time = data['Dmat'].copy() / signal_speed\n", " np.fill_diagonal(delay_time, 0)\n", " indices_ = np.arange(conn_weight.shape[1])\n", " indices_ = np.tile(np.expand_dims(indices_, axis=0), (conn_weight.shape[0], 1))\n", "\n", " self.node = brainmass.WilsonCowanStep(\n", " 80,\n", " noise_E=brainmass.OUProcess(80, sigma=0.01, init=braintools.init.ZeroInit()),\n", " noise_I=brainmass.OUProcess(80, sigma=0.01, init=braintools.init.ZeroInit()),\n", " )\n", " self.coupling = brainmass.DiffusiveCoupling(\n", " self.node.prefetch_delay('rE', (delay_time * u.ms, indices_), init=braintools.init.Uniform(0, 0.05)),\n", " self.node.prefetch('rE'),\n", " conn_weight,\n", " k=k\n", " )\n", "\n", " def update(self):\n", " current = self.coupling()\n", " rE = self.node(current)\n", " return rE\n", "\n", " def step_run(self, i):\n", " return self.update()" ], "id": "c73620d2f05d1557", "outputs": [], "execution_count": null }, { "metadata": {}, "cell_type": "markdown", "source": [ "Network dynamics:\n", "- `prefetch_delay('rE', (delay_time, indices))` constructs a delayed view of E‑rates along edges.\n", "- `DiffusiveCoupling` computes inputs from delayed neighbors and scales by `k`.\n", "- The node integrates these inputs and returns `rE`." ], "id": "explain-network-wc" }, { "metadata": {}, "cell_type": "code", "source": "", "id": "452b74bca667de42", "outputs": [], "execution_count": null }, { "metadata": {}, "cell_type": "code", "source": [ "net = Network()\n", "net.init_all_states()\n", "indices = np.arange(0, 6e3 // (brainstate.environ.get_dt() / u.ms))\n", "exes = brainstate.transform.for_loop(net.step_run, indices)" ], "id": "6b96a7c83fe64c9e", "outputs": [], "execution_count": null }, { "metadata": {}, "cell_type": "markdown", "source": [ "We compute functional connectivity (pairwise correlation) from the simulated `rE` matrix and plot it alongside sample time series. Stronger structure‑function correspondence typically emerges around specific `k` and delay regimes." ], "id": "explain-fc-plot-wc" }, { "metadata": {}, "cell_type": "code", "source": [ "fig, gs = braintools.visualize.get_figure(1, 2, 4, 6)\n", "ax1 = fig.add_subplot(gs[0, 0])\n", "fc = braintools.metric.functional_connectivity(exes)\n", "ax = ax1.imshow(fc)\n", "plt.colorbar(ax, ax=ax1)\n", "ax2 = fig.add_subplot(gs[0, 1])\n", "ax2.plot(indices, exes[:, ::5], alpha=0.8)\n", "plt.show()" ], "id": "b9a7f0b8f16f6733", "outputs": [], "execution_count": null } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 5 }