{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Overview\n", "\n", "``brainpy.state`` represents a complete architectural redesign built on top of the ``brainstate`` framework. This document explains the design principles and architectural components that make ``brainpy.state`` powerful and flexible." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Design Philosophy\n", "\n", "``brainpy.state`` is built around several core principles:\n", "\n", "**State-Based Programming**\n", " All dynamical variables are managed as explicit states, enabling automatic differentiation, efficient compilation, and clear data flow.\n", "\n", "**Modular Composition**\n", " Complex models are built by composing simple, reusable components. Each component has a well-defined interface and responsibility.\n", "\n", "**Scientific Accuracy**\n", " Integration with ``saiunit`` ensures physical correctness and prevents unit-related errors.\n", "\n", "**Performance by Default**\n", " JIT compilation and optimization are built into the framework, not an afterthought.\n", "\n", "**Extensibility**\n", " Adding new neuron models, synapse types, or learning rules is straightforward and follows clear patterns." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Architectural Layers\n", "\n", "brainpy.state is organized into several layers:\n", "\n", "```text\n", "┌─────────────────────────────────────────┐\n", "│ User Models & Networks │ ← Your code\n", "├─────────────────────────────────────────┤\n", "│ BrainPy Components Layer │ ← Neurons, Synapses, Projections\n", "├─────────────────────────────────────────┤\n", "│ BrainState Framework │ ← State management, compilation\n", "├─────────────────────────────────────────┤\n", "│ JAX + XLA Backend │ ← JIT compilation, autodiff\n", "└─────────────────────────────────────────┘\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. JAX + XLA Backend\n", "\n", "The foundation layer provides:\n", "\n", "- Just-In-Time (JIT) compilation\n", "- Automatic differentiation\n", "- Hardware acceleration (CPU/GPU/TPU)\n", "- Functional transformations (vmap, grad, etc.)\n", "\n", "### 2. BrainState Framework\n", "\n", "Built on JAX, ``brainstate`` provides:\n", "\n", "- State management system\n", "- Module composition\n", "- Compilation and optimization\n", "- Program transformations (for_loop, etc.)\n", "\n", "### 3. BrainPy Components\n", "\n", "High-level neuroscience-specific components:\n", "\n", "- Neuron models (LIF, ALIF, etc.)\n", "- Synapse models (Expon, Alpha, etc.)\n", "- Projection architectures\n", "- Learning rules and plasticity\n", "\n", "### 4. User Models\n", "\n", "Your custom networks and experiments built using BrainPy components." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## State Management System\n", "\n", "### The Foundation: ``brainstate.State``\n", "\n", "Everything in ``brainpy.state`` revolves around **states**:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2025-11-13T09:31:08.201528Z", "start_time": "2025-11-13T09:31:02.936126Z" }, "execution": { "iopub.execute_input": "2026-05-11T06:19:33.009178Z", "iopub.status.busy": "2026-05-11T06:19:33.008966Z", "iopub.status.idle": "2026-05-11T06:19:35.657920Z", "shell.execute_reply": "2026-05-11T06:19:35.657134Z" } }, "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": [ "import brainpy.state\n", "import brainstate\n", "import braintools\n", "import saiunit as u\n", "import jax.numpy as jnp\n", "\n", "# Create a state\n", "voltage = brainstate.State(0.0) # Single value\n", "weights = brainstate.State([[0.1, 0.2], [0.3, 0.4]]) # Matrix" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "States are special containers that:\n", "\n", "- Track their values across time\n", "- Support automatic differentiation\n", "- Enable efficient compilation\n", "- Handle batching automatically" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### State Types\n", "\n", "BrainPy uses different state types for different purposes:\n", "\n", "**ParamState** - Trainable Parameters\n", " Used for weights, time constants, and other trainable parameters." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2025-11-13T09:31:08.232382Z", "start_time": "2025-11-13T09:31:08.227046Z" }, "execution": { "iopub.execute_input": "2026-05-11T06:19:35.660619Z", "iopub.status.busy": "2026-05-11T06:19:35.660147Z", "iopub.status.idle": "2026-05-11T06:19:35.664740Z", "shell.execute_reply": "2026-05-11T06:19:35.663713Z" } }, "outputs": [], "source": [ "class MyNeuron(brainstate.nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.tau = brainstate.ParamState(10.0) # Trainable\n", " self.weight = brainstate.ParamState([[0.1, 0.2]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**ShortTermState** - Temporary Variables\n", " Used for membrane potentials, synaptic currents, and other dynamics." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2025-11-13T09:31:08.256361Z", "start_time": "2025-11-13T09:31:08.248156Z" }, "execution": { "iopub.execute_input": "2026-05-11T06:19:35.667391Z", "iopub.status.busy": "2026-05-11T06:19:35.667156Z", "iopub.status.idle": "2026-05-11T06:19:35.670708Z", "shell.execute_reply": "2026-05-11T06:19:35.669836Z" } }, "outputs": [], "source": [ "class MyNeuron(brainstate.nn.Module):\n", " def __init__(self, size):\n", " super().__init__()\n", " self.V = brainstate.ShortTermState(jnp.zeros(size)) # Dynamic\n", " self.spike = brainstate.ShortTermState(jnp.zeros(size))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### State Initialization\n", "\n", "States can be initialized with various strategies:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2025-11-13T09:31:08.332155Z", "start_time": "2025-11-13T09:31:08.288203Z" }, "execution": { "iopub.execute_input": "2026-05-11T06:19:35.673616Z", "iopub.status.busy": "2026-05-11T06:19:35.673335Z", "iopub.status.idle": "2026-05-11T06:19:36.379440Z", "shell.execute_reply": "2026-05-11T06:19:36.378488Z" } }, "outputs": [], "source": [ "# Define example size and shape\n", "size = 100 # Number of neurons\n", "shape = (100, 50) # Weight matrix shape\n", "\n", "# Constant initialization\n", "V = brainstate.ShortTermState(\n", " braintools.init.Constant(-65.0, unit=u.mV)(size)\n", ")\n", "\n", "# Normal distribution\n", "V = brainstate.ShortTermState(\n", " braintools.init.Normal(-65.0, 5.0, unit=u.mV)(size)\n", ")\n", "\n", "# Uniform distribution\n", "weights = brainstate.ParamState(\n", " braintools.init.Uniform(0.0, 1.0)(shape)\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Module System\n", "\n", "### Base Class: brainstate.nn.Module\n", "\n", "All BrainPy components inherit from ``brainstate.nn.Module``:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2025-11-13T09:31:08.344089Z", "start_time": "2025-11-13T09:31:08.338163Z" }, "execution": { "iopub.execute_input": "2026-05-11T06:19:36.381651Z", "iopub.status.busy": "2026-05-11T06:19:36.381492Z", "iopub.status.idle": "2026-05-11T06:19:36.385023Z", "shell.execute_reply": "2026-05-11T06:19:36.384323Z" } }, "outputs": [], "source": [ "class MyComponent(brainstate.nn.Module):\n", " def __init__(self, size):\n", " super().__init__()\n", " # Initialize states\n", " self.state1 = brainstate.ShortTermState(jnp.zeros(size))\n", " self.param1 = brainstate.ParamState(jnp.ones(size))\n", "\n", " def update(self, input):\n", " # Define dynamics\n", " pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Benefits of Module:\n", "\n", "- Automatic state registration\n", "- Nested module support\n", "- State collection and filtering\n", "- Serialization support" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Module Composition\n", "\n", "Modules can contain other modules:\n", "\n", "```python\n", "\n", "class Network(brainstate.nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.neurons = brainpy.state.LIF(100, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", " self.synapse = brainpy.state.Expon(100, tau=5*u.ms)\n", " self.projection = brainpy.state.AlignPostProj(...) # Example - requires more setup\n", "\n", " def update(self, input):\n", " # Compose behavior\n", " self.projection(spikes) # Example\n", " self.neurons(input)\n", "\n", "```\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Component Architecture\n", "\n", "### Neurons\n", "\n", "Neurons model the dynamics of neural populations:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2025-11-13T09:31:08.354960Z", "start_time": "2025-11-13T09:31:08.350705Z" }, "execution": { "iopub.execute_input": "2026-05-11T06:19:36.387295Z", "iopub.status.busy": "2026-05-11T06:19:36.387033Z", "iopub.status.idle": "2026-05-11T06:19:36.391211Z", "shell.execute_reply": "2026-05-11T06:19:36.390139Z" } }, "outputs": [], "source": [ "class Neuron(brainstate.nn.Module):\n", " def __init__(self, size, **kwargs):\n", " super().__init__()\n", " # Membrane potential\n", " self.V = brainstate.ShortTermState(jnp.zeros(size))\n", " # Spike output\n", " self.spike = brainstate.ShortTermState(jnp.zeros(size))\n", "\n", " def update(self, input_current):\n", " # Update membrane potential\n", " # Generate spikes\n", " pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Key responsibilities:\n", "\n", "- Maintain membrane potential\n", "- Generate spikes when threshold is crossed\n", "- Reset after spiking\n", "- Integrate input currents" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Synapses\n", "\n", "Synapses model temporal filtering of spike trains:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2025-11-13T09:31:08.365530Z", "start_time": "2025-11-13T09:31:08.361447Z" }, "execution": { "iopub.execute_input": "2026-05-11T06:19:36.393683Z", "iopub.status.busy": "2026-05-11T06:19:36.393414Z", "iopub.status.idle": "2026-05-11T06:19:36.397456Z", "shell.execute_reply": "2026-05-11T06:19:36.396621Z" } }, "outputs": [], "source": [ "class Synapse(brainstate.nn.Module):\n", " def __init__(self, size, tau, **kwargs):\n", " super().__init__()\n", " # Synaptic conductance/current\n", " self.g = brainstate.ShortTermState(jnp.zeros(size))\n", " self.tau = tau\n", "\n", " def update(self, spike_input):\n", " # Update synaptic variable\n", " # Return filtered output\n", " pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Key responsibilities:\n", "\n", "- Filter spike inputs temporally\n", "- Model synaptic dynamics (exponential, alpha, etc.)\n", "- Provide smooth currents to postsynaptic neurons" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Projections: The Comm-Syn-Out Pattern\n", "\n", "Projections connect populations using a three-stage architecture:\n", "\n", "```text\n", "Presynaptic Spikes → [Comm] → [Syn] → [Out] → Postsynaptic Neurons\n", " │ │ │\n", " Connectivity │ Current\n", " & Weights Dynamics Injection\n", "```\n", "\n", "**Communication (Comm)**\n", " Handles spike transmission, connectivity, and weights." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2025-11-13T09:31:08.380053Z", "start_time": "2025-11-13T09:31:08.371055Z" }, "execution": { "iopub.execute_input": "2026-05-11T06:19:36.400171Z", "iopub.status.busy": "2026-05-11T06:19:36.399867Z", "iopub.status.idle": "2026-05-11T06:19:36.428644Z", "shell.execute_reply": "2026-05-11T06:19:36.427687Z" } }, "outputs": [], "source": [ "# Define population sizes\n", "pre_size = 100\n", "post_size = 50\n", "\n", "# Define prob and weight\n", "prob=0.1\n", "weight=0.5\n", "\n", "comm = brainstate.nn.EventFixedProb(\n", " pre_size, post_size, prob, weight\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Synaptic Dynamics (Syn)**\n", " Temporal filtering of transmitted spikes." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2025-11-13T09:31:08.390544Z", "start_time": "2025-11-13T09:31:08.386339Z" }, "execution": { "iopub.execute_input": "2026-05-11T06:19:36.431009Z", "iopub.status.busy": "2026-05-11T06:19:36.430774Z", "iopub.status.idle": "2026-05-11T06:19:36.433910Z", "shell.execute_reply": "2026-05-11T06:19:36.433215Z" } }, "outputs": [], "source": [ "post_size = 50 # Postsynaptic population size\n", "\n", "syn = brainpy.state.Expon(post_size, tau=5*u.ms)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Output Mechanism (Out)**\n", " How synaptic variables affect postsynaptic neurons." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2025-11-13T09:31:08.400227Z", "start_time": "2025-11-13T09:31:08.397246Z" }, "execution": { "iopub.execute_input": "2026-05-11T06:19:36.435677Z", "iopub.status.busy": "2026-05-11T06:19:36.435509Z", "iopub.status.idle": "2026-05-11T06:19:36.438939Z", "shell.execute_reply": "2026-05-11T06:19:36.438095Z" } }, "outputs": [], "source": [ "# Current-based output\n", "out = brainpy.state.CUBA() \n", "\n", "# Or conductance-based output\n", "out = brainpy.state.COBA(E=0*u.mV)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Complete Projection**" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2025-11-13T09:31:08.420603Z", "start_time": "2025-11-13T09:31:08.405533Z" }, "execution": { "iopub.execute_input": "2026-05-11T06:19:36.441306Z", "iopub.status.busy": "2026-05-11T06:19:36.441071Z", "iopub.status.idle": "2026-05-11T06:19:36.444669Z", "shell.execute_reply": "2026-05-11T06:19:36.444005Z" } }, "outputs": [], "source": [ "# Define postsynaptic neurons\n", "postsynaptic_neurons = brainpy.state.LIF(50, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", "\n", "# Create complete projection\n", "projection = brainpy.state.AlignPostProj(\n", " comm=comm,\n", " syn=syn,\n", " out=out,\n", " post=postsynaptic_neurons\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This separation provides:\n", "\n", "- Clear responsibility boundaries\n", "- Easy component swapping\n", "- Reusable building blocks\n", "- Better testing and debugging" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compilation and Execution\n", "\n", "### Time-Stepped Simulation\n", "\n", "BrainPy uses discrete time steps:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2025-11-13T09:31:08.937334Z", "start_time": "2025-11-13T09:31:08.430048Z" }, "execution": { "iopub.execute_input": "2026-05-11T06:19:36.446778Z", "iopub.status.busy": "2026-05-11T06:19:36.446531Z", "iopub.status.idle": "2026-05-11T06:19:36.688565Z", "shell.execute_reply": "2026-05-11T06:19:36.687741Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "50db3d97379845a69132eca4e50c8879", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/10000 [00:00 0:\n", " optimizer = braintools.optim.Adam(lr=1e-3)\n", " grads, loss = brainstate.transform.grad(\n", " loss_fn,\n", " grad_states=params,\n", " return_value=True\n", " )()\n", " print(f\"Loss: {loss}\")\n", " # Update parameters with optimizer (if defined)\n", " optimizer.update(grads)\n", "else:\n", " # If no trainable parameters, just compute loss\n", " loss = loss_fn()\n", " print(f\"Loss (no trainable params): {loss}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Physical Units System\n", "\n", "### Integration with saiunit\n", "\n", "``brainpy.state`` integrates ``saiunit`` for scientific accuracy:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2025-11-13T09:31:09.386693Z", "start_time": "2025-11-13T09:31:09.382194Z" }, "execution": { "iopub.execute_input": "2026-05-11T06:19:37.059734Z", "iopub.status.busy": "2026-05-11T06:19:37.059472Z", "iopub.status.idle": "2026-05-11T06:19:37.064489Z", "shell.execute_reply": "2026-05-11T06:19:37.063364Z" } }, "outputs": [], "source": [ "# Define with units\n", "tau = 10 * u.ms\n", "threshold = -50 * u.mV\n", "current = 5 * u.nA\n", "\n", "# Units are checked automatically\n", "neuron = brainpy.state.LIF(100, tau=tau, V_th=threshold)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Benefits:\n", "\n", "- Prevents unit errors (e.g., ms vs s)\n", "- Self-documenting code\n", "- Automatic unit conversions\n", "- Scientific correctness" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Unit Operations" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2025-11-13T09:31:09.432404Z", "start_time": "2025-11-13T09:31:09.427692Z" }, "execution": { "iopub.execute_input": "2026-05-11T06:19:37.067119Z", "iopub.status.busy": "2026-05-11T06:19:37.066849Z", "iopub.status.idle": "2026-05-11T06:19:37.083311Z", "shell.execute_reply": "2026-05-11T06:19:37.082377Z" } }, "outputs": [], "source": [ "# Arithmetic with units\n", "total_time = 100 * u.ms + 0.5 * u.second # → 600 ms\n", "\n", "# Unit conversion\n", "time_in_seconds = (100 * u.ms).to_decimal(u.second) # → 0.1\n", "\n", "# Unit checking (automatic in BrainPy operations)\n", "voltage = -65 * u.mV\n", "current = 2 * u.nA\n", "resistance = voltage / current # Automatically gives MΩ" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ecosystem Integration\n", "\n", "``brainpy.state`` integrates tightly with its ecosystem:\n", "\n", "### braintools\n", "\n", "Utilities and tools:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2025-11-13T09:31:09.471630Z", "start_time": "2025-11-13T09:31:09.465919Z" }, "execution": { "iopub.execute_input": "2026-05-11T06:19:37.085634Z", "iopub.status.busy": "2026-05-11T06:19:37.085380Z", "iopub.status.idle": "2026-05-11T06:19:37.089347Z", "shell.execute_reply": "2026-05-11T06:19:37.088417Z" } }, "outputs": [], "source": [ "# Optimizers\n", "optimizer = braintools.optim.Adam(lr=1e-3)\n", "\n", "# Initializers\n", "init = braintools.init.KaimingNormal()\n", "\n", "# Surrogate gradients\n", "spike_fn = braintools.surrogate.ReluGrad()\n", "\n", "# Metrics (example with dummy data)\n", "# pred = jnp.array([0.1, 0.9])\n", "# target = jnp.array([0, 1])\n", "# loss = braintools.metric.cross_entropy(pred, target)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### saiunit\n", "\n", "Physical units:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2025-11-13T09:31:09.496064Z", "start_time": "2025-11-13T09:31:09.490951Z" }, "execution": { "iopub.execute_input": "2026-05-11T06:19:37.091255Z", "iopub.status.busy": "2026-05-11T06:19:37.091053Z", "iopub.status.idle": "2026-05-11T06:19:37.094309Z", "shell.execute_reply": "2026-05-11T06:19:37.093287Z" } }, "outputs": [], "source": [ "# All standard SI units\n", "time = 10 * u.ms\n", "voltage = -65 * u.mV\n", "current = 2 * u.nA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### brainstate\n", "\n", "Core framework (used automatically):" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2025-11-13T09:31:09.518362Z", "start_time": "2025-11-13T09:31:09.512404Z" }, "execution": { "iopub.execute_input": "2026-05-11T06:19:37.096419Z", "iopub.status.busy": "2026-05-11T06:19:37.096207Z", "iopub.status.idle": "2026-05-11T06:19:37.100116Z", "shell.execute_reply": "2026-05-11T06:19:37.099334Z" } }, "outputs": [], "source": [ "import brainstate\n", "\n", "# Module system\n", "class Net(brainstate.nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " pass\n", "\n", "# Compilation\n", "@brainstate.transform.jit\n", "def fn():\n", " return 0\n", "\n", "# Transformations\n", "# result = brainstate.transform.for_loop(...)" ] } ], "metadata": { "kernelspec": { "display_name": "Ecosystem-py", "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" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "42c8b66c76fc4d14977472fae4788d86": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, 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