Changelog

Contents

Changelog#

Version 2.8.0#

Release Date: June 19, 2026

Version 2.8.0 is a broad correctness, quality, and maintainability release. It bundles an audited, library-wide bug-fix sweep — neuron and synapse dynamics, ODE/SDE/FDE integrators, the math and object-transform layer, dnn layers, optimizers and losses, analysis tooling, and the simulation runners — each accompanied by new regression tests. Alongside the fixes, the release introduces static typing with a mypy CI gate, raises test coverage from roughly 84% to over 92%, co-locates tests with the modules they cover, and removes duplicated internals by reusing the shared braintools and brainstate implementations. Documentation and the bundled examples were repaired and refreshed.

Highlights#

  • Audited correctness sweep across the whole library, each fix backed by regression tests.

  • Static typing: type annotations for core utilities, PEP 561 (py.typed), and a new mypy continuous-integration gate (#834).

  • Test-suite overhaul: coverage raised from ~84% to 92%+ (#836), with tests co-located as <module>_test.py (#832).

  • Reduced duplication: reuse braintools (init, metric, surrogate) and brainstate transforms instead of maintaining forked copies (#835, #833, #831).

Bug Fixes#

Neuron and synapse dynamics#

  • CondNeuGroup: corrected synaptic-current scaling that attenuated currents by ~1000× (#842).

  • DualExp: correct handling of equal time constants, plus per-neuron STP reset (#847).

  • dynold: fixed STP construction, sparse-synapse drift, plasticity decay, and Bellec initialization (#848).

  • Rate models: fixed RNNCell.reset_state crash and ThresholdLinearModel noise dt-scaling (#850).

Integrators#

  • RKF45 node handling and Kl/Pl SRK diffusion weights (#841).

  • Fractional integrators: CaputoEuler.reset state desynchronization and set_default_fdeint (#846).

  • Prevented a MilsteinGradFree out-of-memory crash (#837).

Math, sparse, and object transforms#

  • coo_to_csr bounds checking, plus a sparse/event/delay regression suite (#845).

  • Compatibility fixes: gelu integer input, unflatten negative dimension, segment_mean on Array (#844).

  • Object transforms: cond/ifelse, collectors, and VariableView edge cases (#840).

  • ShardedArray pytree flattening and remove_diag guard (#839).

Neural-network layers#

  • BatchNorm running-variance bias, pooling channel_axis, and LayerNorm error handling (#843).

Optimizers and losses#

  • Audited correctness fixes for Adan, SM3, and several loss functions (#838).

Analysis#

  • Fixed-point classification, nullcline selection, gradient-descent batching, and plotting keyword handling (#849).

Runtime and runners#

  • DSRunner: memory_efficient output/monitors and eager bound-validation checks (#851).

  • IntegratorRunner: dict-monitor regression (#854).

  • Runners: jit-dict mutation, dict string monitors, and multi-device parallel concatenation (#852).

  • Inputs / algorithms / connectivity: deprecation aliases, regression fits, CSR guard, and dtype handling (#853).

  • Earlier audited correctness, API-drift, and edge-case fixes with a regression and coverage suite (#830).

Quality and Tooling#

  • Added typing for core utilities, enabled PEP 561, and added a mypy CI job (#834).

  • Comprehensive coverage tests, raising coverage from ~84% to 92%+ (#836).

  • Co-located tests as <module>_test.py and removed the legacy tests/ trees (#832).

  • Reused braintools.init / braintools.metric in brainpy.initialize and losses (#835).

  • Reused braintools.surrogate and removed the duplicate brainpy.math.surrogate (#833).

  • CI: replaced remove_vmap with brainstate.transform.unvmap and resolved brainstate 0.5.1 API drift (#831); green-lighted the Dense fit-flag tracer, buffer-donation test pollution, and L1 loss contract, and restored the Codecov token (#855).

Documentation and Examples#

  • Converted public-API docstrings to NumPy style and enforced the convention (#856).

  • Repaired documentation notebooks and fixed Hz-input and RNNCell.reset(batch_size) bugs (#857).

  • Repaired six broken example scripts — API drift, data paths, and channel handling (#858).

  • Updated documentation links and the Python-version requirement in README.md; added .gitattributes for line endings.

  • Served the project logo as WebP from brainx.chaobrain.com.

Dependencies#

  • Bumped sphinx to >=9.0.4,<9.1.0 (#827), sphinx-book-theme to >=1.2.0 (#826), and jupyter-sphinx to >=0.5.3 (#825).

Version 2.7.8#

Release Date: April 18, 2026

This maintenance release focuses on JAX compatibility cleanup, packaging improvements for brainevent, and documentation streamlining. It also removes duplicated internal JAXPR source-conversion code by relying on the shared brainstate implementation.

Improvements#

JAX Compatibility and Internal Refactoring#

  • Updated: Replaced version-specific JAX imports with brainstate._compatible_import across math compatibility, backend environment utilities, surrogate operators, and vmap-removal helpers

    • Centralizes compatibility handling for Primitive and backend imports across newer JAX releases

  • Updated: brainpy.integrators now imports jaxpr_to_python_code from brainstate.transform

    • Removed the duplicated local brainpy.integrators._jaxpr_to_source_code implementation

    • Keeps integrator math-expression generation aligned with the shared brainstate implementation

  • Updated: bm.trapz now consistently maps to jax.scipy.integrate.trapezoid

Packaging#

  • Updated: Optional install extras now include matching brainevent extras

    • brainpy[cpu] installs brainevent[cpu]

    • brainpy[cuda12] installs brainevent[cuda12]

    • brainpy[cuda13] installs brainevent[cuda13]

    • brainpy[tpu] installs brainevent[tpu]

  • Impact: Simplifies accelerator-specific installation and keeps event-backend dependencies aligned with the selected JAX platform

Documentation#

  • Added: Direct link to the external brainpy.state API reference in the docs API index (#822)

  • Removed: Legacy brainpy and brainpylib historical changelog pages from the main documentation navigation

  • Removed: Advanced operator-customization tutorial notebooks for CuPy, Numba, and Taichi from the documentation tree

  • Updated: Refreshed quickstart simulation notebook content and documentation structure around brainpy.state

Testing and CI/CD#

  • Improved: Disabled JAX traceback filtering in brainpy.dnn linear tests to make failures easier to diagnose

  • Updated: Raised the Sphinx upper bound for documentation builds from <8.2.0 to <9.1.0 (#820)

  • Updated: Bumped styfle/cancel-workflow-action from 0.13.0 to 0.13.1 in CI (#819)

Version 2.7.7#

Release Date: March 12, 2026

This release migrates to the new brainevent binary backend API, fixes numerous bugs across neuron models, synapse dynamics, ODE integrators, and object transforms, and raises the minimum Python version to 3.11.

Breaking Changes#

Python Version Requirement#

  • Raised: Minimum Python version from 3.10 to 3.11

    • Dropped Python 3.10 support from classifiers and build metadata

brainevent API Migration (#817)#

  • Updated: Event-driven operations to use the new brainevent binary backend

    • EventArray replaced by BinaryArray in all event CSR and jitconn operations

    • JITCHomoR replaced by JITCScalarR for homogeneous JIT connectivity

    • STDP weight update functions renamed:

      • dense_on_pre / dense_on_post → update_dense_on_binary_pre / update_dense_on_binary_post

      • csr_on_pre / csr2csc_on_post → update_csr_on_binary_pre / update_csr_on_binary_post

    • Affected layers: Dense, AllToAll, MaskedLinear, and all CSR-based layers

Bug Fixes#

Neuron Models#

  • Fixed: All LIF-family neuron models (LifLTC, LifRefLTC, ExpIFLTC, ExpIFRefLTC, AdExIFLTC, AdExIFRefLTC, QuaIFLTC, QuaIFRefLTC, AdQuaIFLTC, AdQuaIFRefLTC, GifLTC, GifRefLTC) now raise descriptive ValueError messages for invalid spk_reset modes instead of bare ValueError

Synapse Models#

  • Fixed: Synaptic variable updates in DualExpon, Alpha, and NMDA models

    • Replaced in-place += operator with explicit .value assignment to prevent tracing issues under JAX transformations

ODE Integrators#

  • Fixed: BogackiShampine adaptive Runge-Kutta coefficient typo — corrected '4/0' to '4/9' in the Butcher tableau

  • Fixed: set_default_odeint was incorrectly assigning to _DEFAULT_ODE_METHOD instead of _DEFAULT_DDE_METHOD

Object Transforms#

  • Fixed: Variable.varshape used self.batch_size instead of self.batch_axis when computing non-batch shape dimensions

  • Fixed: NodeDict.update() and VarDict.update() referenced args[1] instead of arg[1] when updating from tuple arguments

  • Fixed: NodeDict.__setitem__ duplicate key error message incorrectly displayed the new value instead of the existing one

Training#

  • Fixed: BPTrainer did not raise NoLongerSupportError when seed parameter was passed (missing raise keyword)

  • Fixed: BPTrainer metric aggregation switched from jnp.mean(bm.as_jax(...)) to np.mean(np.asarray(...)) to avoid unnecessary JAX tracing overhead

Core#

  • Fixed: ShardedArray.value setter used _check_tracer() instead of direct _value access

  • Fixed: _slice_to_num did not handle negative step values and raised no error on zero step

  • Fixed: load_state crashed with KeyError when state dict contained missing keys; now correctly reports them as missing

Code Quality#

  • Improved: Narrowed bare except clauses to specific exception types (ValueError, TypeError, ImportError, ModuleNotFoundError) in convolution layers and Flax interoperation module

Dependencies#

  • Updated: brainevent from >=0.0.4 to >=0.0.7

  • Updated: braintools version spec corrected to >=0.0.9

  • Updated: numpy minimum version set to >=1.15

  • Updated: brainpy_state from >=0.0.2 to >=0.0.3

CI/CD#

  • Updated: actions/upload-artifact from v6 to v7 (#815)

  • Updated: actions/download-artifact from v7 to v8 (#816)

Version 2.7.6#

Release Date: January 21, 2026

This is a maintenance release that enhances JAX compatibility and improves CI stability across platforms.

Bug Fixes#

JAX 0.9.0 Compatibility (#813)#

  • Fixed: ODE integrator default time parameter handling

    • Ensured t keyword argument defaults to 0 in _call_integral method

    • Prevents errors when time parameter is not explicitly provided

  • Updated: Backend imports for compatibility with JAX >= 0.8.0

    • Updated brainpy.math.environment module to handle JAX backend changes

    • Improved compatibility layer for future JAX versions

ODE Integrators#

  • Fixed: Explicit Runge-Kutta methods formatting in build method

  • Impact: Ensures stable numerical integration across different JAX versions

Dependencies#

Updated Dependencies#

  • Updated: brainpy_state from 0.0.1 to 0.0.3

  • Enhanced: README documentation with comprehensive module overview and installation instructions

Version 2.7.5#

Release Date: December 25, 2025

This release focuses on improving JAX compatibility and code quality through comprehensive refactoring.

Major Changes#

JAX Compatibility Enhancement (#809)#

  • Updated: Refined JIT wrappers for compatibility with JAX >= 0.8.2

    • Refactored JIT handling across 85 files

    • Updated object transformation modules for new JAX behavior

    • Improved JIT compilation stability and performance

  • Added: Initial brainpy_state module infrastructure

    • Created new state management module with README documentation

    • Set up module structure for future state-based functionality

  • Updated: JAX backend integration

    • Modernized backend import patterns

    • Enhanced compatibility with JAX’s evolving API

Version 2.7.4#

Release Date: December 2025

This release focuses on simplifying the project structure by removing the experimental brainpy.state module and consolidating documentation.

Major Changes#

Removed brainpy.state Module (#806)#

  • Removed: The entire brainpy.state module has been deleted

    • This includes all state-based neuron models (LIF variants, Izhikevich, HH)

    • Removed synapse models, projections, readouts, and STP implementations

    • Removed all associated test files

  • Recommendation: Users should use the brainpy.state library directly for state-based neural network simulations

Decouple the brainpy context with brainstate context#

  • Updated: brainpy.math.defaults is totally decoupled with brainstate context management

Version 2.7.3#

Release Date: December 2025

This is a bug fix release that resolves critical issues with bm.for_loop and improves CI stability.

Bug Fixes#

bm.for_loop jit Parameter Fix#

  • Fixed: The jit parameter in bm.for_loop was accepted but never used - passing jit=False had no effect

  • Implementation: When jit=False, the call is now properly wrapped in jax.disable_jit() context manager

  • Impact: Users can now debug code with jit=False to see actual values instead of JIT-compiled traces

Zero-Length Scan Fix#

  • Fixed: ValueError: zero-length scan is not supported in disable_jit() mode when using jit=False with zero-length inputs

  • Implementation: Automatically falls back to JIT mode for zero-length inputs with a warning

  • Impact: Prevents crashes when DSRunner.run(duration) results in 0 time steps (e.g., duration=0.5, dt=1.0)

Progress Bar Enhancement#

  • Enhanced: progress_bar parameter in bm.for_loop() and bm.scan() now supports advanced customization

  • New Features:

    • Accept ProgressBar instances for fine-grained control (freq, desc, count parameters)

    • Accept integers as shorthand for frequency (e.g., progress_bar=10 means update every 10 iterations)

    • Full backward compatibility with existing progress_bar=True/False usage

  • Export: Added bm.ProgressBar for easy access (from brainpy.math import ProgressBar)

  • Impact: Aligns with brainstate API and enables better progress tracking customization

Parameter Cleanup#

  • Removed: Unused parameters remat and unroll_kwargs from bm.for_loop()

  • Backward Compatibility: remat parameter kept in LoopOverTime.__init__() with deprecation warning

  • Fixes: Resolved TypeErrors in DSRunner and LoopOverTime that used these parameters

Version 2.7.2#

Release Date: October 16, 2025

This is a maintenance release that improves JAX compatibility and documentation.

Bug Fixes#

JAX Compatibility#

  • Updated: Made compatible with JAX >= 0.8.0

  • Fixed: Updated imports and API usage for latest JAX versions

  • Impact: Ensures BrainPy works correctly with the latest JAX releases

Improvements#

Documentation#

  • Updated documentation and CI configuration for better clarity

  • Standardized test paths across the project

  • Improved core concepts documentation

  • Enhanced LIF neuron dynamics documentation (#800)

  • Fixed documentation bugs

Neural Network Classes#

  • Refactored neural network classes for better maintainability

  • Updated progress bar parameters for simulations

  • Improved code organization and structure

Version 2.7.1#

Release Date: October 2025

This is a feature release that introduces new neuron and synapse models in the state-based API (brainpy.state) and enhances the Dynamics base class with improved input handling.

Major Changes#

New Neuron Models (brainpy.state)#

  • LIF (Leaky Integrate-and-Fire) Variants: Added comprehensive set of LIF neuron models

    • LIF: Basic LIF neuron with exponential synaptic input

    • LifRef: LIF with refractory period

    • ExpIF: Exponential Integrate-and-Fire neuron

    • ExpIFRef: ExpIF with refractory period

    • AdExIF: Adaptive Exponential Integrate-and-Fire neuron

    • AdExIFRef: AdExIF with refractory period

    • QuaIF: Quadratic Integrate-and-Fire neuron

    • QuaIFRef: QuaIF with refractory period

    • AdQuaIF: Adaptive Quadratic Integrate-and-Fire neuron

    • AdQuaIFRef: AdQuaIF with refractory period

    • GifRef: Generalized Integrate-and-Fire with refractory period

  • Izhikevich Neuron Models: Added new Izhikevich neuron implementations

    • Izhikevich: Basic Izhikevich neuron model

    • IzhikevichRef: Izhikevich with refractory period

  • Hodgkin-Huxley Model: Added classic biophysical neuron model

    • HH: Classic Hodgkin-Huxley model with Na+ and K+ channels

New Synapse Models (brainpy.state)#

  • BioNMDA: Biological NMDA receptor with second-order kinetics

    • Implements two-state cascade dynamics (x and g variables)

    • Slower rise time compared to AMPA (biologically realistic)

    • Comprehensive documentation with mathematical formulation

Features#

Model Implementation#

  • All new models use the brainstate ecosystem (HiddenState, ShortTermState, LongTermState)

  • Proper unit support with brainunit integration

  • Exponential Euler integration for numerical stability

  • Batch processing support across all models

  • Consistent API design following BrainPy v2.7+ architecture

Dynamics Class Enhancements#

  • Enhanced input handling capabilities in the Dynamics base class

  • Added new properties for better state management

  • Improved integration with brainstate framework

  • Refactored to use public methods instead of private counterparts for clarity

Documentation#

  • Added comprehensive Examples sections to all neuron classes in _lif.py

  • Each example includes:

    • Import statements for required modules

    • Basic usage with parameter specifications

    • State initialization examples

    • Update and spike generation examples

    • Network integration with brainstate.nn.Sequential

    • Notes highlighting key features

  • All 13 neuron classes in _lif.py now have complete documentation

  • Simplified documentation paths by removing ‘core-concepts’ and ‘quickstart’ prefixes in index.rst

Bug Fixes#

  • Fixed import paths in _base.py: changed references from brainstate to brainpy for consistency (057b872d)

  • Fixed test suite issues (95ec2037)

  • Fixed test suite for proper unit handling in synapse models

Notes#

  • This release significantly expands the brainpy.state module with biologically realistic neuron and synapse models

  • All new models are fully compatible with the brainstate ecosystem

  • Enhanced documentation provides clear usage examples for all models

  • The Dynamics class refactoring improves the foundation for future state-based model development

Version 3.0.1#

Release Date: October 2025

This is a patch release focusing on documentation improvements and module structure cleanup following the 3.0.0 release.

Major Changes#

Module Renaming#

  • BREAKING CHANGE: Renamed brainpy.state_based module to brainpy.state

    • All functionality previously in brainpy.state_based is now accessible via brainpy.state

    • Users should update imports from brainpy.state_based to brainpy.state

    • This change provides a cleaner, more intuitive API structure

Code Structure Cleanup#

  • Removed brainpy.version2 module: All BrainPy 2.x functionality has been consolidated

    • The version2 namespace has been removed from the codebase

    • All version2 functionality is now directly accessible through the main brainpy module

    • Version-specific imports are no longer needed

Documentation#

Documentation Reorganization#

  • Renamed docs_version2 to docs_classic for BrainPy 2.x documentation

  • Renamed docs_state_based to docs_state for BrainPy 3.x documentation

  • Renamed examples_version2 to examples_classic for consistency

  • Renamed examples_state_based to examples_state for clarity

Documentation Updates#

  • Updated all documentation references to use brainpy.state instead of brainpy.state_based (#791, #790)

  • Updated API documentation structure for improved clarity

  • Simplified API reference pages by removing redundant content

  • Updated card links and descriptions for brainpy.state APIs

  • Improved quickstart tutorial (5min-tutorial.ipynb) with clearer examples

  • Updated core concepts documentation to reflect new module structure

  • Enhanced tutorials with corrected module references

  • Updated all example files to use new module structure

Examples Updates#

  • Updated simulation examples (EI networks, COBA, CUBA models) to use new API

  • Updated training examples (surrogate gradient training, MNIST models) with correct imports

  • Updated gamma oscillation examples with proper module references

Bug Fixes#

Testing#

  • Removed redundant test for abstract Neuron class that was causing conflicts (d06bb47f)

Migration Guide#

For users upgrading from BrainPy 3.0.0:

  1. Update module imports: Replace brainpy.state_based with brainpy.state

    
# New code (BrainPy 3.0.1)
from brainpy.state import LIF, Expon

  2. Remove version2 references: If you were using brainpy.version2, migrate to the main brainpy module

    # Old code (not recommended)
import brainpy.version2 as bp

# New code
import brainpy as bp

  3. Update documentation references: If you’re linking to documentation, use the new paths:

    • Classic docs: docs_classic/ (formerly docs_version2/)

    • State-based docs: docs_state/ (formerly docs_state_based/)

Notes#

  • This release maintains full backward compatibility with BrainPy 3.0.0 except for the module naming changes

  • The brainpy.state_based to brainpy.state rename provides a cleaner API and better reflects the module’s purpose

  • Documentation is now better organized with clear separation between classic (2.x) and state-based (3.x) APIs

Version 3.0.0#

Release Date: October 2025

This is a major release with significant architectural changes and improvements. BrainPy 3.0.0 introduces a new API design while maintaining backward compatibility through the brainpy module.

Major Changes#

Architecture Reorganization#

  • BREAKING CHANGE: All existing BrainPy 2.x functionality has been moved to brainpy module

    • Users can migrate existing code by replacing import brainpy with import brainpy as brainpy

    • The old brainpy._src module structure has been completely reorganized into brainpy

    • All submodules (math, dyn, dnn, etc.) are now under brainpy.*

New Core API (brainpy.*)#

  • Introduced simplified, streamlined API in the main brainpy namespace

  • New core modules include:

    • Base classes for neurons and synapses

    • LIF (Leaky Integrate-and-Fire) neuron models

    • Exponential synapse models

    • Synaptic projection modules

    • Short-term plasticity (STP) models

    • Input current generators

    • Readout layers

    • Error handling utilities

Dependencies#

  • Updated: brainstate>=0.2.0 (was >=0.1.0)

  • Updated: brainevent>=0.0.4 (new requirement)

  • Updated: braintools>=0.0.9 (integrated into brainpy)

  • Removed: Hard dependency on taichi and numba - now optional

  • Updated: JAX compatibility improvements for version 0.5.0+

Features#

Integration of Brain Ecosystem Libraries#

  • Integrated brainstate for state management (#763)

  • Integrated brainevent for event-driven computations (#771)

  • Integrated braintools utilities and formatting (#769)

Math Module Enhancements (version2.math)#

  • Added event-driven sparse matrix @ matrix operators (#613)

  • Added ein_rearrange, ein_reduce, and ein_repeat functions (#590)

  • Added unflatten function and Unflatten layer (#588)

  • Added JIT weight matrix methods (Uniform & Normal) for dnn.linear (#673)

  • Added JIT connect matrix method for dnn.linear (#672)

  • Replaced math operators with braintaichi for better performance (#698)

  • Support for custom operators using CuPy (#653)

  • Taichi operators as default customized operators (#598)

  • Enhanced taichi custom operator support with GPU backend (#655)

  • Support for more than 8 parameters in taichi GPU operator customization (#642)

  • Rebased operator customization using MLIR registration interface (#618)

  • Added transparent taichi caches with clean caches function (#596)

  • Support for taichi customized op with metal CPU backend (#579)

  • Improved variable retrieval system (#589)

Deep Learning (version2.dnn)#

  • Improved error handling in dnn/linear module (#704)

  • Enhanced activation functions and layers

Dynamics (version2.dyn)#

  • Refactored STDP weight update logic requiring brainevent>=0.0.4 (#771)

  • Fixed STDP and training workflows for JAX compatibility (#772)

  • Enhanced dual exponential synapse model with normalize parameter

  • Improved alpha synapse implementation

  • Added clear_input in the step_run function (#601)

Integrators (version2.integrators)#

  • Support for Integrator.to_math_expr() (#674)

  • Fixed dtype checking during exponential Euler method

  • Added disable_jit support in brainpy.math.scan (#606)

  • Fixed brainpy.math.scan implementation (#604)

Optimizers (version2.optim)#

  • Fixed AdamW optimizer initialization where “amsgrad” was used before being defined (#660)

Tools & Utilities (version2.tools)#

  • Added brainpy.tools.compose and brainpy.tools.pipe functions (#624)

Bug Fixes#

JAX Compatibility#

  • Updated JAX import paths for compatibility with version 0.5.0+ (#722)

  • Fixed compatibility issues with latest JAX versions (#691, #708, #716)

  • Replaced jax.experimental.host_callback with jax.pure_callback (#670)

  • Fixed test_ndarray.py for latest JAX version (#708)

Math & Operations#

  • Fixed CustomOpByNumba with multiple_results=True (#671)

  • Updated CustomOpByNumba to support JAX version >= 0.4.24 (#669)

  • Fixed brainpy.math.softplus and brainpy.dnn.SoftPlus (#581)

  • Fixed bugs in truncated_normal and added TruncatedNormal initialization (#583, #584, #585, #574, #575)

  • Fixed autograd functionality (#687)

  • Fixed order of return values in __load_state__ (#749)

Delay & Timing#

  • Fixed delay bugs including DelayVar in concat mode (#632, #650)

  • Fixed wrong randomness in OU process input (#715)

UI & Progress#

  • Fixed progress bar display and update issues (#683)

  • Fixed incorrect verbose of clear_name_cache() (#681)

Python Compatibility#

  • Replaced collections.Iterable with collections.abc.Iterable for Python 3.10+ (#677)

  • Fixed surrogate gradient function for numpy 2.0 compatibility (#679)

Interoperability#

  • Fixed Flax RNN interoperation (#665)

  • Fixed issue with external library integration (#661, #662)

Exception Handling#

  • Fixed exception handling for missing braintaichi module in dependency check (#746)

Testing & CI#

Python Support#

  • Added CI support for Python 3.12 (#705)

  • Added CI support for Python 3.13

  • Updated supported Python versions: 3.10, 3.11, 3.12, 3.13

CI Improvements#

  • Updated GitHub Actions:

    • actions/setup-python from 5 to 6 (#783)

    • actions/checkout from 4 to 5 (#773)

    • actions/first-interaction from 1 to 3 (#782)

    • actions/labeler from 5 to 6 (#781)

    • actions/download-artifact from 4 to 5 (#780)

    • actions/stale from 9 to 10 (#779)

    • docker/build-push-action from 5 to 6 (#678)

  • Added greetings workflow and labeler configuration

  • Enhanced issue templates and CI configurations

Documentation#

Major Documentation Overhaul#

  • Introduced new BrainPy 3.0 documentation and tutorials (#787)

  • Added comprehensive documentation and examples for BrainPy 3.x (#785)

  • Updated documentation links for BrainPy 3.0 and 2.0 (#786)

  • Implemented dynamic configuration loading for Read the Docs (#784)

  • Added Colab and Kaggle links for documentation notebooks (#614, #619)

  • Added Chinese version of operator_custom_with_cupy.ipynb (#659)

  • Fixed various documentation build issues and path references

Citation & Acknowledgments#

  • Added BrainPy citation information (#770)

  • Updated ACKNOWLEDGMENTS.md

Installation#

  • Refined installation instructions (#767)

  • Updated docstring and parameter formatting (#766)

  • Updated README with ecosystem information

Performance & Memory Management#

  • Enabled clear_buffer_memory() to support clearing array, compilation, and names (#639)

  • Cleaned taichi AOT caches and enabled numpy_func_return setting (#643)

  • Made taichi caches more transparent (#596)

  • Enabled BrainPy objects as pytree for direct use with jax.jit (#625)

Object-Oriented Transformations#

  • Standardized and generalized object-oriented transformations (#628)

Development & Contributing#

  • Updated CONTRIBUTING.md with new guidelines

  • Added CODEOWNERS file

  • Updated SECURITY.md

  • License updated to Apache License 2.0

Removed#

  • Removed Docker workflow

  • Removed hard dependencies on taichi and numba (#635)

  • Removed op register functionality (#700)

  • Removed deprecated deprecation files and old module structure

  • Removed unnecessary dependencies (#703)

Migration Guide#

For users upgrading from BrainPy 2.6.x:

  1. Keep using BrainPy 2.x API: Replace imports with brainpy

    # Old code (BrainPy 2.x)
import brainpy as bp

# New code (BrainPy 3.0 with backward compatibility)
import brainpy as bp

  2. Adopt new BrainPy 3.0 API: Explore the simplified API in the main brainpy namespace for new projects

  3. Update dependencies: Ensure brainstate>=0.2.0, brainevent>=0.0.4, and braintools>=0.0.9 are installed

  4. Review breaking changes: Check if your code uses any of the reorganized internal modules

Notes#

  • This release maintains backward compatibility through brainpy

  • The new API in the main brainpy namespace represents the future direction of the library

  • Documentation for both versions is available on Read the Docs

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