# Changelog

## 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](https://github.com/chaobrain/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`
   ```python

   # 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
   ```python
   # 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`
   ```python
   # 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