Version 0.0.13

What's Changed

• Standardize notebook formatting and enhance load_matfile by @chaoming0625 in #39
• Add comprehensive connectivity module and enhance input generation by @chaoming0625 in #40
• Add input module tutorials and improve connectivity generation by @chaoming0625 in #41
• Standardize docstring code examples and enhance Step duration inference by @chaoming0625 in #42
• Introduce composable connectivity API and new spike processing modules by @chaoming0625 in #43
• Introduce modular, model-specific connectivity with unified API by @chaoming0625 in #44
• Enhance docstrings with examples and detailed descriptions by @chaoming0625 in #45
• Revert "Enhance docstrings with examples and detailed descriptions" by @chaoming0625 in #46
• Refactor conn module, add composable initialization API by @chaoming0625 in #47
• ⬆️ Bump actions/setup-python from 5 to 6 by @dependabot[bot] in #49
• Introduce comprehensive Optax optimizers and LR schedulers by @chaoming0625 in #50
• Add braintools.init module, unify initializers, simplify conn API by @chaoming0625 in #51
• Add topological network patterns; refactor connectivity API by @chaoming0625 in #52
• Introduce unified init, advanced conn patterns, and Optax optimizers by @chaoming0625 in #53

Major Features

New Initialization Framework (braintools.init)

• Unified initialization API consolidating all weight and parameter initialization strategies
• Distance-based initialization: Support for distance-modulated weight patterns
• Variance scaling strategies: Xavier, He, LeCun initialization methods
• Orthogonal initialization for improved training stability
• Composite distributions for complex initialization patterns
• Simplified API with consistent parameter naming across all initializers

Advanced Connectivity Patterns (braintools.conn)

• Topological network patterns:
  • Small-world and scale-free networks
  • Hierarchical and core-periphery structures
  • Modular and clustered random connectivity
• Enhanced biological connectivity:
  • Excitatory-inhibitory balanced networks
  • Distance-dependent connectivity with multiple profiles
  • Compartment-specific connectivity (dendrite, soma, axon)
• Spatial connectivity improvements:
  • 2D convolutional kernels for spatial networks
  • Position-based connectivity with normalization
  • Distance modulation using composable profiles

Comprehensive Optax Integration (braintools.optim)

• Full Optax optimizer support: Adam, SGD, RMSProp, AdaGrad, AdaDelta, and more
• Advanced learning rate schedulers:
  • Cosine annealing with warm restarts
  • Polynomial decay with warmup
  • Piecewise constant schedules
  • Sequential and chained schedulers
• Improved optimizer state management with unique state handling
• Parameter groups with per-group learning rates

Improvements

API Enhancements

• Simplified conn module API with direct class access
• Refactored initialization calls for consistency
• Improved type annotations throughout
• Better default parameter handling

Documentation & Tutorials

• Updated tutorial structure for connectivity patterns
• New examples for topological networks
• Enhanced API documentation with detailed examples
• Improved code readability in tutorials

Code Quality

• Comprehensive test coverage for new features
• Better error handling and validation
• Consistent naming conventions
• Removed deprecated and redundant code

Breaking Changes

• Renamed PointNeuronConnectivity to PointConnectivity
• Renamed ConvKernel to Conv2dKernel
• Unified initializer names (e.g., ConstantWeight → Constant)
• Removed PopulationRateConnectivity class
• Changed some parameter names for clarity (e.g., unified use of rng parameter)

Full Changelog: v0.0.12...v0.0.13