Felice

This project provides a JAX implementation of the different neuron models in felice

Overview

The framework is built on top of diffrax and leverages JAX's automatic differentiation for efficient simulation and training of analogue models.

Key Features

• Delay learning
• WereRabbit Neuron Model: Implementation of a dual-state oscillatory neuron model with bistable dynamics

Installation

Felice uses uv for dependency management. To install:

uv sync

CUDA Support (Optional)

For GPU acceleration with CUDA 13:

uv sync --extra cuda

Neuron Models

WereRabbit

The WereRabbit neuron model implements a bistable oscillatory system with dual-state dynamics (x1, x2). Key characteristics:

• Bistable dynamics: Two stable states with smooth transitions
• Event-based spiking: Spikes detected when system reaches a fixpoint
• Configurable parameters: Bias current, scaling distance, and tolerance settings for the fixpoint calculation

Configuration parameters:

• Ibias: Bias current (default: 300e-12 A)
• scaling_distance: Scaling factor for state attraction (default: 0.6)
• rtol, atol: Relative and absolute tolerances for spike detection

Development

Running Tests

uv run pytest tests

Building Documentation

Documentation is built with MkDocs:

cd docs-site && uv run mkdocs serve

Code Quality

The project uses pre-commit hooks for code formatting and linting with Ruff:

uv run pre-commit install
uv run pre-commit run --all-files

Requirements

• Python >= 3.13
• JAX >= 0.8.1
• Equinox >= 0.13.2
• Diffrax >= 0.7.0

See pyproject.toml for the complete list of dependencies.

Contributing

Contributions are welcome! Please ensure:

1. Code passes all tests (pytest)
2. Code is formatted with Ruff (pre-commit run --all-files)
3. Type annotations are included for new functions

License

• Code is licensed under the MIT License.
• Documentation is licensed under CC BY 4.0.