Overview
Rockpool is an open-source Python package focused on dynamic neural network architectures, tailored for event-driven networks and neuromorphic hardware. Managed by SynSense,
Rockpool facilitates the design, training, and evaluation of recurrent neural networks with either continuous-time or event-driven dynamics. The library is designed for
efficiency, enabling fast simulation and training of networks, which is crucial for real-time applications and deployment on low-power neuromorphic hardware.
The framework offers standard modules, tools for working with time series data, as well as specialized training techniques for Jax and Torch networks. It provides an extensive API and
supports various training methods, including gradient descent and adversarial training. Additionally, Rockpool is capable of interfacing with specific types of hardware, such as the
Xylo™ inference processors, Xylo™ Audio, Xylo™ IMU, and DYNAP-SE2 mixed-signal processor, offering resources for quick starting and training networks tailored for these devices.
Rockpool stands out for its user-friendly interface and integration with Python, making it accessible to a broad range of users, from researchers to practitioners in the field of AI
and neuroscience. Furthermore, it also provides tools for analyzing and visualizing neural data, aiding in the understanding of complex network behaviors. Rockpool’s documentation
includes tutorials and covers advanced topics such as computational graphs and graph mapping, parameter handling, performance benchmarks, and a comprehensive API summary. The project
also provides developer documentation, including UML diagrams and notes for backend management. Aimed at simplifying and optimizing the process of designing and deploying neural
networks on various hardware platforms, bridging the gap between dynamic neural modeling and practical application.
