  Neuromorphic computing is an emerging research field that aims to develop new
intelligent systems by integrating theories and technologies from
multi-disciplines such as neuroscience and deep learning. Currently, there have
been various software frameworks developed for the related fields, but there is
a lack of an efficient framework dedicated for spike-based computing models and
algorithms. In this work, we present a Python based spiking neural network
(SNN) simulation and training framework, aka SPAIC that aims to support
brain-inspired model and algorithm researches integrated with features from
both deep learning and neuroscience. To integrate different methodologies from
the two overwhelming disciplines, and balance between flexibility and
efficiency, SPAIC is designed with neuroscience-style frontend and deep
learning backend structure. We provide a wide range of examples including
neural circuits Simulation, deep SNN learning and neuromorphic applications,
demonstrating the concise coding style and wide usability of our framework. The
SPAIC is a dedicated spike-based artificial intelligence computing platform,
which will significantly facilitate the design, prototype and validation of new
models, theories and applications. Being user-friendly, flexible and
high-performance, it will help accelerate the rapid growth and wide
applicability of neuromorphic computing research.
