Neuromorphic computing is an emerging research field that aims to develop new intelligent systems by integrating theories and technologies from multiple disciplines, such as neuroscience, deep learning and microelectronics. Various software frameworks have been developed for related fields, but an efficient framework dedicated to spike-based computing models and algorithms is lacking. In this work, we present a Python-based spiking neural network (SNN) simulation and training framework, named SPAIC, that aims to support brain-inspired model and algorithm research integrated with features from both deep learning and neuroscience. To integrate different methodologies from multiple disciplines and balance flexibility and efficiency, SPAIC is designed with a neuroscience-style frontend and a deep learning-based backend. Various types of examples are provided to demonstrate the wide usability of the framework, including neural circuit simulation, deep SNN learning and neuromorphic applications. As a user-friendly, flexible, and high-performance software tool, it will help accelerate the rapid growth and wide applicability of neuromorphic computing methodologies.

中文翻译：

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SPAIC：基于Spike的人工智能计算框架


神经形态计算是一个新兴的研究领域，旨在通过整合神经科学、深度学习和微电子学等多学科的理论和技术来开发新的智能系统。相关领域已经开发了各种软件框架，但缺乏专用于基于尖峰的计算模型和算法的高效框架。在这项工作中，我们提出了一个基于 Python 的尖峰神经网络 (SNN) 模拟和训练框架，名为 SPAIC，旨在支持集成了深度学习和神经科学特征的大脑启发模型和算法研究。为了整合来自多个学科的不同方法并平衡灵活性和效率，SPAIC 设计有神经科学风格的前端和基于深度学习的后端。提供了各种类型的示例来展示该框架的广泛可用性，包括神经电路模拟、深度 SNN 学习和神经形态应用。作为一种用户友好、灵活、高性能的软件工具，它将有助于加速神经形态计算方法的快速发展和广泛适用。