提出模块化脉冲神经网络框架Spark，提升学习效率与硬件适配性。
Spark: Modular Spiking Neural Networks
- 采用模块化设计，从基础单元构建完整脉冲网络模型。
- 在稀疏奖励的倒立摆任务中仅用简单可塑性机制实现成功训练。
- 适合连续、非批处理学习研究，类比动物学习方式。
当前神经网络虽强大，但在数据和能耗方面效率低下。脉冲神经网络因其适合高效硬件实现而受到关注，但有效的学习算法仍难以获得，尽管有研究认为合理的可塑性机制或可改善数据效率问题。本文提出一种名为Spark的新框架，基于模块化设计理念，由简单组件逐步构建完整模型，旨在为脉冲神经网络提供高效、简洁的训练流程。通过在稀疏奖励的倒立摆（sparse-reward cartpole）任务中使用简单可塑性机制实现成功控制，验证了该框架的有效性。我们希望这一兼容传统机器学习流程的框架能推动持续性、非批处理学习的研究，更接近生物体的学习模式。
原文摘要 · Abstract (English)
Nowadays, neural networks act as a synonym for artificial intelligence. Present neural network models, although remarkably powerful, are inefficient both in terms of data and energy. Several alternative forms of neural networks have been proposed to address some of these problems. Specifically, spiking neural networks are suitable for efficient hardware implementations. However, effective learning algorithms for spiking networks remain elusive, although it is suspected that effective plasticity mechanisms could alleviate the problem of data efficiency. Here, we present a new framework for spiking neural networks - Spark - built upon the idea of modular design, from simple components to entire models. The aim of this framework is to provide an efficient and streamlined pipeline for spiking neural networks. We showcase this framework by solving the sparse-reward cartpole problem with simple plasticity mechanisms. We hope that a framework compatible with traditional ML pipelines may accelerate research in the area, specifically for continuous and unbatched learning, akin to the one animals exhibit.
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