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.

翻译：如今，神经网络已成为人工智能的代名词。现有的神经网络模型虽然功能强大，但在数据和能源效率方面均存在不足。为应对这些问题，学界已提出多种替代形式的神经网络。其中，脉冲神经网络特别适用于高效硬件实现。然而，针对脉冲网络的有效学习算法仍待突破，尽管人们推测有效的可塑性机制可能缓解数据效率问题。本文提出一种基于模块化设计理念的新型脉冲神经网络框架——Spark，该框架支持从基础组件到完整模型的系统性构建。该框架旨在为脉冲神经网络提供高效、精简的开发流程。我们通过结合简单可塑性机制解决稀疏奖励倒立摆问题，展示了该框架的应用潜力。我们期望这种兼容传统机器学习流程的框架能够加速该领域的研究进展，特别是在实现类似动物行为的连续无批次学习方面。