发表机构
University of Tennessee; Cambrya, Inc.（田纳西大学; 坎布里亚公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究在RISP神经处理器上实现了同步环传播网络，用于无监督乐器聚类，达到与DBSCAN相当的精度，且运行速度提升一个数量级以上，证明SLP机制可有效移植到神经形态架构。
AI 中文摘要
神经形态脉冲神经网络（SNNs）为在计算资源或数据受限的任务中提供了一种传统深度神经网络的有前景的替代方案。然而，其在复杂学习任务上的表现相对较弱，限制了实际应用。诸如同步环传播（SLP）等实验方法日益寻求通过更复杂和异质的神经元模型来解决这一问题，并已取得了令人鼓舞的初步成果。然而，这些神经元模型不易直接移植到为支持简单漏电积分发放神经元而设计的标准神经形态系统中。我们展示了在RISP神经处理器（一个事件驱动的神经形态仿真平台）上实现SLP网络，并评估了其在无监督乐器聚类任务上的性能。该网络实现了与DBSCAN算法相当的聚类精度，同时相较于先前的非神经形态SLP实现，在运行速度和计算效率上提升了一个数量级以上。这些结果表明，SLP的核心机制可以有效地转化为神经形态架构，以支持复杂的无监督学习。更广泛地，这项工作凸显了异质且可扩展的神经元模型在扩展神经形态系统设计空间以应对更复杂学习任务方面的潜力。
英文摘要
Neuromorphic spiking neural networks (SNNs) offer a promising alternative to conventional deep neural networks for tasks with computational resource or data constraints. However, their practical applications have been limited by comparatively weak performance on complex learning tasks. Experimental approaches such as Synchrony Loop Propagation (SLP) increasingly seek to address this problem through more sophisticated and heterogeneous neuron models, and have achieved encouraging initial results. However, these neuron models do not readily translate to standard neuromorphic systems designed to support simple leaky integrate-and-fire neurons. We present an implementation of an SLP network on the RISP neuroprocessor, an event-driven neuromorphic simulation platform, and evaluate its performance on an unsupervised musical instrument clustering task. The network achieves clustering accuracy comparable to the DBSCAN algorithm, while providing over an order of magnitude improvement in runtime speed and computational efficiency relative to a prior non-neuromorphic SLP implementation. These results demonstrate that SLP's core mechanisms can be effectively translated into a neuromorphic architecture to support complex unsupervised learning. More broadly, this work highlights the potential of heterogeneous and extensible neuron models to expand the design space of neuromorphic systems to more complex learning tasks.
