Neuromorphic engineering aims to advance computing by mimicking the brain's efficient processing, where data is encoded as asynchronous temporal events. This eliminates the need for a synchronisation clock and minimises power consumption when no data is present. However, many benchmarks for neuromorphic algorithms primarily focus on spatial features, neglecting the temporal dynamics that are inherent to most sequence-based tasks. This gap may lead to evaluations that fail to fully capture the unique strengths and characteristics of neuromorphic systems. In this paper, we present NeuroMorse, a temporally structured dataset designed for benchmarking neuromorphic learning systems. NeuroMorse converts the top 50 words in the English language into temporal Morse code spike sequences. Despite using only two input spike channels for Morse dots and dashes, complex information is encoded through temporal patterns in the data. The proposed benchmark contains feature hierarchy at multiple temporal scales that test the capacity of neuromorphic algorithms to decompose input patterns into spatial and temporal hierarchies. We demonstrate that our training set is challenging to categorise using a linear classifier and that identifying keywords in the test set is difficult using conventional methods. The NeuroMorse dataset is available at Zenodo, with our accompanying code on GitHub at https://github.com/Ben-E-Walters/NeuroMorse.

翻译：神经形态工程旨在通过模拟大脑的高效处理方式来推进计算，其中数据被编码为异步时间事件。这消除了对同步时钟的需求，并在无数据存在时最大限度地降低了功耗。然而，许多神经形态算法的基准测试主要关注空间特征，忽略了大多数基于序列的任务所固有的时间动态特性。这一差距可能导致评估无法充分捕捉神经形态系统的独特优势和特征。本文提出了NeuroMorse，一个专为神经形态学习系统基准测试设计的时间结构化数据集。NeuroMorse将英语中最常用的50个单词转换为时间莫尔斯电码脉冲序列。尽管仅使用两个输入脉冲通道分别对应莫尔斯电码的点与划，但复杂信息通过数据中的时间模式进行编码。所提出的基准测试包含多个时间尺度上的特征层次结构，用于测试神经形态算法将输入模式分解为空间和时间层次结构的能力。我们证明，使用线性分类器对我们的训练集进行分类具有挑战性，并且使用传统方法在测试集中识别关键词是困难的。NeuroMorse数据集可在Zenodo获取，我们的配套代码位于GitHub：https://github.com/Ben-E-Walters/NeuroMorse。