Artificial Neural Networks (ANN) have gained significant popularity thanks to their ability to learn using the well-known backpropagation algorithm. Conversely, Spiking Neural Networks (SNNs), despite having broader capabilities than ANNs, have always posed challenges in the training phase. This paper shows a new method to perform supervised learning on SNNs, using Spike Timing Dependent Plasticity (STDP) and homeostasis, aiming at training the network to identify spatial patterns. Spatial patterns refer to spike patterns without a time component, where all spike events occur simultaneously. The method is tested using the SpiNNaker digital architecture. A SNN is trained to recognise one or multiple patterns and performance metrics are extracted to measure the performance of the network. Some considerations are drawn from the results showing that, in the case of a single trained pattern, the network behaves as the ideal detector, with 100% accuracy in detecting the trained pattern. However, as the number of trained patterns on a single network increases, the accuracy of identification is linked to the similarities between these patterns. This method of training an SNN to detect spatial patterns may be applied to pattern recognition in static images or traffic analysis in computer networks, where each network packet represents a spatial pattern. It will be stipulated that the homeostatic factor may enable the network to detect patterns with some degree of similarity, rather than only perfectly matching patterns.The principles outlined in this article serve as the fundamental building blocks for more complex systems that utilise both spatial and temporal patterns by converting specific features of input signals into spikes.One example of such a system is a computer network packet classifier, tasked with real-time identification of packet streams based on features within the packet content

翻译：人工神经网络（ANN）凭借其利用经典反向传播算法进行学习的能力而广受欢迎。相比之下，脉冲神经网络（SNN）尽管具备比ANN更广泛的能力，但在训练阶段始终面临挑战。本文提出了一种利用脉冲时序依赖可塑性（STDP）与稳态机制在SNN上实现监督学习的新方法，旨在训练网络识别空间模式。空间模式指不含时间成分的脉冲模式，其中所有脉冲事件同时发生。该方法通过SpiNNaker数字架构进行验证。研究训练了一个SNN以识别单个或多个模式，并提取性能指标以评估网络表现。从结果中可得出若干结论：在训练单一模式的情况下，网络表现出理想检测器的特性，对训练模式的检测准确率达到100%。然而，当单个网络训练的模式数量增加时，识别准确率与模式间的相似度密切相关。这种训练SNN检测空间模式的方法可应用于静态图像中的模式识别或计算机网络流量分析，其中每个网络数据包均可视为空间模式。研究指出稳态因子可能使网络能够检测具有一定相似度的模式，而非仅识别完全匹配的模式。本文阐述的原理构成了更复杂系统的基础构建模块，这些系统通过将输入信号的特定特征转换为脉冲，同时利用空间与时间模式。此类系统的一个实例是计算机网络数据包分类器，其任务是基于数据包内容的特征实时识别数据流。