Artificial Neural Networks (ANN) have gained large popularity thanks to their ability to learn using the well-known backpropagation algorithm. On the other hand, Spiking Neural Networks (SNNs), despite having wider abilities than ANNs, have always presented a challenge 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. 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 the identification is linked to the similarities between these patterns. This method of training an SNN to detect spatial patterns may be applied on 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 similarities, rather than only perfectly matching patterns.

翻译：人工神经网络（ANN）因其利用著名的反向传播算法进行学习的能力而广受欢迎。另一方面，脉冲神经网络（SNN）尽管比ANN具有更广泛的能力，但在训练阶段始终面临挑战。本文展示了一种使用脉冲时序依赖可塑性（STDP）和稳态方法对SNN进行监督学习的新方法，旨在训练网络识别空间模式。该方法采用SpiNNaker数字架构进行测试。训练SNN以识别一个或多个模式，并提取性能指标以衡量网络性能。从结果中得出一些结论：在仅训练单一模式的情况下，网络表现为理想检测器，对训练模式的检测准确率达到100%。然而，随着单一网络上训练模式数量的增加，识别准确率与这些模式之间的相似性相关。这种训练SNN检测空间模式的方法可应用于静态图像的模式识别或计算机网络中的流量分析，其中每个网络数据包代表一个空间模式。可以推断，稳态因子可能使网络能够检测具有一定相似度的模式，而不仅仅是完全匹配的模式。