  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
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