A SNN-Based Implementation of a Spiking Counter for Filtering and Processing Spike Trains in Real Time

In recent years, neuromorphic engineering has emerged to emulate and understand the functioning of the biological nervous system, which governs the behavior of living beings. With the claim to exploit its real-time capability and low power consumption, a new generation of artificial neural networks, Spiking Neural Networks, has appeared, which is more biologically plausible. Previous work has discussed the possibility to implement a spiking computer that would benefit from these features, for which basic spiking components were implemented using this type of networks. This work focuses on the implementation of a spiking counter using these building blocks and on the advantages it can bring to the design of spiking applications. A practical example based on the processing of ECG signals is also shown to filter QRS complexes, proving the correct operation of the implemented spiking counter.