An Overview on Applications of Spiking Neural Networks and Spiking Neural P Systems

Spiking Neural Networks (SNNs) and Spiking Neural P Systems (SNPSs) are advanced computational models inspired by the biological processes of the human brain. SNNs use discrete spikes to transmit information between neurons, offering a more energy-efficient and biologically plausible approach compared to traditional neural networks. These networks are particularly well-suited for resource-constrained environments, processing temporal information with reduced energy consumption. On the other hand, SNPSs combine the principles of SNNs with membrane computing, enabling efficient distributed computing and complex problem-solving. Both models are capable of modeling biological neural processes and offer advantages in tasks such as image recognition, classification, and diagnostics. Their sparse, asynchronous activity and ability to adapt through spike-timing dependent plasticity make them a promising tool for a range of applications, from artificial intelligence to medical fields. Despite some challenges, particularly in training algorithms, these models present exciting opportunities for more efficient and biologically inspired computational systems.