In recent years, the presence of artificial intelligence (AI) has become increasingly pervasive, with artificial neural networks (ANNs) being applied across a growing number of domains. These models, while powerful, are also becoming larger and more computationally demanding. In addition, an interest has emerged in understanding and emulating the remarkable efficiency and compactness of the human brain. This has led to the rise of neuromorphic computing, which aims to design lightweight and energy-efficient systems inspired by biological neurons, through spiking neural networks (SNNs). By combining ideas from neuroscience and machine learning, neuromorphic computing offers a way to reinterpret traditional AI models in a more biologically grounded way.