The computational resources necessary for training and utilizing traditional deep learning (DL) models are becoming increasingly costly as time progresses. The amount of computing power required has increased tenfold from 2012 to 2019, leading to a significant rise in costs. Neuromorphic technologies and spiking neural networks (SNNs) are inspired by how human brains work and offer an innovative approach, significantly reducing the required resources. The objective of this thesis is to evaluate the potential for the adoption of brain-inspired solutions as a substitute for established techniques used to solve engineering tasks. In particular, this thesis aims to assess the feasibility of employing SNN-based models to enhance the performance of navigation and tracking tasks.