Explainability Techniques and Training Strategies for Spiking Neural Networks

Spiking Neural Networks (SNNs) have emerged as a promising class of biologically inspired models that process information through discrete spike events and temporal dynamics, closely emulating neural computation in the brain.  Their inherent advantages include energy-efficient processing, event-driven operation, and natural suitability for neuromorphic hardware, positioning them as strong candidates for next-generation artificial intelligence systems.  However, despite significant advancements, challenges remain in training SNNs effectively, understanding their internal decision-making processes, and deploying them in practical applications.  This survey provides a comprehensive and detailed overview of the state-of-the-art in explainable and effective Spiking Neural Networks, addressing fundamental principles, training algorithms, neuromorphic hardware integration, and interpretability methodologies.  We systematically explore how recent developments in surrogate gradient techniques, biologically plausible learning rules, and hybrid architectures have improved the accuracy and efficiency of SNNs, while maintaining or enhancing model transparency.  Furthermore, we review a broad spectrum of explainability approaches specifically tailored to the temporal and event-driven nature of SNNs, including spike visualization, saliency mapping, rule extraction, and biologically grounded interpretability frameworks.  Key application domains are surveyed, highlighting successes and challenges in neuromorphic vision, brain-machine interfaces, robotics, and biomedical signal processing, where the combination of explainability and effectiveness is critical for trustworthiness and deployment.  Finally, we discuss open challenges related to training complexity, standardization of interpretability metrics, hardware-software co-design, and the balance between biological realism and engineering practicality, proposing future research directions to overcome these barriers.  By synthesizing current knowledge and identifying promising avenues, this survey aims to guide researchers and practitioners in advancing the development of Spiking Neural Networks that are not only high-performing but also transparent, interpretable, and suitable for real-world applications.