Spike frequency adaptation (SFA) is a key feature in biological neurons that allows them to adjust their firing rates based on recent activity, enhancing computational efficiency and energy savings. This review explores SFA in spiking neural networks (SNNs), highlighting its role in improving performance and efficiency. SFA is intrinsic to biological neurons and is modeled in various SNN neuron models, such as the leaky-integrate-and-fire (LIF) and adaptive LIF (ALIF) models. These models incorporate dynamic thresholds to simulate SFA, enabling more efficient and accurate information processing. SFA has been shown to improve computational efficiency in SNNs, particularly in tasks like working memory, speech recognition, and image classification. Adaptive neuron models, including the double exponential adaptive threshold (DEXAT) and multi-time scale adaptive threshold models, offer enhanced performance and flexibility. These models are crucial for neuromorphic computing, where energy efficiency and computational power are paramount. The integration of SFA in hardware, such as neuromorphic circuits and memristive devices, further enhances the practical applications of SNNs. Challenges remain in optimizing SFA for various tasks, including encoding techniques, learning algorithms, and network architecture. Future research aims to address these challenges, leveraging SFA for sustainable and efficient AI systems. The development of adaptive neuron models and their integration into hardware and software frameworks represents a promising direction for advancing neuromorphic computing and AI technologies.Spike frequency adaptation (SFA) is a key feature in biological neurons that allows them to adjust their firing rates based on recent activity, enhancing computational efficiency and energy savings. This review explores SFA in spiking neural networks (SNNs), highlighting its role in improving performance and efficiency. SFA is intrinsic to biological neurons and is modeled in various SNN neuron models, such as the leaky-integrate-and-fire (LIF) and adaptive LIF (ALIF) models. These models incorporate dynamic thresholds to simulate SFA, enabling more efficient and accurate information processing. SFA has been shown to improve computational efficiency in SNNs, particularly in tasks like working memory, speech recognition, and image classification. Adaptive neuron models, including the double exponential adaptive threshold (DEXAT) and multi-time scale adaptive threshold models, offer enhanced performance and flexibility. These models are crucial for neuromorphic computing, where energy efficiency and computational power are paramount. The integration of SFA in hardware, such as neuromorphic circuits and memristive devices, further enhances the practical applications of SNNs. Challenges remain in optimizing SFA for various tasks, including encoding techniques, learning algorithms, and network architecture. Future research aims to address these challenges, leveraging SFA for sustainable and efficient AI systems. The development of adaptive neuron models and their integration into hardware and software frameworks represents a promising direction for advancing neuromorphic computing and AI technologies.