This chapter introduces the concept of slow electronics, a novel paradigm in electronic device design that challenges the prevailing trend of ever-faster processing. Inspired by the ultra-low-power efficiency of biological neural networks, slow electronics aims to develop computational architectures that interact with long-time-scale environmental data in real time. The chapter explores the limitations of conventional artificial neural networks (ANNs), which struggle with high power consumption and inefficiency in processing slow-varying information. To address these challenges, the concept of neuromorphic devices–artificial neurons and synapses with extended time constants–is proposed. Additionally, the chapter discusses spiking neural networks (SNNs) and reservoir computing, which enable real-time adaptation without the computational overhead of traditional deep learning. A key feature of slow electronics is its reliance on attractor dynamics, which allow for efficient learning and inference in long-time-scale data. By integrating these three pillars–neuromorphic devices, environment-responsive architectures, and attractor-based algorithms–slow electronics paves the way for ultra-low-power, human-friendly computing. This approach has profound implications for edge computing, wearable AI, and intelligent assistive technologies, offering a sustainable alternative to cloud-based machine learning.