The brain orchestrates complex physiological processes through intricate neural networks, with synapses serving as the fundamental units for inter-neuronal communication and ensuring the efficient functioning of these networks. Consequently, the development of devices capable of emulating synaptic functions represents a crucial avenue for advancing our understanding of neural networks. Among these devices, memristors have emerged as a promising candidate. Recognized as the fourth fundamental passive circuit element, memristors exhibit distinctive nonlinear memory characteristics. Their resistance values dynamically adjust in response to variations in the charge flowing through them and, importantly, retain these modified states even after power disconnection. These unique properties render memristors particularly suitable for emulating synaptic functions in neural systems. This paper provides a comprehensive overview of recent advancements in material selection and resistive switching mechanisms for flexible memristors, highlighting their applications in the construction of artificial neural networks. Furthermore, we discuss the feasibility of implementing neural networks using memristor-based architectures, while also addressing the current challenges that need to be overcome. Finally, we outline the development prospects and ongoing challenges in this rapidly evolving field.
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