Owing to its close resemblance to biological systems and materials, soft matter has been successfully implemented in numerous bioelectronic and biosensing applications, as well as in bioinspired computing and neuromorphic electronics. Particularly, organic mixed ionic–electronic conductors possess favourable characteristics for their efficient use in organic electrochemical transistors, electrochemical memory and artificial synapses and neurons. Owing to their mixed ionic–electronic conduction, leading to high amplification, these materials are ideal for translating chemical signals, such as ions or neurotransmitters, into electrical signals, as well as for accurately controlling stable conductance states to efficiently emulate synaptic weights in artificial neural networks. Because these mixed conductors operate with ionic charges — similar to signalling in biological neuronal networks — they also exhibit ideal properties to emulate biological spiking neurons. In this Perspective, we consider the potential of soft matter, especially based on organic mixed conductors, for bioinspired systems and their possible applications. We discuss the potential that these materials have in applications in which low power, conformability and tunability are key, such as smart and adaptive biosensors, low-power in-sensor and edge computing, intelligent agents and robotics, and event-driven systems and biohybrid spiking circuits at the interface with biology. We present a comprehensive perspective of the potential of biomimetic and bioinspired electronics based on soft matter to integrate artificial intelligence into everyday life.

由于其与生物系统和材料非常相似，软物质已成功应用于众多生物电子和生物传感应用以及仿生计算和神经形态电子学中。特别是，有机混合离子电子导体因其在有机电化学晶体管、电化学记忆以及人工突触和神经元中的有效使用而具有良好的特性。由于其混合离子电子传导，导致高放大率，这些材料非常适合将化学信号（例如离子或神经递质）转化为电信号，以及精确控制稳定的电导状态以有效模拟人工神经中的突触权重网络。由于这些混合导体通过离子电荷进行操作（类似于生物神经元网络中的信号传导），因此它们还表现出模拟生物尖峰神经元的理想特性。在本视角中，我们考虑了软物质（尤其是基于有机混合导体的软物质）在仿生系统及其可能应用中的潜力。我们讨论了这些材料在以低功耗、一致性和可调谐性为关键的应用中的潜力，例如智能和自适应生物传感器、低功耗传感器内和边缘计算、智能代理和机器人、事件驱动系统和生物混合技术与生物学接口处的尖峰电路。我们全面展示了基于软物质的仿生和仿生电子产品将人工智能融入日常生活的潜力。