The main goal of the field of neuromorphic computing is to build machines that emulate aspects of the brain in its ability to perform complex tasks in parallel and with great energy efficiency. Thanks to new computing architectures, these machines could revolutionize high-performance computing and find applications to perform local, low-energy computing for sensors and robots. The use of organic and soft materials in neuromorphic computing is appealing in many respects, for instance, because it allows better integration with living matter to seamlessly meld sensing with signal processing, and ultimately, stimulation in a closed-feedback loop. Indeed, not only can the mechanical properties of organic materials match those of tissue, but also, the working mechanisms of these devices involving ions, in addition to electrons, are compatible with human physiology. Another advantage of organic materials is the potential to introduce novel fabrication techniques relying on additive manufacturing amenable to one-of-a-kind form factors. This field is still nascent, therefore many concepts are still being proposed, without a clear winner. Furthermore, the field of application of organic neuromorphics, where bioinspiration and biointegration are extremely appealing, calls for a co-design approach from materials to systems.

神经形态计算领域的主要目标是构建能够模仿大脑各方面的机器，使其能够并行执行复杂任务并具有很高的能源效率。得益于新的计算架构，这些机器可以彻底改变高性能计算，并找到应用程序来执行用于传感器和机器人的本地低能耗计算。例如，在神经形态计算中有机和软材料的使用在许多方面都具有吸引力，因为它可以更好地与生物融合，从而无缝融合传感与信号处理，并最终在封闭反馈回路中进行刺激。确实，有机材料的机械性能不仅可以与组织的机械性能相匹配，而且这些设备除电子外还涉及离子的工作机理，与人类生理相容。有机材料的另一个优点是有可能引入依赖于一种形状因数的增材制造的新颖制造技术。该领域仍处于起步阶段，因此仍在提出许多概念，但没有明确的赢家。此外，在有机神经形态学的应用领域中，生物灵感和生物整合极具吸引力，因此需要从材料到系统的协同设计方法。