Brain-inspired computing hardware aims to emulate the structure and working principles of the brain and could be used to address current limitations in artificial intelligence technologies. However, brain-inspired silicon chips are still limited in their ability to fully mimic brain function as most examples are built on digital electronic principles. Here we report an artificial intelligence hardware approach that uses adaptive reservoir computation of biological neural networks in a brain organoid. In this approach—which is termed Brainoware—computation is performed by sending and receiving information from the brain organoid using a high-density multielectrode array. By applying spatiotemporal electrical stimulation, nonlinear dynamics and fading memory properties are achieved, as well as unsupervised learning from training data by reshaping the organoid functional connectivity. We illustrate the practical potential of this technique by using it for speech recognition and nonlinear equation prediction in a reservoir computing framework.

类脑计算硬件旨在模拟大脑的结构和工作原理，可用于解决当前人工智能技术的局限性。然而，受大脑启发的硅芯片在完全模拟大脑功能方面仍然受到限制，因为大多数例子都是基于数字电子原理构建的。在这里，我们报告了一种人工智能硬件方法，该方法在大脑类器官中使用生物神经网络的自适应储存库计算。在这种称为 Brainoware 的方法中，计算是通过使用高密度多电极阵列从大脑类器官发送和接收信息来执行的。通过应用时空电刺激，实现了非线性动力学和褪色记忆特性，以及通过重塑类器官功能连接从训练数据中进行无监督学习。我们通过在储层计算框架中将其用于语音识别和非线性方程预测来说明该技术的实际潜力。