Optical neural networks process information at the speed of light and are energetically efficient. Photonic artificial intelligence allows speech recognition, image classification, and Ising machines. Modern machine learning paradigms, as extreme learning machines, reveal that disordered and biological materials may realize optical neural networks with thousands of nodes trained only at the input and at the readout. May we use living matter for machine learning? Here, we employ living three-dimensional tumor brain models to demonstrate a random optical learning machine (ROM) for the investigation of glioblastoma. The tumor spheroid act as a computational reservoir. The ROM detects cancer morphodynamics by laser-induced hyperthermia, quantifies chemotherapy, and cell metabolism. The ROM is a sensitive noninvasive smart probe for cytotoxicity assay and enables real-time investigation of tumor dynamics. We hence design and demonstrate a novel bio-hardware for optical computing and the study of light/complex matter interaction.

光学神经网络以光速处理信息，并且在能量上高效。光子人工智能可以进行语音识别，图像分类和Ising机器。作为极限学习机的现代机器学习范式表明，无序和生物材料可能会实现光学神经网络，其中成千上万个节点仅在输入和读出时受过训练。我们可以将生物用于机器学习吗？在这里，我们采用活的三维肿瘤脑模型来证明随机光学学习机（ROM）用于研究胶质母细胞瘤。肿瘤球体充当计算库。ROM通过激光诱导的高温检测癌症的形态动力学，量化化学疗法和细胞代谢。ROM是用于细胞毒性测定的灵敏的非侵入性智能探针，可实时调查肿瘤动态。因此，我们设计并演示了用于光学计算和光/复杂物质相互作用研究的新型生物硬件。