Abstract
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.
Highlights
Optical neural networks process information at the speed of light and are energetically efficient
We demonstrate that a tumor-based random optical learning machine (ROM) is a novel approach to investigate cancer morphodynamics under the action of external stimuli as hyperthermia and chemotherapy
We show that a ROM may output information for tumor diagnosis and drug efficacy, which seems competitive with more conventional approaches, as biochemical assays and confocal imaging
Summary
Optical neural networks process information at the speed of light and are energetically efficient. We employ living three-dimensional tumor brain models to demonstrate a random optical learning machine (ROM) for the investigation of glioblastoma. In conventional deep neural networks (D-NNs), the training becomes demanding as the number of nodes growths[2,3] For this reason, new architectures containing a large number of untrained random nodes—a computing reservoir—are very appealing. Random optical-learning machines (ROMs) may have innovative and surprising applications in biophysics and medicine, because any biological system may act as a reservoir for mixing light signals and perform computations[26]. Our living random optical neural network has thousands of cells acting as wave-mixing nodes made of glioblastoma cells, which form a large-scale computing reservoir and enable the detection of tumor morphodynamics. The biocomputing optical system measures biophysical quantities, as cancer metabolism, which commonly requires invasive methods
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