Researchers train living rat neurons to perform real-time AI computations — experiments could pave the way for new brain-machine interfaces

Researchers in Japan trained living rat cortical neurons to generate complex temporal signals using a real-time machine learning framework, integrated with microelectrode arrays and microfluidic devices. The closed-loop system learned to produce periodic and chaotic waveforms without external input, cycling every 333 milliseconds. By confining neuronal cell bodies in specific patterns, the network's dynamics were enhanced, enabling the system to generate various waveforms and approximate a chaotic trajectory. The study suggests that living neuronal networks could serve as novel computational resources, with potential applications in brain-machine interfaces and neuroprosthetic devices.