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

A rat brain (Image credit: Getty Images) Copy link Facebook X Whatsapp Reddit Pinterest Flipboard Email Share this article 0 Join the conversation Follow us Add us as a preferred source on Google Newsletter Stay On the Cutting Edge: Get the Tom's Hardware Newsletter Get Tom's Hardware's best news and in-depth reviews, straight to your inbox. By submitting your information you agree to the Terms & Conditions and Privacy Policy and are aged 16 or over. You are now subscribed Your newsletter sign-up was successful An account already exists for this email address, please log in. Subscribe to our newsletter A team at Tohoku University and Future University Hakodate in Japan trained cultured rat cortical neurons to autonomously generate complex temporal signals using a real-time machine learning framework, according to a study published March 12 in the journal Proceedings of the National Academy of Sciences . The researchers integrated the living neurons with high-density microelectrode arrays and microfluidic devices, creating a closed-loop reservoir computing system that learned to produce periodic and chaotic waveforms without any external input. Go deeper with TH Premium: AI and data centers (Image credit: Microsoft) Photonics and high-speed data movement is the next big AI bottleneck The data center cooling state of play Massive AI data center buildouts are squeezing energy supplies Ultra Ethernet: The data center interconnection of tomorrow The system recorded spike trains from the neurons across a 26,400-electrode array with a 17.5-micrometer pitch, filtered them into continuous signals, and decoded an output through a linear readout layer. That output was then fed back to the neurons as electrical stimulation, completing a feedback loop that cycled roughly every 333 milliseconds. The readout weights were optimized in real time using an algorithm called FORCE (First-Order Reduced and Controlled Error) learning, which continuously adjusted the decoder to minimize the error between the network's output and a target waveform. The enabling technology, per the researchers, was the use of PDMS microfluidic films to constrain how the neurons connected. Without physical constraints, cultured neurons form dense, highly synchronized networks that fire in lockstep, and these homogeneous networks failed to learn any of the target signals. Article continues below Instead, the researchers confined neuronal cell bodies to 128 square wells, each roughly 100x100 micrometers, with each well holding an average of 14.6 neurons. The wells were linked by microchannels in two configurations: a lattice design with uniform nearest-neighbor connections, and a hierarchical design with sparser, multi-scale connections. Both patterned configurations dramatically reduced pairwise neural correlations compared to unpatterned cultures (0.11 and 0.12 versus 0.45, respectively), increasing the dimensionality of the network's dynamics. Lattice networks consistently outperformed hi