The multiple condition (MC)-retention model is an uncertainty-aware graph-based neural network that predicts liquid chromatography (LC) retention times across multiple column chem ...

Researchers have demonstrated a new training technique that significantly improves the accuracy of graph neural networks (GNNs)—AI systems used in applications from drug discovery to weather ...

Researchers at Skoltech have proposed a new approach to training neural networks for wave propagation in absorbing media. The ...

A research team at Tohoku University and Future University Hakodate has demonstrated that living biological neurons can be trained to perform a supervised temporal pattern learning task previously ...

How can AI stabilize the power grid? New research uses biomimetic neural networks to manage the uncertainty of solar and wind energy, reducing hardware costs and preventing blackouts.

Can living neurons replace AI? A new study shows that biological neural networks (BNNs) can be trained to perform reservoir ...

Highlights: Living rat neurons successfully trained to perform real-time AI computations Structured neuron networks improved learning and reduced synchronization Technology shows strong potential for ...

The advent of high-density recording technologies, such as Neuropixels and large-scale calcium imaging, has provided an unprecedented look into the ...

Over the past decades, computer scientists have introduced numerous artificial intelligence (AI) systems designed to emulate the organization and functioning of networks of neurons in the brain.