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Researchers develop a biodegradable artificial synapse that uses less energy than the human brain, enabling sustainable neuromorphic electronics and temporary intelligent devices.

Researchers create a semiconductor that copies how brain cells adjust to repeated signals, improving energy use and reliability in artificial intelligence systems.

Researchers have mapped out a path for the development of neuromorphic computers that work like the brain.

New hafnium-based memristor mimics neuron connections with ultra-low power and high stability, potentially reducing AI energy use by up to 70% while enabling adaptive learning in hardware.

This new device mimics brain neurons using superconductors for fast, low-power AI networks that resist manufacturing flaws.

Australian company Cortical Labs creates a biological computer that learns fast and uses less energy than regular AI systems.

Flexible printed devices produce realistic signals that activate real neurons and point toward efficient brain-inspired computing.

New device combines memory and processing using precise hydrogen ion movement to mimic brain synapses in a compact stacked design for efficient computing.

UC San Diego researchers create a computing device that mimics brain networks to combine memory and processing for faster and more energy-efficient performance.

A neuromorphic system that emulates large networks of spiking neurons, rivaling the estimated rate of operations in the human brain