Allison Whitten

An energy-efficient chip called NeuRRAM fixes an old design flaw to run large-scale AI algorithms on smaller devices, reaching the same accuracy as wasteful digital computers.

Letting human brain organoids grow in animal brains could be an ethical new option for experimental studies of neurological disorders.

Christopher Kanan is building algorithms that can continuously learn over time — the way we do.

Intelligent beings learn by interacting with the world. Artificial intelligence researchers have adopted a similar strategy to teach their virtual agents new tricks.

Neuroscientists uncovered an energy-saving mode in vision-system neurons that works at the cost of being able to see fine-grained details.

Dopamine, a neurochemical often associated with reward behavior, also seems to help organize precisely when the brain initiates movements. It’s the latest revelation about the power of neuromodulators.

Algorithms that use the brain’s communication signal can now work on analog neuromorphic chips, which closely mimic our energy-efficient brains.

Tiny amounts of artificial noise can fool neural networks, but not humans. Some researchers are looking to neuroscience for a fix.

A new model of learning centers on bursts of neural activity that act as teaching signals — approximating backpropagation, the algorithm behind learning in AI.