Neural networks

Computational neuroscientists taught an artificial neural network to imitate a biological neuron. The result offers a new way to think about the complexity of single brain cells.

Researchers are turning to the mathematics of higher-order interactions to better model the complex connections within their data.

Melanie Mitchell has worked on digital minds for decades. She says they’ll never truly be like ours until they can make analogies.

A temporal pattern of activity observed in human brains may explain how we can learn so quickly.

For all their triumphs, AI systems can’t seem to generalize the concepts of “same” and “different.” Without that, researchers worry, the quest to create truly intelligent machines may be hopeless.

Glycans, the complex sugars that stud cellular surfaces, are like a language that life uses to mediate vital interactions. Researchers are learning how to read their meaning.

To build a general artificial intelligence, we may need to know more about our own minds, argues the computer scientist Melanie Mitchell.

Two new approaches allow deep neural networks to solve entire families of partial differential equations, making it easier to model complicated systems and to do so orders of magnitude faster.

The learning algorithm that enables the runaway success of deep neural networks doesn’t work in biological brains, but researchers are finding alternatives that could.