Metabolomics, the large-scale study of small molecules in biological systems, has long faced a stubborn interpretability problem. Researchers can measure thousands of metabolites at once, but deciding ...

Continuous bump attractor networks (CBANs) are a prevailing model for how neural circuits represent continuous variables. CBANs maintain these representations by temporally integrating inputs that ...

Biological neural networks as a research area focus on the structure, dynamics, and computation of networks of real neurons in living organisms, integrating cellular neurophysiology, synaptic ...

Abstract: Recent experimental breakthroughs have paved the way for collecting “big” neural datasets through the simultaneous recording of the activity in thousands of neurons. However, our ...

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 ...

AI neural networks require about one million times more power than human brains use. Previous neural networks using live neurons were inefficient, so researchers found a viable solution in a polymer ...

Science & Applications describes a framework for building neural networks that operate entirely with light, processing ...

"Does AI have consciousness?"As generative AI rapidly advances, we are seeing this question more and more often.However, if we look a little deeper into the research of 2026, a more interesting ...

Publisher's note: This series was paid for by National Minority Quality Forum. The views expressed are the authors' own. Nearly seven million Americans currently live with Alzheimer's disease, a ...