Neuroscientists want to understand how individual neurons encode information that allows us to distinguish objects, like ...

Researchers have long been interested in how humans and animals make decisions by focusing on trial-and-error behavior ...

This article proposes a novel parametric modeling technique incorporating a joint polynomial-transfer function with neural networks (short for neuro-PTF) for electromagnetic (EM) behaviors of ...

The strength of certain neural connections can predict how well someone can learn math, and mildly electrically stimulating ...

Using machine learning and math, a Brigham Young University student improved a key tool firefighters rely on during wildfire ...

Approximate multipliers (AppMults) are widely employed in deep neural network (DNN) accelerators to reduce the area, delay, and power consumption. However, the inaccuracies of AppMults degrade DNN ...

Brigham Young University graduate Jane Housley's research could help make a widely used wildfire modeling tool faster and ...