Biological neural networks are immensely complex systems underlying all aspects of cognition and behavior. Despite significant advances in neuroscience, a ...

Before jumping into the AI deep end, it’s vital to take the time to understand how your infrastructure really fits together.

Machine learning models called convolutional neural networks (CNNs) power technologies like image recognition and language ...

The authors analyzed spectral properties of neural activity recorded using laminar probes while mice engaged in a global/local visual oddball paradigm. They found solid evidence for an increase in ...

Interactive platforms like Codecademy and Dataquest.io let you learn and code right in your browser, making python online ...

This manuscript makes a valuable contribution to understanding learning in multidimensional environments with spurious associations, which is critical for understanding learning in the real world. The ...

Abstract: Recent advancements in deep neural networks heavily rely on large-scale labeled datasets. However, acquiring annotations for large datasets can be challenging due to annotation constraints.

Five federally funded AI institutes provide a backbone for agriculture-focused AI research. The grand challenge facing global ...

We show that, compared with surgeon predictions and existing risk-prediction tools, our machine-learning model can enhance ...

The research aim is to develop an intelligent agent for cybersecurity systems capable of detecting abnormal user behavior ...

Abstract: This paper studies the fuzzy-based synchronization control problem of coupled neural networks under cyber attacks, where the considered attacks can block the communication links between ...