At a time when conflict and division dominate the headlines, a new study from UCLA finds remarkable similarities in how mice ...

Abstract: Human motor learning is a neural process essential for acquiring new motor skills and adapting existing ones, which is fundamental to everyday activities. Neurological disorders such as ...

In today's data-driven environment, Python has become the mainstream language in the fields of machine learning and data science due to its concise syntax, rich library support, and active community, ...

A simulation model for the digital reconstruction of 3D root system architectures. Integrated with a simulation-based inference generative deep learning model.

A recent Nature study shows that separated artificial neural networks can accurately model SiC MOSFETs using minimal training data. Silicon carbide MOSFETs are increasingly replacing traditional ...

Artificial intelligence is now part of our daily lives, with the subsequent pressing need for larger, more complex models. However, the demand for ever-increasing power and computing capacity is ...

For every motor skill you've ever learned, whether it's walking or watchmaking, there is a small ensemble of neurons in your brain that makes that movement happen. Our brains trigger these ...

STM-Graph is a Python framework for analyzing spatial-temporal urban data and doing predictions using Graph Neural Networks. It provides a complete end-to-end pipeline from raw event data to trained ...

Neural networks are computing systems designed to mimic both the structure and function of the human brain. Caltech researchers have been developing a neural network made out of strands of DNA instead ...

Abstract: Graph Neural Network (GNN) is a popular semi-supervised graph representation learning method, whose performance strongly relies on the quality and quantity of labeled nodes. Given the ...