MLCommons' AI training tests show that the more chips you have, the more critical the network that's between them.

To facilitate the computations on quantum computers, we map each model to a spin 1/2 system using the Jordan-Wigner transformation. This transformation allows us to take advantage of the capabilities ...

Abstract: Classification of resting state - functional Magnetic Resonance Imaging (rs-fMRI) data using deep learning algorithms is a challenging problem. Previous studies using latent space ...

Two RIKEN researchers have used a scheme for simplifying data to mimic how the brain of a fruit fly reduces the complexity of ...

How the brain largely maintains its function when neurons are lost—this is what researchers at the University Medical Center ...

The ability to precisely predict movements is essential not only for humans and animals, but also for many AI applications - ...

Explore 20 essential activation functions implemented in Python for deep neural networks—including ELU, ReLU, Leaky ReLU, Sigmoid, and more. Perfect for machine learning enthusiasts and AI ...

Scientists at Weill Cornell Medicine have developed a new algorithm, the Krakencoder, that merges multiple types of brain ...

Understanding neural network dynamics is a cornerstone of systems neuroscience, bridging the gap between biological neural networks and artificial neural ...

A neural network's basic computational units (also called nodes). Each neuron receives inputs, processes them using a mathematical function, and transmits the output to the next layer of neurons.