Artificial neural networks have been applied to problems ... we can make the network 'learn' to solve many types of problems. A model neuron is referred to as a threshold unit and its function ...

The AI model that shook the world is part of a broad trend to squeeze more out of chips using what's called sparsity.

Engineering simulations often require significant computational resources and time, which creates barriers for users and can ...

Rats perceive the world with a complexity that modern artificial neural networks struggle to match. This is the finding of a recent study published in the journal Patterns by the Visual Neuroscience ...

But what if, instead of collecting all possible data from a sensor, we could be more selective, collecting just enough data to accurately identify whatever we’re looking for? Th ...

This article establishes a neural network-based technique for automatic peak picking in 2D NMR spectroscopy, demonstrating a ...

Just as GPUs once eclipsed CPUs for AI workloads, Neural Processing Units (NPUs) are set to challenge GPUs by delivering even ...

Traditional artificial intelligence (AI) systems are built on artificial neural networks that mimic the human brain’s neurons ...

When designing a robot, such as Boston Dynamics' anthropomorphic robot Atlas, which appears exercising and sorting boxes, ...

Researchers applied the mathematical theory of synchronization to clarify how recurrent neural networks (RNNs) generate predictions, revealing a certain map, based on the generalized synchronization, ...

Morphological profiling allows accurate identification of cell types in dense iPSC-derived cultures, allowing its use for quality control and differentiation monitoring.