“When you write code to build an artificial neural network, you're basically defining this architecture,” explained Grace Lindsay, a computational neuroscientist at New York University. She uses ANNs ...

The weights in any ANN are always just real numbers and the learning problem boils down to choosing the best value for each weight in the network. This means there are two important decisions to make ...

Inspired by the human brain, artificial neural networks (ANNs) are a type of machine learning model containing multiple layers of interconnected nodes (or neurons) that can process data ... Scientists ...

Many conventional computer architectures are ill-equipped to meet the computational demands of machine learning-based models. In recent years, some engineers have thus been trying to design ...

Researchers at FORTH have developed a new type of artificial neural network (ANN) that incorporates features of biological ...

Humans and certain animals appear to have an innate capacity to learn relationships between different objects or events in ...

Neural networks can be tuned to adapt to new data and situations, which essentially allows them to learn from experience and improve over time. Neural networks have revolutionized the fields of ...

Scientists in Spain have used genetic algorithms to optimize a feedforward artificial neural network for the prediction of energy generation of PV systems. Genetic algorithms use “parents” and ...