May, L., et al. (2025). Pre-training artificial neural networks with spontaneous retinal activity improves motion prediction in natural scenes. PLoS Computational Biology.

The field of artificial neural networks is extremely complicated and readily evolving. In order to understand neural networks and how they process information, it is critical to examine how these ...

Here, we feed the neural network vast amounts of training data, labeled by humans so that a neural network can essentially fact-check itself as it’s learning.

image: Prof. Dr. Julijana Gjorgjieva. view more . Credit: Astrid Eckert / TUM. Researchers at TUM trained artificial neural networks using biological data from the early visual system development.

Deep neural networks are at the heart of artificial intelligence, ranging from pattern recognition to large language and ...

In 1989 Crick wrote, “As far as the learning process is concerned, it is unlikely that the brain actually uses back propagation.” Backprop is considered biologically implausible for several major ...

Neural networks are a subset of machine learning, which is a technique used to help computers learn using training that is modeled on results gleaned from large data sets. As such, neural networks ...

A team from MIT's Computer Science and Artificial Intelligence Lab (CSAIL) says that understanding these representations, as well as how they inform the ways that neural networks learn from data ...

Artificial Neural Network Architecture. Scientists design ANNs to function like neurons. 6 They write lines of code in an algorithm such that there are nodes that each contain a mathematical function, ...