Recurrent neural networks (RNN), first proposed in the 1980s, made adjustments to the original structure of neural networks to enable them to process streams of data.

Recurrent neural networks are a classification of artificial neural networks used in artificial intelligence (AI), natural language processing (NLP), deep learning, and machine learning.

Neural networks are a powerful tool for modeling neural activity in the brain. In this talk, I will discuss how these models have helped in my own research and highlight recent work building neural ...

Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional, and transformer networks, and their use cases in modern AI.

Recurrent neural networks can solve some types of problems that regular feed-forward networks cannot handle.

One approach center on recurrent neural networks, a type of neural circuit model that consists of many recurrently connected units.

This study presents valuable computational findings on the neural basis of learning new motor memories without interfering with previously learned behaviours using recurrent neural networks. The ...