Recurrent Neural Networks are artificial neural networks designed to handle sequential data like text, speech or financial records.

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

They specifically used it to analyze and model the neural dynamics in datasets containing recordings of the neuronal activity in the brains of non-human primates while they completed different tasks.

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 ...

A recurrent neural network structure exists in the most important part of the brain -- the frontal cortex -- and this network is less complex than has been thought and mostly unidirectional, new ...

These include recurrent neural networks (for analysing sequences of data), variational autoencoders (for spotting patterns in data), generative adversarial networks (where one model learns to do a ...

Several types of convolutional neural networks exist, including traditional CNNs, recurrent neural networks, fully convolutional networks and spatial transformer networks — among others.