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.

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.

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

AI Terminology 101: Discover how Recurrent Neural Networks process sequential data, their applications, and their future in the AI landscape.

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 ...

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 ...