Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are designed to ...

More information: Omid G. Sani et al, Dissociative and prioritized modeling of behaviorally relevant neural dynamics using ...

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

Recurrent neural networks, or RNNs, are a style of neural network that involve data moving backward among layers. This style of neural network is also known as a cyclical graph .

Recurrent neural networks can solve some types of problems that regular feed-forward networks cannot handle. There are several different types of recurrent neural networks. This article describes one ...

This important study demonstrates the significance of incorporating biological constraints in training neural networks to develop models that make accurate predictions under novel conditions. By ...

To test their hypothesis, Langdon and Engel first applied their new model to recurrent neural networks trained to perform a context-dependent decision-making task. The task, performed by humans, ...

Choosing what stimulus to focus on, a.k.a. attention, is also the main mechanism behind another neural network architecture, the transformer, which has become the heart of large language models ...