Researchers at University of Southern California and University of Pennsylvania recently introduced a new nonlinear dynamical modeling framework based on recurrent neural networks (RNNs) that ...

A new computational model improves estimation of Granger connectivity by removing spurious effects of external inputs, and estimation of linear encoding models by removing spurious effects of ...

Artificial neural networks (ANNs) have proven to be extremely useful for solving problems such as classification, regression, function estimation and dimensionality reduction.However, it turns out ...

A new neural-network architecture developed by researchers at Google might solve one of the great challenges for large language models (LLMs): extending their memory at inference time without ...

Using Recurrent Neural Networks to Extract High-Quality Information From Lung Cancer Screening Computerized Tomography Reports for Inter ... Training set 2 was also used to train Bi-LSTM model 2, ...

Renesas Electronics Corporation (TSE:6723), a premier supplier of advanced semiconductor solutions, today introduced the RA8P1 microcontroller (MCU) Group targeted at Artificial Intelligence (AI) and ...

More information: Omid G. Sani et al, Dissociative and prioritized modeling of behaviorally relevant neural dynamics using recurrent neural networks, Nature Neuroscience (2024). DOI: 10.1038 ...