AIMC Topic: Recurrent Neural Networks

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SegElegans: Instance segmentation using dual convolutional recurrent neural network decoder in Caenorhabditis elegans microscopic images.

Computers in biology and medicine
Caenorhabditis elegans is a great model for exploring organismal, cellular, and subcellular biology through optical and fluorescence microscopy, with its research applications steadily expanding. However, manual processing of numerous microscopic ima...

Validity of recurrent neural networks to predict pedal forces and lower limb kinetics in cycling.

Journal of biomechanics
Dynamic variables contribute to understand the mechanics of pedalling and can assist with injury prevention. Measuring pedal forces and joint moments and powers has a high cost, which can be mitigated by using trained artificial neural networks (ANN)...

Structure of activity in multiregion recurrent neural networks.

Proceedings of the National Academy of Sciences of the United States of America
Neural circuits comprise multiple interconnected regions, each with complex dynamics. The interplay between local and global activity is thought to underlie computational flexibility, yet the structure of multiregion neural activity and its origins i...

Exploring a digital music teaching model integrated with recurrent neural networks under artificial intelligence.

Scientific reports
This study proposes an intelligent digital music teaching model based on Artificial Intelligence (AI) and Long Short-Term Memory (LSTM) networks to enhance personalized assessment and feedback in music education. Within this teaching model, the music...

Complex-Valued Convolutional Gated Recurrent Neural Network for Ultrasound Beamforming.

IEEE transactions on neural networks and learning systems
Ultrasound detection is a potent tool for the clinical diagnosis of various diseases due to its real-time, convenient, and noninvasive qualities. Yet, existing ultrasound beamforming and related methods face a big challenge to improve both the qualit...

GARNN: An interpretable graph attentive recurrent neural network for predicting blood glucose levels via multivariate time series.

Neural networks : the official journal of the International Neural Network Society
Accurate prediction of future blood glucose (BG) levels can effectively improve BG management for people living with type 1 or 2 diabetes, thereby reducing complications and improving quality of life. The state of the art of BG prediction has been ac...

Recurrent neural networks with transient trajectory explain working memory encoding mechanisms.

Communications biology
Whether working memory (WM) is encoded by persistent activity using attractors or by dynamic activity using transient trajectories has been debated for decades in both experimental and modeling studies, and a consensus has not been reached. Even thou...

Comparative Analysis of Recurrent Neural Networks with Conjoint Fingerprints for Skin Corrosion Prediction.

Journal of chemical information and modeling
Skin corrosion assessment is an essential toxicity end point that addresses safety concerns for topical dosage forms and cosmetic products. Previously, skin corrosion assessments required animal testing; however, differences in skin architecture and ...

Chaotic recurrent neural networks for brain modelling: A review.

Neural networks : the official journal of the International Neural Network Society
Even in the absence of external stimuli, the brain is spontaneously active. Indeed, most cortical activity is internally generated by recurrence. Both theoretical and experimental studies suggest that chaotic dynamics characterize this spontaneous ac...

Performance of recurrent neural networks with Monte Carlo dropout for predicting pharmacokinetic parameters from dynamic contrast-enhanced magnetic resonance imaging data.

Journal of applied clinical medical physics
PURPOSE: To quantitatively evaluate the performance of two types of recurrent neural networks (RNNs), long short-term memory (LSTM) and gated recurrent units (GRU), using Monte Carlo dropout (MCD) to predict pharmacokinetic (PK) parameters from dynam...