AI Medical Compendium Journal:
IEEE transactions on neural networks and learning systems

Showing 61 to 70 of 780 articles

Predefined-Time Convergent Kinematic Control of Robotic Manipulators With Unknown Models Based on Hybrid Neural Dynamics and Human Behaviors.

IEEE transactions on neural networks and learning systems
This article proposes a model-free kinematic control method with predefined-time convergence for robotic manipulators with unknown models. The predefined-time convergence property guarantees that the regulation task can be finished by robotic manipul...

Rectify ViT Shortcut Learning by Visual Saliency.

IEEE transactions on neural networks and learning systems
Shortcut learning in deep learning models occurs when unintended features are prioritized, resulting in degenerated feature representations and reduced generalizability and interpretability. However, shortcut learning in the widely used vision transf...

A Human-Machine Joint Learning Framework to Boost Endogenous BCI Training.

IEEE transactions on neural networks and learning systems
Brain-computer interfaces (BCIs) provide a direct pathway from the brain to external devices and have demonstrated great potential for assistive and rehabilitation technologies. Endogenous BCIs based on electroencephalogram (EEG) signals, such as mot...

A Bio-Inspired Spiking Attentional Neural Network for Attentional Selection in the Listening Brain.

IEEE transactions on neural networks and learning systems
Humans show a remarkable ability in solving the cocktail party problem. Decoding auditory attention from the brain signals is a major step toward the development of bionic ears emulating human capabilities. Electroencephalography (EEG)-based auditory...

Low Latency and Sparse Computing Spiking Neural Networks With Self-Driven Adaptive Threshold Plasticity.

IEEE transactions on neural networks and learning systems
Spiking neural networks (SNNs) have captivated the attention worldwide owing to their compelling advantages in low power consumption, high biological plausibility, and strong robustness. However, the intrinsic latency associated with SNNs during infe...

Brain Network Classification for Accurate Detection of Alzheimer's Disease via Manifold Harmonic Discriminant Analysis.

IEEE transactions on neural networks and learning systems
Mounting evidence shows that Alzheimer's disease (AD) manifests the dysfunction of the brain network much earlier before the onset of clinical symptoms, making its early diagnosis possible. Current brain network analyses treat high-dimensional networ...

Synchronization-Inspired Interpretable Neural Networks.

IEEE transactions on neural networks and learning systems
Synchronization is a ubiquitous phenomenon in nature that enables the orderly presentation of information. In the human brain, for instance, functional modules such as the visual, motor, and language cortices form through neuronal synchronization. In...

Clinical Prompt Learning With Frozen Language Models.

IEEE transactions on neural networks and learning systems
When the first transformer-based language models were published in the late 2010s, pretraining with general text and then fine-tuning the model on a task-specific dataset often achieved the state-of-the-art performance. However, more recent work sugg...

Lifelong Learning With Cycle Memory Networks.

IEEE transactions on neural networks and learning systems
Learning from a sequence of tasks for a lifetime is essential for an agent toward artificial general intelligence. Despite the explosion of this research field in recent years, most work focuses on the well-known catastrophic forgetting issue. In con...

GeSeNet: A General Semantic-Guided Network With Couple Mask Ensemble for Medical Image Fusion.

IEEE transactions on neural networks and learning systems
At present, multimodal medical image fusion technology has become an essential means for researchers and doctors to predict diseases and study pathology. Nevertheless, how to reserve more unique features from different modal source images on the prem...