AIMC Topic: Brain
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Brain-optimized deep neural network models of human visual areas learn non-hierarchical representations.
Nature communications
Jun 7, 2023
Deep neural networks (DNNs) optimized for visual tasks learn representations that align layer depth with the hierarchy of visual areas in the primate brain. One interpretation of this finding is that hierarchical representations are necessary to accu...
Model discovery to link neural activity to behavioral tasks.
eLife
Jun 6, 2023
Brains are not engineered solutions to a well-defined problem but arose through selective pressure acting on random variation. It is therefore unclear how well a model chosen by an experimenter can relate neural activity to experimental conditions. H...
Understanding computational dialogue understanding.
Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Jun 5, 2023
In this paper, we first explain why human-like dialogue understanding is so difficult for artificial intelligence (AI). We discuss various methods for testing the understanding capabilities of dialogue systems. Our review of the development of dialog...
Recognizing Object by Components With Human Prior Knowledge Enhances Adversarial Robustness of Deep Neural Networks.
IEEE transactions on pattern analysis and machine intelligence
Jun 5, 2023
Adversarial attacks can easily fool object recognition systems based on deep neural networks (DNNs). Although many defense methods have been proposed in recent years, most of them can still be adaptively evaded. One reason for the weak adversarial ro...
Deep Learning Accelerated Image Reconstruction of Fluid-Attenuated Inversion Recovery Sequence in Brain Imaging: Reduction of Acquisition Time and Improvement of Image Quality.
Academic radiology
Jun 4, 2023
RATIONALE AND OBJECTIVES: Fluid-attenuated inversion recovery (FLAIR) imaging is playing an increasingly significant role in the detection of brain metastases with a concomitant increase in the number of magnetic resonance imaging (MRI) examinations....
Brain tumor detection and screening using artificial intelligence techniques: Current trends and future perspectives.
Computers in biology and medicine
Jun 1, 2023
A brain tumor is an abnormal mass of tissue located inside the skull. In addition to putting pressure on the healthy parts of the brain, it can lead to significant health problems. Depending on the region of the brain tumor, it can cause a wide range...
Brain Tumor Classification based on Improved Stacked Ensemble Deep Learning Methods.
Asian Pacific journal of cancer prevention : APJCP
Jun 1, 2023
OBJECTIVE: Brain Tumor diagnostic prediction is essential for assisting radiologists and other healthcare professionals in identifying and classifying brain tumors. For the diagnosis and treatment of cancer diseases, prediction and classification acc...
Fast and Calibrationless Low-Rank Parallel Imaging Reconstruction Through Unrolled Deep Learning Estimation of Multi-Channel Spatial Support Maps.
IEEE transactions on medical imaging
Jun 1, 2023
Low-rank technique has emerged as a powerful calibrationless alternative for parallel magnetic resonance (MR) imaging. Calibrationless low-rank reconstruction, such as low-rank modeling of local k-space neighborhoods (LORAKS), implicitly exploits bot...
Understanding brain functional architecture through robotics.
Science robotics
May 31, 2023
Robotics is increasingly seen as a useful test bed for computational models of the brain functional architecture underlying animal behavior. We provide an overview of past and current work, focusing on probabilistic and dynamical models, including ap...
BrainS: Customized multi-core embedded multiple scale neuromorphic system.
Neural networks : the official journal of the International Neural Network Society
May 30, 2023
Research on modeling and mechanisms of the brain remains the most urgent and challenging task. The customized embedded neuromorphic system is one of the most effective approaches for multi-scale simulations ranging from ion channel to network. This p...
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