AIMC Topic: Brain
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Interactive Multi-Stage Robotic Positioner for Intra-Operative MRI-Guided Stereotactic Neurosurgery.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Dec 10, 2023
Magnetic resonance imaging (MRI) demonstrates clear advantages over other imaging modalities in neurosurgery with its ability to delineate critical neurovascular structures and cancerous tissue in high-resolution 3D anatomical roadmaps. However, its ...
Generalizable synthetic MRI with physics-informed convolutional networks.
Medical physics
Dec 8, 2023
BACKGROUND: Magnetic resonance imaging (MRI) provides state-of-the-art image quality for neuroimaging, consisting of multiple separately acquired contrasts. Synthetic MRI aims to accelerate examinations by synthesizing any desirable contrast from a s...
Four attributes of intelligence, a thousand questions.
Biological cybernetics
Dec 7, 2023
Jeff Hawkins is one of those rare individuals who speaks the languages of both AI and neuroscience. In his recent book, "A Thousand Brains: A New Theory of Intelligence", Hawkins proposes that current learning algorithms lack four attributes which wi...
Depression Identification Using EEG Signals via a Hybrid of LSTM and Spiking Neural Networks.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Dec 7, 2023
Depression severity can be classified into distinct phases based on the Beck depression inventory (BDI) test scores, a subjective questionnaire. However, quantitative assessment of depression may be attained through the examination and categorization...
BASE: Brain Age Standardized Evaluation.
NeuroImage
Dec 6, 2023
Brain age, most commonly inferred from T1-weighted magnetic resonance images (T1w MRI), is a robust biomarker of brain health and related diseases. Superior accuracy in brain age prediction, often falling within a 2-3 year range, is achieved predomin...
Even deeper problems with neural network models of language.
The Behavioral and brain sciences
Dec 6, 2023
We recognize today's deep neural network (DNN) models of language behaviors as engineering achievements. However, what we know intuitively and scientifically about language shows that what DNNs are and how they are trained on bare texts, makes them p...
Where do the hypotheses come from? Data-driven learning in science and the brain.
The Behavioral and brain sciences
Dec 6, 2023
Everyone agrees that testing hypotheses is important, but Bowers et al. provide scant details about where hypotheses about perception and brain function should come from. We suggest that the answer lies in considering how information about the outsid...
Let's move forward: Image-computable models and a common model evaluation scheme are prerequisites for a scientific understanding of human vision.
The Behavioral and brain sciences
Dec 6, 2023
In the target article, Bowers et al. dispute deep artificial neural network (ANN) models as the currently leading models of human vision without producing alternatives. They eschew the use of public benchmarking platforms to compare vision models wit...
Psychophysics may be the game-changer for deep neural networks (DNNs) to imitate the human vision.
The Behavioral and brain sciences
Dec 6, 2023
Psychologically faithful deep neural networks (DNNs) could be constructed by training with psychophysics data. Moreover, conventional DNNs are mostly monocular vision based, whereas the human brain relies mainly on binocular vision. DNNs developed as...
Fear-Neuro-Inspired Reinforcement Learning for Safe Autonomous Driving.
IEEE transactions on pattern analysis and machine intelligence
Dec 5, 2023
Ensuring safety and achieving human-level driving performance remain challenges for autonomous vehicles, especially in safety-critical situations. As a key component of artificial intelligence, reinforcement learning is promising and has shown great ...
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