AIMC Topic: Brain

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Playing Brains: The Ethical Challenges Posed by Silicon Sentience and Hybrid Intelligence in DishBrain.

Science and engineering ethics Oct 26, 2023
The convergence of human and artificial intelligence is currently receiving considerable scholarly attention. Much debate about the resulting Hybrid Minds focuses on the integration of artificial intelligence into the human brain through intelligent ...
Humans Brain Learning Artificial Intelligence Intelligence Silicon
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Deep Learning-Driven Transformation: A Novel Approach for Mitigating Batch Effects in Diffusion MRI Beyond Traditional Harmonization.

Journal of magnetic resonance imaging : JMRI Oct 25, 2023
BACKGROUND: "Batch effect" in MR images, due to vendor-specific features, MR machine generations, and imaging parameters, challenges image quality and hinders deep learning (DL) model generalizability.
Diffusion Magnetic Resonance Imaging Deep Learning Image Processing, Computer-Assisted Male Middle Aged Retrospective Studies Humans Female Brain Aged Reproducibility of Results Adult
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Next-generation cognitive assessment: Combining functional brain imaging, system perturbations and novel equipment interfaces.

Brain research bulletin Oct 22, 2023
Conventional cognitive assessment is widely used in clinical and research settings, in educational institutions, and in the corporate world for personnel selection. Such approaches involve having a client, a patient, or a research participant complet...
Cognition Brain Neuroimaging Functional Neuroimaging Humans Artificial Intelligence
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On the structure function dichotomy: A perspective from human brain network modeling. Comment on "Structure and function in artificial, zebrafish and human neural networks" by Peng Ji et al.

Physics of life reviews Oct 18, 2023
Head Animals Zebrafish Neural Networks, Computer Brain Humans
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Transformer-based deep learning denoising of single and multi-delay 3D arterial spin labeling.

Magnetic resonance in medicine Oct 17, 2023
PURPOSE: To present a Swin Transformer-based deep learning (DL) model (SwinIR) for denoising single-delay and multi-delay 3D arterial spin labeling (ASL) and compare its performance with convolutional neural network (CNN) and other Transformer-based ...
Brain Deep Learning Image Processing, Computer-Assisted Humans Spin Labels Arteries Magnetic Resonance Imaging Cerebrovascular Circulation
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Deep learning based diagnosis of Alzheimer's disease using FDG-PET images.

Neuroscience letters Oct 17, 2023
PURPOSE: The aim of this study is to develop a deep neural network to diagnosis Alzheimer's disease and categorize the stages of the disease using FDG-PET scans. Fluorodeoxyglucose positron emission tomography (FDG-PET) is a highly effective diagnost...
Positron-Emission Tomography Fluorodeoxyglucose F18 Cognitive Dysfunction Alzheimer Disease Neuroimaging Humans Brain Deep Learning
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Lateralized Learning to Solve Complex Boolean Problems.

IEEE transactions on cybernetics Oct 17, 2023
Modern classifier systems can effectively classify targets that consist of simple patterns. However, they can fail to detect hierarchical patterns of features that exist in many real-world problems, such as understanding speech or recognizing object ...
Machine Learning Brain
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Model metamers reveal divergent invariances between biological and artificial neural networks.

Nature neuroscience Oct 16, 2023
Deep neural network models of sensory systems are often proposed to learn representational transformations with invariances like those in the brain. To reveal these invariances, we generated 'model metamers', stimuli whose activations within a model ...
Humans Neural Networks, Computer Learning Brain
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Detection and classification of adult epilepsy using hybrid deep learning approach.

Scientific reports Oct 16, 2023
The electroencephalogram (EEG) has emerged over the past few decades as one of the key tools used by clinicians to detect seizures and other neurological abnormalities of the human brain. The proper diagnosis of epilepsy is crucial due to its distinc...
Brain Deep Learning Neural Networks, Computer Algorithms Epilepsy Humans Seizures Signal Processing, Computer-Assisted Child Electroencephalography
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Deep Learning-Based Classification of Epileptic Electroencephalography Signals Using a Concentrated Time-Frequency Approach.

International journal of neural systems Oct 13, 2023
ConceFT (concentration of frequency and time) is a new time-frequency (TF) analysis method which combines multitaper technique and synchrosqueezing transform (SST). This combination produces highly concentrated TF representations with approximately p...
Humans Electroencephalography Seizures Algorithms Epilepsy Brain Signal Processing, Computer-Assisted Deep Learning
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