AIMC Topic: Brain

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Bioplausible Unsupervised Delay Learning for Extracting Spatiotemporal Features in Spiking Neural Networks.

Neural computation
The plasticity of the conduction delay between neurons plays a fundamental role in learning temporal features that are essential for processing videos, speech, and many high-level functions. However, the exact underlying mechanisms in the brain for t...

Do we empathize humanoid robots and humans in the same way? Behavioral and multimodal brain imaging investigations.

Cerebral cortex (New York, N.Y. : 1991)
Humanoid robots have been designed to look more and more like humans to meet social demands. How do people empathize humanoid robots who look the same as but are essentially different from humans? We addressed this issue by examining subjective feeli...

AmyloidPETNet: Classification of Amyloid Positivity in Brain PET Imaging Using End-to-End Deep Learning.

Radiology
Background Visual assessment of amyloid PET scans relies on the availability of radiologist expertise, whereas quantification of amyloid burden typically involves MRI for processing and analysis, which can be computationally expensive. Purpose To dev...

Assistive tools for classifying neurological disorders using fMRI and deep learning: A guide and example.

Brain and behavior
BACKGROUND: Deep-learning (DL) methods are rapidly changing the way researchers classify neurological disorders. For example, combining functional magnetic resonance imaging (fMRI) and DL has helped researchers identify functional biomarkers of neuro...

In vivo neuropil density from anatomical MRI and machine learning.

Cerebral cortex (New York, N.Y. : 1991)
Brain energy budgets specify metabolic costs emerging from underlying mechanisms of cellular and synaptic activities. While current bottom-up energy budgets use prototypical values of cellular density and synaptic density, predicting metabolism from ...

Early autism diagnosis based on path signature and Siamese unsupervised feature compressor.

Cerebral cortex (New York, N.Y. : 1991)
Autism spectrum disorder has been emerging as a growing public health threat. Early diagnosis of autism spectrum disorder is crucial for timely, effective intervention and treatment. However, conventional diagnosis methods based on communications and...

Diagnostic effectiveness of deep learning-based MRI in predicting multiple sclerosis: A meta-analysis.

Neurosciences (Riyadh, Saudi Arabia)
OBJECTIVES: The brain and spinal cord, constituting the central nervous system (CNS), could be impacted by an inflammatory disease known as multiple sclerosis (MS). The convolutional neural networks (CNN), a machine learning method, can detect lesion...

Heterogeneous Forgetting Rates and Greedy Allocation in Slot-Based Memory Networks Promotes Signal Retention.

Neural computation
A key question in the neuroscience of memory encoding pertains to the mechanisms by which afferent stimuli are allocated within memory networks. This issue is especially pronounced in the domain of working memory, where capacity is finite. Presumably...