AIMC Topic: Brain

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Representations and generalization in artificial and brain neural networks.

Proceedings of the National Academy of Sciences of the United States of America
Humans and animals excel at generalizing from limited data, a capability yet to be fully replicated in artificial intelligence. This perspective investigates generalization in biological and artificial deep neural networks (DNNs), in both in-distribu...

Robot-aided assessment and associated brain lesions of impaired ankle proprioception in chronic stroke.

Journal of neuroengineering and rehabilitation
BACKGROUND: Impaired ankle proprioception strongly predicts balance dysfunction in chronic stroke. However, only sparse data on ankle position sense and no systematic data on ankle motion sense dysfunction in stroke are available. Moreover, the lesio...

Evaluation of machine learning-based classification of clinical impairment and prediction of clinical worsening in multiple sclerosis.

Journal of neurology
BACKGROUND: Robust predictive models of clinical impairment and worsening in multiple sclerosis (MS) are needed to identify patients at risk and optimize treatment strategies.

Learning sequence attractors in recurrent networks with hidden neurons.

Neural networks : the official journal of the International Neural Network Society
The brain is targeted for processing temporal sequence information. It remains largely unclear how the brain learns to store and retrieve sequence memories. Here, we study how recurrent networks of binary neurons learn sequence attractors to store pr...

Siamese Graph Convolutional Network quantifies increasing structure-function discrepancy over the cognitive decline continuum.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Alzheimer's disease dementia (ADD) is well known to induce alterations in both structural and functional brain connectivity. However, reported changes in connectivity are mostly limited to global/local network features, whic...

Assessment of Deep Learning-Based Triage Application for Acute Ischemic Stroke on Brain MRI in the ER.

Academic radiology
RATIONALE AND OBJECTIVES: To assess a deep learning application (DLA) for acute ischemic stroke (AIS) detection on brain magnetic resonance imaging (MRI) in the emergency room (ER) and the effect of T2-weighted imaging (T2WI) on its performance.

On convergence properties of the brain-state-in-a-convex-domain.

Neural networks : the official journal of the International Neural Network Society
Convergence in the presence of multiple equilibrium points is one of the most fundamental dynamical properties of a neural network (NN). Goal of the paper is to investigate convergence for the classic Brain-State-in-a-Box (BSB) NN model and some of i...

Spontaneous brain activity in patients with central retinal artery occlusion: a resting-state functional MRI study using machine learning.

Neuroreport
Central retinal artery occlusion (CRAO) is a serious eye condition that poses a risk to vision, resulting from the blockage of the central retinal artery. Because of the anatomical connection between the ocular artery, which derives from the internal...

Insights into traditional Large Deformation Diffeomorphic Metric Mapping and unsupervised deep-learning for diffeomorphic registration and their evaluation.

Computers in biology and medicine
This paper explores the connections between traditional Large Deformation Diffeomorphic Metric Mapping methods and unsupervised deep-learning approaches for non-rigid registration, particularly emphasizing diffeomorphic registration. The study provid...

Multivariate modeling and prediction of cerebral physiology in acute traumatic neural injury: A scoping review.

Computers in biology and medicine
Traumatic brain injury (TBI) poses a significant global public health challenge necessitating a profound understanding of cerebral physiology. The dynamic nature of TBI demands sophisticated methodologies for modeling and predicting cerebral signals ...