AIMC Topic: Brain

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Limits of Neural Computation in Humans and Machines.

Science and engineering ethics
Aicardi et al. (Ethical and social aspects of neurorobotics, Science and Engineering Ethics, 2020) look to neuroscience to mitigate the limitations of current robotics technology. They propose that robotics technology guided by neuroscience has the c...

Keeping the "Human in the Loop" in the Age of Artificial Intelligence : Accompanying Commentary for "Correcting the Brain?" by Rainey and Erden.

Science and engineering ethics
The benefits of Artificial Intelligence (AI) in medicine are unquestionable and it is unlikely that the pace of its development will slow down. From better diagnosis, prognosis, and prevention to more precise surgical procedures, AI has the potential...

Large-Scale Structural Covariance Networks Predict Age in Middle-to-Late Adulthood: A Novel Brain Aging Biomarker.

Cerebral cortex (New York, N.Y. : 1991)
The aging process is accompanied by changes in the brain's cortex at many levels. There is growing interest in summarizing these complex brain-aging profiles into a single, quantitative index that could serve as a biomarker both for characterizing in...

Evaluating the Sensitivity of Resting-State BOLD Variability to Age and Cognition after Controlling for Motion and Cardiovascular Influences: A Network-Based Approach.

Cerebral cortex (New York, N.Y. : 1991)
Recent functional magnetic resonance imaging (fMRI) studies report that moment-to-moment variability in the BOLD signal is related to differences in age and cognition and, thus, may be sensitive to age-dependent decline. However, head motion and/or c...

Regularized Bagged Canonical Component Analysis for Multiclass Learning in Brain Imaging.

Neuroinformatics
A fundamental problem of supervised learning algorithms for brain imaging applications is that the number of features far exceeds the number of subjects. In this paper, we propose a combined feature selection and extraction approach for multiclass pr...

Using deep-learning algorithms to classify fetal brain ultrasound images as normal or abnormal.

Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology
OBJECTIVES: To evaluate the feasibility of using deep-learning algorithms to classify as normal or abnormal sonographic images of the fetal brain obtained in standard axial planes.

The human brain reveals resting state activity patterns that are predictive of biases in attitudes toward robots.

Science robotics
The increasing presence of robots in society necessitates a deeper understanding into what attitudes people have toward robots. People may treat robots as mechanistic artifacts or may consider them to be intentional agents. This might result in expla...

Artificial Neural Networks for Neuroscientists: A Primer.

Neuron
Artificial neural networks (ANNs) are essential tools in machine learning that have drawn increasing attention in neuroscience. Besides offering powerful techniques for data analysis, ANNs provide a new approach for neuroscientists to build models fo...