AIMC Topic: Cognition
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Cognitive and MRI trajectories for prediction of Alzheimer's disease.
Scientific reports
Jan 22, 2021
The concept of Mild Cognitive Impairment (MCI) is used to describe the early stages of Alzheimer's disease (AD), and identification and treatment before further decline is an important clinical task. We selected longitudinal data from the ADNI databa...
Construction and Supervised Learning of Long-Term Grey Cognitive Networks.
IEEE transactions on cybernetics
Jan 15, 2021
Modeling a real-world system by means of a neural model involves numerous challenges that range from formulating transparent knowledge representations to obtaining reliable simulation errors. However, that knowledge is often difficult to formalize in...
PsychRNN: An Accessible and Flexible Python Package for Training Recurrent Neural Network Models on Cognitive Tasks.
eNeuro
Jan 15, 2021
Task-trained artificial recurrent neural networks (RNNs) provide a computational modeling framework of increasing interest and application in computational, systems, and cognitive neuroscience. RNNs can be trained, using deep-learning methods, to per...
Reconstructing seen image from brain activity by visually-guided cognitive representation and adversarial learning.
NeuroImage
Jan 1, 2021
Reconstructing perceived stimulus (image) only from human brain activity measured with functional Magnetic Resonance Imaging (fMRI) is a significant task in brain decoding. However, the inconsistent distribution and representation between fMRI signal...
Deep learning detection of informative features in tau PET for Alzheimer's disease classification.
BMC bioinformatics
Dec 28, 2020
BACKGROUND: Alzheimer's disease (AD) is the most common type of dementia, typically characterized by memory loss followed by progressive cognitive decline and functional impairment. Many clinical trials of potential therapies for AD have failed, and ...
Comparing feedforward and recurrent neural network architectures with human behavior in artificial grammar learning.
Scientific reports
Dec 17, 2020
In recent years artificial neural networks achieved performance close to or better than humans in several domains: tasks that were previously human prerogatives, such as language processing, have witnessed remarkable improvements in state of the art ...
Transforming task representations to perform novel tasks.
Proceedings of the National Academy of Sciences of the United States of America
Dec 10, 2020
An important aspect of intelligence is the ability to adapt to a novel task without any direct experience (zero shot), based on its relationship to previous tasks. Humans can exhibit this cognitive flexibility. By contrast, models that achieve superh...
Changes to information in working memory depend on distinct removal operations.
Nature communications
Dec 7, 2020
Holding information in working memory is essential for cognition, but removing unwanted thoughts is equally important. Here we use multivariate pattern analyses of brain activity to demonstrate the successful manipulation and removal of information f...
A Brain-Inspired Framework for Evolutionary Artificial General Intelligence.
IEEE transactions on neural networks and learning systems
Nov 30, 2020
From the medical field to agriculture, from energy to transportation, every industry is going through a revolution by embracing artificial intelligence (AI); nevertheless, AI is still in its infancy. Inspired by the evolution of the human brain, this...
Identification of contributing genes of Huntington's disease by machine learning.
BMC medical genomics
Nov 23, 2020
BACKGROUND: Huntington's disease (HD) is an inherited disorder caused by the polyglutamine (poly-Q) mutations of the HTT gene results in neurodegeneration characterized by chorea, loss of coordination, cognitive decline. However, HD pathogenesis is s...
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