AIMC Topic:
Learning

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Invertible generalized synchronization: A putative mechanism for implicit learning in neural systems.

Chaos (Woodbury, N.Y.) Jun 1, 2020
Regardless of the marked differences between biological and artificial neural systems, one fundamental similarity is that they are essentially dynamical systems that can learn to imitate other dynamical systems whose governing equations are unknown. ...
Humans Models, Neurological Synapses Nerve Net Neural Networks, Computer Learning
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Reinforcement Learning: Full Glass or Empty - Depends Who You Ask.

Current biology : CB Apr 6, 2020
An extension of the prediction error theory of dopamine, imported from artificial intelligence, represents the full distribution over future rewards rather than only the average and better explains dopamine responses.
Artificial Intelligence Learning Reward Dopamine Reinforcement, Psychology
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STDP Forms Associations between Memory Traces in Networks of Spiking Neurons.

Cerebral cortex (New York, N.Y. : 1991) Mar 14, 2020
Memory traces and associations between them are fundamental for cognitive brain function. Neuron recordings suggest that distributed assemblies of neurons in the brain serve as memory traces for spatial information, real-world items, and concepts. Ho...
Memory Action Potentials Neurons Neural Networks, Computer Humans Learning Neuronal Plasticity Models, Neurological
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Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks.

Neuron Feb 5, 2020
Evolution is a blind fitting process by which organisms become adapted to their environment. Does the brain use similar brute-force fitting processes to learn how to perceive and act upon the world? Recent advances in artificial neural networks have ...
Brain Learning Humans Neural Networks, Computer Nerve Net Biological Evolution
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Integrating Convolutional Neural Networks and Multi-Task Dictionary Learning for Cognitive Decline Prediction with Longitudinal Images.

Journal of Alzheimer's disease : JAD Jan 1, 2020
BACKGROUND: Disease progression prediction based on neuroimaging biomarkers is vital in Alzheimer's disease (AD) research. Convolutional neural networks (CNN) have been proved to be powerful for various computer vision research by refining reliable a...
Aged Male Disease Progression Neural Networks, Computer Cognitive Dysfunction Chromans Longitudinal Studies Humans Learning Mental Status and Dementia Tests Image Interpretation, Computer-Assisted
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Density and Distinctiveness in Early Word Learning: Evidence From Neural Network Simulations.

Cognitive science Jan 1, 2020
High phonological neighborhood density has been associated with both advantages and disadvantages in early word learning. High density may support the formation and fine-tuning of new word sound memories-a process termed lexical configuration (e.g., ...
Linguistics Verbal Learning Phonetics Humans Neural Networks, Computer Learning Child Computer Simulation
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A Review of Perceptual Expertise in Radiology-How it develops, How we can test it, and Why humans still matter in the era of Artificial Intelligence.

Academic radiology Jan 1, 2020
As the first step in image interpretation is detection, an error in perception can prematurely end the diagnostic process leading to missed diagnoses. Because perceptual errors of this sort-"failure to detect"-are the most common interpretive error (...
Artificial Intelligence Learning Radiography Radiology Humans
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L2TOR: Second-Language Tutoring Using Social Robots.

Cyberpsychology, behavior and social networking Oct 1, 2019
Humans Language Multilingualism Learning Teaching Interpersonal Relations Robotics
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GOAL-ROBOTS: Goal-Based Open-Ended Autonomous Learning Robots.

Cyberpsychology, behavior and social networking Sep 1, 2019
Goals Interdisciplinary Research Humans Learning Artificial Intelligence Robotics
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Evaluating the Validity of a Knowledge-Based System for Proactive Knowledge Transfer for Caregiving Relatives.

Studies in health technology and informatics Aug 21, 2019
The evaluation of the validity of a knowledge-based system is of great importance during its development. It determines whether the system represents the experts' knowledge correctly. This is highly important, but also particularly difficult, if the ...
Knowledge Expert Testimony Learning Knowledge Bases
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