AIMC Topic: Learning

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Incremental model-based reinforcement learning with model constraint.

Neural networks : the official journal of the International Neural Network Society Feb 8, 2025
In model-based reinforcement learning (RL) approaches, the estimated model of a real environment is learned with limited data and then utilized for policy optimization. As a result, the policy optimization process in model-based RL is influenced by b...
Algorithms Learning Reinforcement, Psychology Humans Neural Networks, Computer
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Tuned Compositional Feature Replays for Efficient Stream Learning.

IEEE transactions on neural networks and learning systems Feb 6, 2025
Our brains extract durable, generalizable knowledge from transient experiences of the world. Artificial neural networks come nowhere close to this ability. When tasked with learning to classify objects by training on nonrepeating video frames in temp...
Neural Networks, Computer Algorithms Memory Learning Humans
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Memory-Dependent Computation and Learning in Spiking Neural Networks Through Hebbian Plasticity.

IEEE transactions on neural networks and learning systems Feb 6, 2025
Spiking neural networks (SNNs) are the basis for many energy-efficient neuromorphic hardware systems. While there has been substantial progress in SNN research, artificial SNNs still lack many capabilities of their biological counterparts. In biologi...
Computer Simulation Learning Neural Networks, Computer Machine Learning Action Potentials Humans Neurons Memory Neuronal Plasticity Models, Neurological
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Education and Training Assessment and Artificial Intelligence. A Pragmatic Guide for Educators.

British journal of biomedical science Feb 5, 2025
The emergence of ChatGPT and similar new Generative AI tools has created concern about the validity of many current assessment methods in higher education, since learners might use these tools to complete those assessments. Here we review the current...
Learning Humans Educational Measurement Artificial Intelligence
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Error fields: personalized robotic movement training that augments one's more likely mistakes.

Scientific reports Feb 4, 2025
Control of movement is learned and uses error feedback during practice to predict actions for the next movement. We previously showed that augmenting error can enhance learning, but while such findings are encouraging, the methods need to be refined ...
Movement Robotics Psychomotor Performance Adult Male Humans Female Young Adult Learning
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Hybrid neural networks for continual learning inspired by corticohippocampal circuits.

Nature communications Feb 2, 2025
Current artificial systems suffer from catastrophic forgetting during continual learning, a limitation absent in biological systems. Biological mechanisms leverage the dual representation of specific and generalized memories within corticohippocampal...
Neurons Hippocampus Animals Models, Neurological Memory Cerebral Cortex Nerve Net Humans Neural Networks, Computer Learning
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FxTS-Net: Fixed-time stable learning framework for Neural ODEs.

Neural networks : the official journal of the International Neural Network Society Feb 1, 2025
Neural Ordinary Differential Equations (Neural ODEs), as a novel category of modeling big data methods, cleverly link traditional neural networks and dynamical systems. However, it is challenging to ensure the dynamics system reaches a correctly pred...
Humans Time Factors Neural Networks, Computer Computer Simulation Machine Learning Learning Algorithms
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Exploring AI-Driven Feedback as a Cultural Tool: A Cultural-Historical Perspective on Design of AI Environments to Support Students' Writing Process.

Integrative psychological & behavioral science Feb 1, 2025
This study draws on the cultural-historical perspectives of Vygotsky and Galperin to examine the role of AI-generated feedback within the Assessment for Learning (AfL) process in fostering students' development as learners. By leveraging Galperin's c...
Humans Artificial Intelligence Students Writing Learning
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The calcitron: A simple neuron model that implements many learning rules via the calcium control hypothesis.

PLoS computational biology Jan 29, 2025
Theoretical neuroscientists and machine learning researchers have proposed a variety of learning rules to enable artificial neural networks to effectively perform both supervised and unsupervised learning tasks. It is not always clear, however, how t...
Neuronal Plasticity Models, Neurological Animals Humans Computational Biology Neural Networks, Computer Synapses Machine Learning Neurons Learning Calcium
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A systematic review of the impact of artificial intelligence on educational outcomes in health professions education.

BMC medical education Jan 27, 2025
BACKGROUND: Artificial intelligence (AI) has a variety of potential applications in health professions education and assessment; however, measurable educational impacts of AI-based educational strategies on learning outcomes have not been systematica...
Health Occupations Learning Humans Artificial Intelligence
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