AI Medical Compendium Topic:
Learning

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Population coding in the cerebellum: a machine learning perspective.

Journal of neurophysiology
The cere resembles a feedforward, three-layer network of neurons in which the "hidden layer" consists of Purkinje cells (P-cells) and the output layer consists of deep cerebellar nucleus (DCN) neurons. In this analogy, the output of each DCN neuron i...

Federated Learning on Clinical Benchmark Data: Performance Assessment.

Journal of medical Internet research
BACKGROUND: Federated learning (FL) is a newly proposed machine-learning method that uses a decentralized dataset. Since data transfer is not necessary for the learning process in FL, there is a significant advantage in protecting personal privacy. T...

Flexible Working Memory Through Selective Gating and Attentional Tagging.

Neural computation
Working memory is essential: it serves to guide intelligent behavior of humans and nonhuman primates when task-relevant stimuli are no longer present to the senses. Moreover, complex tasks often require that multiple working memory representations ca...

The covariance perceptron: A new paradigm for classification and processing of time series in recurrent neuronal networks.

PLoS computational biology
Learning in neuronal networks has developed in many directions, in particular to reproduce cognitive tasks like image recognition and speech processing. Implementations have been inspired by stereotypical neuronal responses like tuning curves in the ...

Understanding Human Intelligence through Human Limitations.

Trends in cognitive sciences
Recent progress in artificial intelligence provides the opportunity to ask the question of what is unique about human intelligence, but with a new comparison class. I argue that we can understand human intelligence, and the ways in which it may diffe...

Kinematic parameters obtained with the ArmeoSpring for upper-limb assessment after stroke: a reliability and learning effect study for guiding parameter use.

Journal of neuroengineering and rehabilitation
BACKGROUND: After stroke, kinematic measures obtained with non-robotic and robotic devices are highly recommended to precisely quantify the sensorimotor impairments of the upper-extremity and select the most relevant therapeutic strategies. Although ...

Biological batch normalisation: How intrinsic plasticity improves learning in deep neural networks.

PloS one
In this work, we present a local intrinsic rule that we developed, dubbed IP, inspired by the Infomax rule. Like Infomax, this rule works by controlling the gain and bias of a neuron to regulate its rate of fire. We discuss the biological plausibilit...

Learning to select actions shapes recurrent dynamics in the corticostriatal system.

Neural networks : the official journal of the International Neural Network Society
Learning to select appropriate actions based on their values is fundamental to adaptive behavior. This form of learning is supported by fronto-striatal systems. The dorsal-lateral prefrontal cortex (dlPFC) and the dorsal striatum (dSTR), which are st...

3-D Inorganic Crystal Structure Generation and Property Prediction via Representation Learning.

Journal of chemical information and modeling
Generative models have been successfully used to synthesize completely novel images, text, music, and speech. As such, they present an exciting opportunity for the design of new materials for functional applications. So far, generative deep-learning ...

Training Radiology Residents, Bloom Style.

Academic radiology
Bloom's Taxonomy, an integral component of learning theory since its inception, describes cognitive skill levels in increasing complexity (Remember, Understand, Apply, Analyze, Evaluate, and Create). Considering Bloom's Taxonomy when writing learning...