AI Medical Compendium Topic

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Vision, Ocular

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Optimizing Deeper Spiking Neural Networks for Dynamic Vision Sensing.

Neural networks : the official journal of the International Neural Network Society
Spiking Neural Networks (SNNs) have recently emerged as a new generation of low-power deep neural networks due to sparse, asynchronous, and binary event-driven processing. Most previous deep SNN optimization methods focus on static datasets (e.g., MN...

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature.

Sensors (Basel, Switzerland)
Today, computer vision algorithms are very important for different fields and applications, such as closed-circuit television security, health status monitoring, and recognizing a specific person or object and robotics. Regarding this topic, the pres...

Uncovering structured responses of neural populations recorded from macaque monkeys with linear support vector machines.

STAR protocols
When a mammal, such as a macaque monkey, sees a complex natural image, many neurons in its visual cortex respond simultaneously. Here, we provide a protocol for studying the structure of population responses in laminar recordings with a machine learn...

Vision-Based Hybrid Controller to Release a 4-DOF Parallel Robot from a Type II Singularity.

Sensors (Basel, Switzerland)
The high accuracy and dynamic performance of parallel robots (PRs) make them suitable to ensure safe operation in human-robot interaction. However, these advantages come at the expense of a reduced workspace and the possible appearance of type II sin...

No-Reference Screen Content Image Quality Assessment With Unsupervised Domain Adaptation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
In this paper, we quest the capability of transferring the quality of natural scene images to the images that are not acquired by optical cameras (e.g., screen content images, SCIs), rooted in the widely accepted view that the human visual system has...

Unsupervised foveal vision neural architecture with top-down attention.

Neural networks : the official journal of the International Neural Network Society
Deep learning architectures are an extremely powerful tool for recognizing and classifying images. However, they require supervised learning and normally work on vectors of the size of image pixels and produce the best results when trained on million...

Limits to visual representational correspondence between convolutional neural networks and the human brain.

Nature communications
Convolutional neural networks (CNNs) are increasingly used to model human vision due to their high object categorization capabilities and general correspondence with human brain responses. Here we evaluate the performance of 14 different CNNs compare...

One-shot object parsing in newborn chicks.

Journal of experimental psychology. General
Controlled-rearing studies provide the unique opportunity to examine which psychological mechanisms are present at birth and which mechanisms emerge from experience. Here we show that one core component of visual perception-the ability to parse objec...

Bioinspired multisensory neural network with crossmodal integration and recognition.

Nature communications
The integration and interaction of vision, touch, hearing, smell, and taste in the human multisensory neural network facilitate high-level cognitive functionalities, such as crossmodal integration, recognition, and imagination for accurate evaluation...

Deep learning-based pupil model predicts time and spectral dependent light responses.

Scientific reports
Although research has made significant findings in the neurophysiological process behind the pupillary light reflex, the temporal prediction of the pupil diameter triggered by polychromatic or chromatic stimulus spectra is still not possible. State o...