Neural networks : the official journal of the International Neural Network Society
Mar 8, 2022
Humans learn to recognize and manipulate new objects in lifelong settings without forgetting the previously gained knowledge under non-stationary and sequential conditions. In autonomous systems, the agents also need to mitigate similar behaviour to ...
Recent stereo matching methods, especially end-to-end deep stereo matching networks, have achieved remarkable performance in the fields of autonomous driving and depth sensing. However, state-of-the-art stereo algorithms, even with the deep neural ne...
Stimulus images can be reconstructed from visual cortical activity. However, our perception of stimuli is shaped by both stimulus-induced and top-down processes, and it is unclear whether and how reconstructions reflect top-down aspects of perception...
Neural decoding can be conceptualized as the problem of mapping brain responses back to sensory stimuli via a feature space. We introduce (i) a novel experimental paradigm that uses well-controlled yet highly naturalistic stimuli with a priori known ...
Neural networks : the official journal of the International Neural Network Society
Dec 17, 2021
Deep Convolutional Neural Networks (DNNs) have achieved superhuman accuracy on standard image classification benchmarks. Their success has reignited significant interest in their use as models of the primate visual system, bolstered by claims of thei...
Computer methods and programs in biomedicine
Dec 14, 2021
BACKGROUND AND OBJECTIVE: Most studies used neural activities evoked by linguistic stimuli such as phrases or sentences to decode the language structure. However, compared to linguistic stimuli, it is more common for the human brain to perceive the o...
International journal of neural systems
Dec 11, 2021
Color has an important role in object recognition and visual working memory (VWM). Decoding color VWM in the human brain is helpful to understand the mechanism of visual cognitive process and evaluate memory ability. Recently, several studies showed ...
Early theories of efficient coding suggested the visual system could compress the world by learning to represent features where information was concentrated, such as contours. This view was validated by the discovery that neurons in posterior visual ...
Here we examine the plausibility of deep convolutional neural networks (CNNs) as a theoretical framework for understanding biological vision in the context of image classification. Recent work on object recognition in human vision has shown that both...
IEEE transactions on pattern analysis and machine intelligence
Oct 1, 2021
This work presents a novel method of exploring human brain-visual representations, with a view towards replicating these processes in machines. The core idea is to learn plausible computational and biological representations by correlating human neur...