Tasks and their role in visual neuroscience
Vision is widely used as a model system to gain insights into how sensory inputs are
processed and interpreted by the brain. Historically, careful quantification and control of …
processed and interpreted by the brain. Historically, careful quantification and control of …
[HTML][HTML] A large and rich EEG dataset for modeling human visual object recognition
The human brain achieves visual object recognition through multiple stages of linear and
nonlinear transformations operating at a millisecond scale. To predict and explain these …
nonlinear transformations operating at a millisecond scale. To predict and explain these …
Advancing naturalistic affective science with deep learning
People express their own emotions and perceive others' emotions via a variety of channels,
including facial movements, body gestures, vocal prosody, and language. Studying these …
including facial movements, body gestures, vocal prosody, and language. Studying these …
Using deep neural networks to disentangle visual and semantic information in human perception and memory
Mental representations of familiar categories are composed of visual and semantic
information. Disentangling the contributions of visual and semantic information in humans is …
information. Disentangling the contributions of visual and semantic information in humans is …
Network communications flexibly predict visual contents that enhance representations for faster visual categorization
Models of visual cognition generally assume that brain networks predict the contents of a
stimulus to facilitate its subsequent categorization. However, understanding prediction and …
stimulus to facilitate its subsequent categorization. However, understanding prediction and …
Strength of predicted information content in the brain biases decision behavior
Prediction-for-perception theories suggest that the brain predicts incoming stimuli to facilitate
their categorization. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 However, it remains …
their categorization. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 However, it remains …
Simulation of Spatial and Temporal Distribution of Forest Carbon Stocks in Long Time Series—Based on Remote Sensing and Deep Learning
X Zhang, W Jia, Y Sun, F Wang, Y Miu - Forests, 2023 - mdpi.com
Due to the complexity and difficulty of forest resource ground surveys, remote-sensing-
based methods to assess forest resources and effectively plan management measures are …
based methods to assess forest resources and effectively plan management measures are …
Improved region proposal network for enhanced few-shot object detection
Z Shangguan, M Rostami - arXiv preprint arXiv:2308.07535, 2023 - arxiv.org
Despite significant success of deep learning in object detection tasks, the standard training
of deep neural networks requires access to a substantial quantity of annotated images …
of deep neural networks requires access to a substantial quantity of annotated images …
What makes a language easy to deep-learn?
Neural networks drive the success of natural language processing. A fundamental property
of language is its compositional structure, allowing humans to produce forms for new …
of language is its compositional structure, allowing humans to produce forms for new …
Multivariate analysis of brain activity patterns as a tool to understand predictive processes in speech perception
Speech perception is heavily influenced by our expectations about what will be said. In this
review, we discuss the potential of multivariate analysis as a tool to understand the neural …
review, we discuss the potential of multivariate analysis as a tool to understand the neural …