Crashes in visual processing occur when neurons processing one image are tasked with processing another too quickly. This results in either one or both images being unable to reach our conscious awareness.
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EmoNet, a new convolutional neural network, can accurately decode images into eleven distinct emotional categories. Training the AI on over 25,000 images, researchers demonstrate image content is sufficient to predict the category and valence of human emotions.
An asymmetric coupling between the peripheral visual and olfactory sensory systems allows for enhanced steering response to discrete objects in mosquitos.
The human brain can desensitize background motion and focus on smaller moving objects in the foreground as a result of activity in the middle temporal visual area. However, our ability to pick out smaller objects changes over time. Younger people are better at picking out foreground objects moving, while those over 65 have heightened awareness of objects moving in the background.
People are able to form the correct mental model of puzzles from either visual or haptic experiences alone and are able to predict haptic properties from visual ones. Findings suggest humans segment scenes into objects without explicit boundary cues by using purely statistical information.
Optical illusions are helping researchers better understand attention and visual perception. Findings suggest attention operates periodically on the perceptual binding of visual information.
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Results from a new artificial intelligence study indicate number sense is spontaneously created by the visual system, without prior experience of counting.
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A new deep learning algorithm can reliably determine what visual stimuli neurons in the visual cortex respond best to.
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Current deep learning models are able to create images strongly enough to activate specific neurons in the visual cortex. However, researchers say more accurate artificial neural network models should be developed to help produce more accurate control.
CT1 cells connect around 1400 areas in the fly brain. Each cell area works like a separate neuron, allowing CT1 to access information from the fly's eye and support local motion detection.