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Visual Perception

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A zero-shot learning approach to the development of brain-computer interfaces for image retrieval.

PloS one
Brain decoding-the process of inferring a person's momentary cognitive state from their brain activity-has enormous potential in the field of human-computer interaction. In this study we propose a zero-shot EEG-to-image brain decoding approach which ...

Modeling place cells and grid cells in multi-compartment environments: Entorhinal-hippocampal loop as a multisensory integration circuit.

Neural networks : the official journal of the International Neural Network Society
Hippocampal place cells and entorhinal grid cells are thought to form a representation of space by integrating internal and external sensory cues. Experimental data show that different subsets of place cells are controlled by vision, self-motion or a...

Exploring Duality in Visual Question-Driven Top-Down Saliency.

IEEE transactions on neural networks and learning systems
Top-down, goal-driven visual saliency exerts a huge influence on the human visual system for performing visual tasks. Text generations, like visual question answering (VQA) and visual question generation (VQG), have intrinsic connections with top-dow...

A visual encoding model based on deep neural networks and transfer learning for brain activity measured by functional magnetic resonance imaging.

Journal of neuroscience methods
BACKGROUND: Building visual encoding models to accurately predict visual responses is a central challenge for current vision-based brain-machine interface techniques. To achieve high prediction accuracy on neural signals, visual encoding models shoul...

Exploring spatiotemporal neural dynamics of the human visual cortex.

Human brain mapping
The human visual cortex is organized in a hierarchical manner. Although previous evidence supporting this hypothesis has been accumulated, specific details regarding the spatiotemporal information flow remain open. Here we present detailed spatiotemp...

Improving the repeatability of two-rate model parameter estimations by using autoencoder networks.

Progress in brain research
The adaptive changes elicited in visuomotor adaptation experiments are usually well explained at group level by two-rate models (Smith et al., 2006), but parameters fitted to individuals show considerable variance. Data cleaning can mitigate this pro...

Optimizing colour for camouflage and visibility using deep learning: the effects of the environment and the observer's visual system.

Journal of the Royal Society, Interface
Avoiding detection can provide significant survival advantages for prey, predators, or the military; conversely, maximizing visibility would be useful for signalling. One simple determinant of detectability is an animal's colour relative to its envir...

Number detectors spontaneously emerge in a deep neural network designed for visual object recognition.

Science advances
Humans and animals have a "number sense," an innate capability to intuitively assess the number of visual items in a set, its numerosity. This capability implies that mechanisms to extract numerosity indwell the brain's visual system, which is primar...

Adaptive neural network classifier for decoding MEG signals.

NeuroImage
We introduce two Convolutional Neural Network (CNN) classifiers optimized for inferring brain states from magnetoencephalographic (MEG) measurements. Network design follows a generative model of the electromagnetic (EEG and MEG) brain signals allowin...

Defining Image Memorability Using the Visual Memory Schema.

IEEE transactions on pattern analysis and machine intelligence
Memorability of an image is a characteristic determined by the human observers' ability to remember images they have seen. Yet recent work on image memorability defines it as an intrinsic property that can be obtained independent of the observer. The...