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Photic Stimulation

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Learning visual features under motion invariance.

Neural networks : the official journal of the International Neural Network Society
Humans are continuously exposed to a stream of visual data with a natural temporal structure. However, most successful computer vision algorithms work at image level, completely discarding the precious information carried by motion. In this paper, we...

Cross-modal dual subspace learning with adversarial network.

Neural networks : the official journal of the International Neural Network Society
Cross-modal retrieval has recently attracted much interest along with the rapid development of multimodal data, and effectively utilizing the complementary relationship of different modal data and eliminating the heterogeneous gap as much as possible...

Causal importance of low-level feature selectivity for generalization in image recognition.

Neural networks : the official journal of the International Neural Network Society
Although our brain and deep neural networks (DNNs) can perform high-level sensory-perception tasks, such as image or speech recognition, the inner mechanism of these hierarchical information-processing systems is poorly understood in both neuroscienc...

A neural circuit model for a contextual association task inspired by recommender systems.

Hippocampus
Behavioral data shows that humans and animals have the capacity to learn rules of associations applied to specific examples, and generalize these rules to a broad variety of contexts. This article focuses on neural circuit mechanisms to perform a con...

Separability and geometry of object manifolds in deep neural networks.

Nature communications
Stimuli are represented in the brain by the collective population responses of sensory neurons, and an object presented under varying conditions gives rise to a collection of neural population responses called an 'object manifold'. Changes in the obj...

Semantic and structural image segmentation for prosthetic vision.

PloS one
Prosthetic vision is being applied to partially recover the retinal stimulation of visually impaired people. However, the phosphenic images produced by the implants have very limited information bandwidth due to the poor resolution and lack of color ...

Machine learning validation of EEG+tACS artefact removal.

Journal of neural engineering
OBJECTIVE: Electroencephalography (EEG) recorded during transcranial alternating current simulation (tACS) is highly desirable in order to investigate brain dynamics during stimulation, but is corrupted by large amplitude stimulation artefacts. Artef...

Evaluating the Potential Gain of Auditory and Audiovisual Speech-Predictive Coding Using Deep Learning.

Neural computation
Sensory processing is increasingly conceived in a predictive framework in which neurons would constantly process the error signal resulting from the comparison of expected and observed stimuli. Surprisingly, few data exist on the accuracy of predicti...

Assessing various sensorimotor and cognitive functions in people with epilepsy is feasible with robotics.

Epilepsy & behavior : E&B
BACKGROUND: Epilepsy is a common neurological disorder characterized by recurrent seizures, along with comorbid cognitive and psychosocial impairment. Current gold standards of assessment can quantify cognitive and motor performance, but may not capt...

Adaptive feature extraction in EEG-based motor imagery BCI: tracking mental fatigue.

Journal of neural engineering
OBJECTIVE: Electroencephalogram (EEG) signals are non-stationary. This could be due to internal fluctuation of brain states such as fatigue, frustration, etc. This necessitates the development of adaptive brain-computer interfaces (BCI) whose perform...