AIMC Topic: Photic Stimulation

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A border-ownership model based on computational electromagnetism.

Neural networks : the official journal of the International Neural Network Society
The mathematical relation between a vector electric field and its corresponding scalar potential field is useful to formulate computational problems of lower/middle-order visual processing, specifically related to the assignment of borders to the sid...

STDP-based spiking deep convolutional neural networks for object recognition.

Neural networks : the official journal of the International Neural Network Society
Previous studies have shown that spike-timing-dependent plasticity (STDP) can be used in spiking neural networks (SNN) to extract visual features of low or intermediate complexity in an unsupervised manner. These studies, however, used relatively sha...

Decoding of finger trajectory from ECoG using deep learning.

Journal of neural engineering
OBJECTIVE: Conventional decoding pipeline for brain-machine interfaces (BMIs) consists of chained different stages of feature extraction, time-frequency analysis and statistical learning models. Each of these stages uses a different algorithm trained...

Modeling mesoscopic cortical dynamics using a mean-field model of conductance-based networks of adaptive exponential integrate-and-fire neurons.

Journal of computational neuroscience
Voltage-sensitive dye imaging (VSDi) has revealed fundamental properties of neocortical processing at macroscopic scales. Since for each pixel VSDi signals report the average membrane potential over hundreds of neurons, it seems natural to use a mean...

Code-modulated visual evoked potentials using fast stimulus presentation and spatiotemporal beamformer decoding.

Scientific reports
When encoding visual targets using various lagged versions of a pseudorandom binary sequence of luminance changes, the EEG signal recorded over the viewer's occipital pole exhibits so-called code-modulated visual evoked potentials (cVEPs), the phase ...

Deep Manifold Learning Combined With Convolutional Neural Networks for Action Recognition.

IEEE transactions on neural networks and learning systems
Learning deep representations have been applied in action recognition widely. However, there have been a few investigations on how to utilize the structural manifold information among different action videos to enhance the recognition accuracy and ef...

A machine learning approach for automated wide-range frequency tagging analysis in embedded neuromonitoring systems.

Methods (San Diego, Calif.)
EEG is a standard non-invasive technique used in neural disease diagnostics and neurosciences. Frequency-tagging is an increasingly popular experimental paradigm that efficiently tests brain function by measuring EEG responses to periodic stimulation...

Extending the Stabilized Supralinear Network model for binocular image processing.

Neural networks : the official journal of the International Neural Network Society
The visual cortex is both extensive and intricate. Computational models are needed to clarify the relationships between its local mechanisms and high-level functions. The Stabilized Supralinear Network (SSN) model was recently shown to account for ma...

Dynamic neural architecture for social knowledge retrieval.

Proceedings of the National Academy of Sciences of the United States of America
Social behavior is often shaped by the rich storehouse of biographical information that we hold for other people. In our daily life, we rapidly and flexibly retrieve a host of biographical details about individuals in our social network, which often ...