AIMC Topic: Photic Stimulation
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Extending the Stabilized Supralinear Network model for binocular image processing.
Neural networks : the official journal of the International Neural Network Society
Mar 18, 2017
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
Mar 13, 2017
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 ...
Patch Based Multiple Instance Learning Algorithm for Object Tracking.
Computational intelligence and neuroscience
Feb 22, 2017
To deal with the problems of illumination changes or pose variations and serious partial occlusion, patch based multiple instance learning (P-MIL) algorithm is proposed. The algorithm divides an object into many blocks. Then, the online MIL algorithm...
Face Alignment With Deep Regression.
IEEE transactions on neural networks and learning systems
Nov 1, 2016
In this paper, we present a deep regression approach for face alignment. The deep regressor is a neural network that consists of a global layer and multistage local layers. The global layer estimates the initial face shape from the whole image, while...
A robot-based behavioural task to quantify impairments in rapid motor decisions and actions after stroke.
Journal of neuroengineering and rehabilitation
Oct 10, 2016
BACKGROUND: Stroke can affect our ability to perform daily activities, although it can be difficult to identify the underlying functional impairment(s). Recent theories highlight the importance of sensory feedback in selecting future motor actions. T...
Objects Classification by Learning-Based Visual Saliency Model and Convolutional Neural Network.
Computational intelligence and neuroscience
Oct 10, 2016
Humans can easily classify different kinds of objects whereas it is quite difficult for computers. As a hot and difficult problem, objects classification has been receiving extensive interests with broad prospects. Inspired by neuroscience, deep lear...
From free energy to expected energy: Improving energy-based value function approximation in reinforcement learning.
Neural networks : the official journal of the International Neural Network Society
Aug 26, 2016
Free-energy based reinforcement learning (FERL) was proposed for learning in high-dimensional state and action spaces. However, the FERL method does only really work well with binary, or close to binary, state input, where the number of active states...
Driving a Semiautonomous Mobile Robotic Car Controlled by an SSVEP-Based BCI.
Computational intelligence and neuroscience
Jul 26, 2016
Brain-computer interfaces represent a range of acknowledged technologies that translate brain activity into computer commands. The aim of our research is to develop and evaluate a BCI control application for certain assistive technologies that can be...
Extreme learning machine and adaptive sparse representation for image classification.
Neural networks : the official journal of the International Neural Network Society
Jun 23, 2016
Recent research has shown the speed advantage of extreme learning machine (ELM) and the accuracy advantage of sparse representation classification (SRC) in the area of image classification. Those two methods, however, have their respective drawbacks,...
Pattern recognition for electroencephalographic signals based on continuous neural networks.
Neural networks : the official journal of the International Neural Network Society
Apr 6, 2016
This study reports the design and implementation of a pattern recognition algorithm to classify electroencephalographic (EEG) signals based on artificial neural networks (NN) described by ordinary differential equations (ODEs). The training method fo...
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