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Visual Perception

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The emergence of polychronization and feature binding in a spiking neural network model of the primate ventral visual system.

Psychological review
We present a hierarchical neural network model, in which subpopulations of neurons develop fixed and regularly repeating temporal chains of spikes (polychronization), which respond specifically to randomized Poisson spike trains representing the inpu...

Deep Neural Networks for Modeling Visual Perceptual Learning.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Understanding visual perceptual learning (VPL) has become increasingly more challenging as new phenomena are discovered with novel stimuli and training paradigms. Although existing models aid our knowledge of critical aspects of VPL, the connections ...

Efficient coding matters in the organization of the early visual system.

Neural networks : the official journal of the International Neural Network Society
Individual areas in the brain are organized into a hierarchical network as a result of evolution. Previous work indicated that the receptive fields (RFs) of individual areas have been evolved to favor metabolically efficient neural codes. In this pap...

Spatial and Time Domain Feature of ERP Speller System Extracted via Convolutional Neural Network.

Computational intelligence and neuroscience
Feature of event-related potential (ERP) has not been completely understood and illiteracy problem remains unsolved. To this end, P300 peak has been used as the feature of ERP in most brain-computer interface applications, but subjects who do not sho...

Computational mechanisms underlying cortical responses to the affordance properties of visual scenes.

PLoS computational biology
Biologically inspired deep convolutional neural networks (CNNs), trained for computer vision tasks, have been found to predict cortical responses with remarkable accuracy. However, the internal operations of these models remain poorly understood, and...

Using a model of human visual perception to improve deep learning.

Neural networks : the official journal of the International Neural Network Society
Deep learning algorithms achieve human-level (or better) performance on many tasks, but there still remain situations where humans learn better or faster. With regard to classification of images, we argue that some of those situations are because the...

A Machine Learning Approach to the Detection of Pilot's Reaction to Unexpected Events Based on EEG Signals.

Computational intelligence and neuroscience
This work considers the problem of utilizing electroencephalographic signals for use in systems designed for monitoring and enhancing the performance of aircraft pilots. Systems with such capabilities are generally referred to as cognitive cockpits. ...

Visual mental imagery: A view from artificial intelligence.

Cortex; a journal devoted to the study of the nervous system and behavior
This article investigates whether, and how, an artificial intelligence (AI) system can be said to use visual, imagery-based representations in a way that is analogous to the use of visual mental imagery by people. In particular, this article aims to ...

Pseudoneglect in Visual Search: Behavioral Evidence and Connectional Constraints in Simulated Neural Circuitry.

eNeuro
Most people tend to bisect horizontal lines slightly to the left of their true center (pseudoneglect) and start visual search from left-sided items. This physiological leftward spatial bias may depend on hemispheric asymmetries in the organization of...

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...