Journal ArticleDOI
Speed of processing in the human visual system.
TLDR
The visual processing needed to perform this highly demanding task can be achieved in under 150 ms, and ERP analysis revealed a frontal negativity specific to no-go trials that develops roughly 150 ms after stimulus onset.Abstract:
How long does it take for the human visual system to process a complex natural image? Subjectively, recognition of familiar objects and scenes appears to be virtually instantaneous, but measuring this processing time experimentally has proved difficult. Behavioural measures such as reaction times can be used, but these include not only visual processing but also the time required for response execution. However, event-related potentials (ERPs) can sometimes reveal signs of neural processing well before the motor output. Here we use a go/no-go categorization task in which subjects have to decide whether a previously unseen photograph, flashed on for just 20 ms, contains an animal. ERP analysis revealed a frontal negativity specific to no-go trials that develops roughly 150 ms after stimulus onset. We conclude that the visual processing needed to perform this highly demanding task can be achieved in under 150 ms.read more
Citations
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Journal ArticleDOI
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky,Jia Deng,Hao Su,Jonathan Krause,Sanjeev Satheesh,Sean Ma,Zhiheng Huang,Andrej Karpathy,Aditya Khosla,Michael S. Bernstein,Alexander C. Berg,Li Fei-Fei +11 more
TL;DR: The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) as mentioned in this paper is a benchmark in object category classification and detection on hundreds of object categories and millions of images, which has been run annually from 2010 to present, attracting participation from more than fifty institutions.
Proceedings ArticleDOI
A Bayesian hierarchical model for learning natural scene categories
Li Fei-Fei,Pietro Perona +1 more
TL;DR: This work proposes a novel approach to learn and recognize natural scene categories by representing the image of a scene by a collection of local regions, denoted as codewords obtained by unsupervised learning.
Journal ArticleDOI
Hierarchical models of object recognition in cortex
TL;DR: A new hierarchical model consistent with physiological data from inferotemporal cortex that accounts for this complex visual task and makes testable predictions is described.
Journal ArticleDOI
Dynamic predictions: Oscillations and synchrony in top–down processing
TL;DR: It is argued that coherence among subthreshold membrane potential fluctuations could be exploited to express selective functional relationships during states of expectancy or attention, and these dynamic patterns could allow the grouping and selection of distributed neuronal responses for further processing.
Book
Spiking Neuron Models: Single Neurons, Populations, Plasticity
TL;DR: A comparison of single and two-dimensional neuron models for spiking neuron models and models of Synaptic Plasticity shows that the former are superior to the latter, while the latter are better suited to population models.