Evidence for Multiscale Multiplexed Representation of Visual Features in EEG

IF 2.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Hamid Karimi-Rouzbahani
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引用次数: 0

Abstract

Distinct neural processes such as sensory and memory processes are often encoded over distinct timescales of neural activations. Animal studies have shown that this multiscale coding strategy is also implemented for individual components of a single process, such as individual features of a multifeature stimulus in sensory coding. However, the generalizability of this encoding strategy to the human brain has remained unclear. We asked if individual features of visual stimuli were encoded over distinct timescales. We applied a multiscale time-resolved decoding method to electroencephalography (EEG) collected from human subjects presented with grating visual stimuli to estimate the timescale of individual stimulus features. We observed that the orientation and color of the stimuli were encoded in shorter timescales, whereas spatial frequency and the contrast of the same stimuli were encoded in longer timescales. The stimulus features appeared in temporally overlapping windows along the trial supporting a multiplexed coding strategy. These results provide evidence for a multiplexed, multiscale coding strategy in the human visual system.
脑电图中视觉特征多尺度复用表征的证据
不同的神经过程(如感觉和记忆过程)通常在不同的神经激活时间尺度上进行编码。动物研究表明,这种多尺度编码策略也适用于单个过程的单个成分,如感觉编码中多特征刺激的单个特征。然而,这种编码策略在人脑中的通用性仍不清楚。我们想知道视觉刺激的单个特征是否在不同的时间尺度上进行编码。我们将多尺度时间分辨解码方法应用于从人类受试者身上收集到的光栅视觉刺激脑电图(EEG),以估计单个刺激特征的时间尺度。我们观察到,刺激物的方向和颜色以较短的时间尺度编码,而相同刺激物的空间频率和对比度则以较长的时间尺度编码。刺激物的特征在试验过程中出现在时间上重叠的窗口中,这支持了多路编码策略。这些结果为人类视觉系统的多路复用、多尺度编码策略提供了证据。
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来源期刊
Neural Computation
Neural Computation 工程技术-计算机:人工智能
CiteScore
6.30
自引率
3.40%
发文量
83
审稿时长
3.0 months
期刊介绍: Neural Computation is uniquely positioned at the crossroads between neuroscience and TMCS and welcomes the submission of original papers from all areas of TMCS, including: Advanced experimental design; Analysis of chemical sensor data; Connectomic reconstructions; Analysis of multielectrode and optical recordings; Genetic data for cell identity; Analysis of behavioral data; Multiscale models; Analysis of molecular mechanisms; Neuroinformatics; Analysis of brain imaging data; Neuromorphic engineering; Principles of neural coding, computation, circuit dynamics, and plasticity; Theories of brain function.
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