Analyzing top-down visual attention in the context of gamma oscillations: a layer- dependent network-of- networks approach.

IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Frontiers in Computational Neuroscience Pub Date : 2024-09-23 eCollection Date: 2024-01-01 DOI:10.3389/fncom.2024.1439632
Tianyi Zheng, Masato Sugino, Yasuhiko Jimbo, G Bard Ermentrout, Kiyoshi Kotani
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Abstract

Top-down visual attention is a fundamental cognitive process that allows individuals to selectively attend to salient visual stimuli in the environment. Recent empirical findings have revealed that gamma oscillations participate in the modulation of visual attention. However, computational studies face challenges when analyzing the attentional process in the context of gamma oscillation due to the unstable nature of gamma oscillations and the complexity induced by the layered fashion in the visual cortex. In this study, we propose a layer-dependent network-of-networks approach to analyze such attention with gamma oscillations. The model is validated by reproducing empirical findings on orientation preference and the enhancement of neuronal response due to top-down attention. We perform parameter plane analysis to classify neuronal responses into several patterns and find that the neuronal response to sensory and attention signals was modulated by the heterogeneity of the neuronal population. Furthermore, we revealed a counter-intuitive scenario that the excitatory populations in layer 2/3 and layer 5 exhibit opposite responses to the attentional input. By modification of the original model, we confirmed layer 6 plays an indispensable role in such cases. Our findings uncover the layer-dependent dynamics in the cortical processing of visual attention and open up new possibilities for further research on layer-dependent properties in the cerebral cortex.

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在伽马振荡背景下分析自上而下的视觉注意力:一种依赖层的网络方法。
自上而下的视觉注意是一种基本的认知过程,它能让人有选择地注意环境中的显著视觉刺激。最近的实证研究发现,伽马振荡参与了视觉注意力的调节。然而,由于伽马振荡的不稳定性和视觉皮层分层方式的复杂性,计算研究在分析伽马振荡背景下的注意过程时面临挑战。在本研究中,我们提出了一种层依赖网络(network-of-networks)方法来分析伽马振荡下的注意力。该模型通过再现方位偏好和自上而下注意引起的神经元反应增强的经验发现得到了验证。我们进行了参数平面分析,将神经元反应分为几种模式,并发现神经元对感觉和注意力信号的反应受神经元群异质性的调节。此外,我们还发现了一种与直觉相反的情况,即第 2/3 层和第 5 层的兴奋神经元群对注意输入的反应相反。通过修改原始模型,我们证实第 6 层在这种情况下发挥着不可或缺的作用。我们的发现揭示了大脑皮层处理视觉注意力过程中的层依赖动态,为进一步研究大脑皮层的层依赖特性提供了新的可能性。
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来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
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