A Chimera Model for Motion Anticipation in the Retina and the Primary Visual Cortex.

IF 2.1 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jérôme Emonet, Selma Souihel, Frédéric Chavane, Alain Destexhe, Matteo di Volo, Bruno Cessac
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引用次数: 0

Abstract

We propose a mean field model of the primary visual cortex (V1), connected to a realistic retina model, to study the impact of the retina on motion anticipation. We first consider the case where the retina does not itself provide anticipation-which is then only triggered by a cortical mechanism, the "anticipation by latency"-and unravel the effects of the retinal input amplitude, of stimulus features such as speed and contrast and of the size of cortical extensions and fiber conduction speed. Then we explore the changes in the cortical wave of anticipation when V1 is triggered by retina-driven anticipatory mechanisms: gain control and lateral inhibition by amacrine cells. Here, we show how retinal and cortical anticipation combine to provide an efficient processing where the simulated cortical response is in advance over the moving object that triggers this response, compensating the delays in visual processing.

视网膜和初级视觉皮层运动预期的嵌合体模型。
我们提出了一个初级视觉皮层(V1)的平均场模型,连接到一个真实的视网膜模型,研究视网膜对运动预期的影响。我们首先考虑视网膜本身不提供预期的情况——这种预期只能由一种皮层机制触发,即“潜伏期预期”——并揭示视网膜输入幅度、刺激特征(如速度和对比度)、皮质延伸的大小和纤维传导速度的影响。然后,我们探讨了视网膜驱动的预期机制:无毛细胞的增益控制和侧抑制触发V1时皮质预期波的变化。在这里,我们展示了视网膜和皮层预期如何结合起来提供一个有效的处理,其中模拟的皮层反应提前于触发该反应的移动物体,补偿视觉处理的延迟。
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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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