A computational study of the role of spatial receptive field structure in processing natural and non-natural scenes
A computational study of the role of spatial receptive field structure in processing natural and non-natural scenes
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DOI：
10.1016/j.jtbi.2018.06.011
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发表时间：
2018-10-07
影响因子：
2
通讯作者：
Zhu, Xiuqi George
中科院分区：
文献类型：
--
作者：
Barranca, Victor J.;Zhu, Xiuqi George
视觉系统中普遍存在的中心-环绕感受野结构被认为在图像处理任务中具有进化优势。我们解决了潜在的功能优势和缺点的空间定位和中心-周围拮抗作用的背景下，集成和消防神经网络模型与基于图像的强制。利用自然场景的稀疏性，我们推导出一个压缩感知框架，利用诱发神经元放电率的输入图像重建。我们调查如何输入编码的准确性取决于感受野的架构，并表明，在视觉刺激采样的空间定位有利于显着改善自然场景处理超越均匀随机兴奋连接。然而，对于特定类别的图像，我们表明，空间定位固有的生理感受野结合信息丢失，通过非线性神经网络动力学可能是常见的视错觉的基础，给他们的表现一个新的解释。在信号处理的背景下，我们预计这项工作可能会建议新的采样协议，用于扩展传统的压缩感知理论。(C)2018爱思唯尔有限公司版权所有
The center-surround receptive field structure, ubiquitous in the visual system, is hypothesized to be evolutionarily advantageous in image processing tasks. We address the potential functional benefits and shortcomings of spatial localization and center-surround antagonism in the context of an integrate-and-fire neuronal network model with image-based forcing. Utilizing the sparsity of natural scenes, we derive a compressive-sensing framework for input image reconstruction utilizing evoked neuronal firing rates. We investigate how the accuracy of input encoding depends on the receptive field architecture, and demonstrate that spatial localization in visual stimulus sampling facilitates marked improvements in natural scene processing beyond uniformly-random excitatory connectivity. However, for specific classes of images, we show that spatial localization inherent in physiological receptive fields combined with information loss through nonlinear neuronal network dynamics may underlie common optical illusions, giving a novel explanation for their manifestation. In the context of signal processing, we expect this work may suggest new sampling protocols useful for extending conventional compressive sensing theory. (C) 2018 Elsevier Ltd. All rights reserved.