A computational study of the role of spatial receptive field structure in processing natural and non-natural scenes

JOURNAL OF THEORETICAL BIOLOGY

卷:454

Article

Barranca, Victor J.¹  Zhu, Xiuqi George¹ 

[1] Swarthmore Coll, 500 Coll Ave, Swarthmore, PA 19081 USA

关键词: Sensory processing;    Neuronal networks;    Nonlinear dynamics;    Optical illusions;    Compressive sensing;   

DOI  :  10.1016/j.jtbi.2018.06.011

来源: Elsevier

【 摘 要 】

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.

【 授权许可】

Free