University Medical Center Göttingen, Department of Ophthalmology, Göttingen, Germany.
Bernstein Center for Computational Neuroscience Göttingen, Göttingen, Germany.
PLoS Comput Biol. 2022 Mar 8;18(3):e1009925. doi: 10.1371/journal.pcbi.1009925. eCollection 2022 Mar.
A central goal in sensory neuroscience is to understand the neuronal signal processing involved in the encoding of natural stimuli. A critical step towards this goal is the development of successful computational encoding models. For ganglion cells in the vertebrate retina, the development of satisfactory models for responses to natural visual scenes is an ongoing challenge. Standard models typically apply linear integration of visual stimuli over space, yet many ganglion cells are known to show nonlinear spatial integration, in particular when stimulated with contrast-reversing gratings. We here study the influence of spatial nonlinearities in the encoding of natural images by ganglion cells, using multielectrode-array recordings from isolated salamander and mouse retinas. We assess how responses to natural images depend on first- and second-order statistics of spatial patterns inside the receptive field. This leads us to a simple extension of current standard ganglion cell models. We show that taking not only the weighted average of light intensity inside the receptive field into account but also its variance over space can partly account for nonlinear integration and substantially improve response predictions of responses to novel images. For salamander ganglion cells, we find that response predictions for cell classes with large receptive fields profit most from including spatial contrast information. Finally, we demonstrate how this model framework can be used to assess the spatial scale of nonlinear integration. Our results underscore that nonlinear spatial stimulus integration translates to stimulation with natural images. Furthermore, the introduced model framework provides a simple, yet powerful extension of standard models and may serve as a benchmark for the development of more detailed models of the nonlinear structure of receptive fields.
感觉神经科学的一个核心目标是理解参与自然刺激编码的神经元信号处理。实现这一目标的关键步骤是开发成功的计算编码模型。对于脊椎动物视网膜中的神经节细胞，开发用于自然视觉场景响应的令人满意的模型仍然是一个持续的挑战。标准模型通常应用视觉刺激在空间上的线性积分，但许多神经节细胞表现出非线性的空间积分，特别是当用对比度反转光栅刺激时。在这里，我们使用来自分离的蝾螈和老鼠视网膜的多电极阵列记录来研究自然图像编码中空间非线性的影响。我们评估了自然图像的响应如何依赖于感受野内空间模式的一阶和二阶统计。这导致了对当前标准神经节细胞模型的简单扩展。我们表明，不仅考虑感受野内光强度的加权平均值，而且还考虑其空间方差，可以部分解释非线性积分，并大大改善对新图像的响应预测。对于蝾螈神经节细胞，我们发现对于具有大感受野的细胞类别的响应预测，从包括空间对比度信息中受益最多。最后，我们展示了如何使用这个模型框架来评估非线性积分的空间尺度。我们的结果强调了非线性空间刺激整合转化为对自然图像的刺激。此外，引入的模型框架为标准模型的简单而强大的扩展提供了一种方法，并可能作为开发感受野非线性结构更详细模型的基准。
