Model Constrained by Visual Hierarchy Improves Prediction of Neural Responses to Natural Scenes.
Model Constrained by Visual Hierarchy Improves Prediction of Neural Responses to Natural Scenes.
复制标题
DOI：
10.1371/journal.pcbi.1004927
复制
发表时间：
2016-06
影响因子：
4.3
通讯作者：
Mrsic-Flogel TD
中科院分区：
文献类型：
--
作者：
Antolík J;Hofer SB;Bednar JA;Mrsic-Flogel TD
准确估计神经元感受野对于理解早期视觉系统的感觉处理至关重要。然而，感受野的完整表征仍然不完整，特别是在自然视觉刺激和完整的皮质神经元群体中。虽然之前的工作已经整合了早期视觉系统的已知结构特性，例如横向连接或施加简单细胞样的感受野结构，但没有研究利用附近的 V1 神经元共享来自丘脑和其他上游皮层神经元的共同前馈输入这一事实。我们引入了一种新方法，使用结合了前馈视觉层次知识的基于模型的分析，同时估计 V1 神经元群体的感受野。我们假设 V1 神经元群体共享丘脑输入的公共池，并由两层简单和复杂的 V1 神经元组成。当适合小鼠层 2/3 V1 神经元的局部记录时，我们的模型可以准确描述它们对自然图像的反应，并比当前最先进的方法显着提高预测能力。我们表明，具有局部不同感受野的大量局部 V1 神经元群体的反应可以用数量惊人的有限丘脑输入来描述，这与最近的实验结果一致。我们的结构模型不仅提供了 V1 神经元的改进功能表征，而且还提供了研究视觉皮层区域连接性和功能之间关系的框架。感觉神经科学的一个关键目标是了解感觉刺激与其在感觉神经元网络中引起的活动模式之间的关系。过去已经提出了许多模型；然而，这些模型在很大程度上忽略了实验研究中揭示的初级视觉皮层的已知结构，从而限制了它们准确描述神经对感觉刺激的反应的能力。在这里，我们提出了一个初级视觉皮层模型，该模型考虑了已知的视觉皮层结构，特别是视觉皮层局部区域内仅共享有限数量的具有刻板感受野的丘脑输入这一事实，以及从具有线性感受野的神经元（简单细胞）到具有非线性感受野的神经元（复杂细胞）的层次进展。我们表明，当适合小鼠 V1 神经元局部群体响应自然图像刺激的双光子钙记录时，所提出的模型优于最先进的感受野估计方法。该模型演示了如何从有限数量 (< 20) 的丘脑输入构建局部神经元群体中的不同感受野集。
Accurate estimation of neuronal receptive fields is essential for understanding sensory processing in the early visual system. Yet a full characterization of receptive fields is still incomplete, especially with regard to natural visual stimuli and in complete populations of cortical neurons. While previous work has incorporated known structural properties of the early visual system, such as lateral connectivity, or imposing simple-cell-like receptive field structure, no study has exploited the fact that nearby V1 neurons share common feed-forward input from thalamus and other upstream cortical neurons. We introduce a new method for estimating receptive fields simultaneously for a population of V1 neurons, using a model-based analysis incorporating knowledge of the feed-forward visual hierarchy. We assume that a population of V1 neurons shares a common pool of thalamic inputs, and consists of two layers of simple and complex-like V1 neurons. When fit to recordings of a local population of mouse layer 2/3 V1 neurons, our model offers an accurate description of their response to natural images and significant improvement of prediction power over the current state-of-the-art methods. We show that the responses of a large local population of V1 neurons with locally diverse receptive fields can be described with surprisingly limited number of thalamic inputs, consistent with recent experimental findings. Our structural model not only offers an improved functional characterization of V1 neurons, but also provides a framework for studying the relationship between connectivity and function in visual cortical areas. A key goal in sensory neuroscience is to understand the relationship between sensory stimuli and patterns of activity they elicit in networks of sensory neurons. Many models have been proposed in the past; however, these models have largely ignored the known architecture of primary visual cortex revealed in experimental studies, thus limiting their ability to accurately describe neural responses to sensory stimuli. Here we propose a model of primary visual cortex that takes into account the known architecture of visual cortex, specifically the fact that only a limited number of thalamic inputs with stereotypical receptive fields are shared within a local area of visual cortex, and the hierarchical progression from neurons with linear receptive fields (simple cells) to neurons with non-linear receptive fields (complex cells). We show that the proposed model outperforms state-of-the-art methods for receptive field estimation when fitted to two-photon calcium recordings of local populations of mouse V1 neurons responding to natural image stimuli. The model demonstrates how the diverse set of receptive fields in the local population of neurons can be constructed from a limited number (< 20) thalamic inputs.