Learning divisive normalization in primary visual cortex.
Learning divisive normalization in primary visual cortex.
复制标题
初级视觉皮层分裂归一化学习。
DOI：
10.1371/journal.pcbi.1009028
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发表时间：
2021-06
影响因子：
4.3
通讯作者：
Ecker AS
中科院分区：
文献类型：
--
作者：
Burg MF;Cadena SA;Denfield GH;Walker EY;Tolias AS;Bethge M;Ecker AS
分裂归一化（DN）是大脑中一个重要的计算构建块，已被提出作为一个规范的皮层操作。大量的实验研究已经证实了它的重要性，捕捉非线性神经响应特性简单，人工刺激，和计算研究表明，DN也是一个重要的组成部分，处理自然刺激。然而，我们缺乏DN的定量模型，这些模型直接通过测量大脑中的尖峰反应来获得信息，并且适用于任意刺激。在这里，我们提出了一个DN模型，适用于任意输入图像。我们测试其预测猕猴初级视皮层（V1）神经元对自然图像的反应的能力，重点是经典感受野内的非线性响应特性。我们的模型由一层亚单元，其次是学习方向特定的DN。它优于线性-非线性和基于小波的特征表示，并朝着最先进的卷积神经网络（CNN）模型的性能迈出了重要的一步。与深度CNN不同，我们的紧凑DN模型提供了对归一化性质的直接解释。通过检查我们模型的学习归一化池，我们深入了解了一个长期存在的问题，即DN的调谐特性更新了当前的教科书描述：我们发现，在感受野内，定向特征优先被具有相似方向的特征归一化，而不是像目前假设的那样非特异性。分裂归一化（DN）是贯穿大脑中的感觉处理的计算构建块。我们目前缺乏对这种正常化机制在处理自然图像等复杂刺激时所起作用的理解。在这里，我们使用现代机器学习方法来构建一个通用的DN模型，该模型直接由来自初级视觉皮层（V1）的数据提供信息。与高预测性的深度学习模型相反，我们基于DN的模型的参数提供了对归一化性质的直接解释。在感受野内，我们发现对特定方向反应强烈的神经元优先被其他对类似方向高度活跃的神经元归一化，而不是像教科书模型目前假设的那样被所有神经元归一化。
Divisive normalization (DN) is a prominent computational building block in the brain that has been proposed as a canonical cortical operation. Numerous experimental studies have verified its importance for capturing nonlinear neural response properties to simple, artificial stimuli, and computational studies suggest that DN is also an important component for processing natural stimuli. However, we lack quantitative models of DN that are directly informed by measurements of spiking responses in the brain and applicable to arbitrary stimuli. Here, we propose a DN model that is applicable to arbitrary input images. We test its ability to predict how neurons in macaque primary visual cortex (V1) respond to natural images, with a focus on nonlinear response properties within the classical receptive field. Our model consists of one layer of subunits followed by learned orientation-specific DN. It outperforms linear-nonlinear and wavelet-based feature representations and makes a significant step towards the performance of state-of-the-art convolutional neural network (CNN) models. Unlike deep CNNs, our compact DN model offers a direct interpretation of the nature of normalization. By inspecting the learned normalization pool of our model, we gained insights into a long-standing question about the tuning properties of DN that update the current textbook description: we found that within the receptive field oriented features were normalized preferentially by features with similar orientation rather than non-specifically as currently assumed. Divisive normalization (DN) is a computational building block throughout sensory processing in the brain. We currently lack an understanding of what role this normalization mechanism plays when processing complex stimuli like natural images. Here, we use modern machine learning methods to build a general DN model that is directly informed by data from primary visual cortex (V1). Contrary to high-predictive deep learning models, our DN-based model’s parameters offer a straightforward interpretation of the nature of normalization. Within the receptive field, we found that neurons responding strongly to a specific orientation are preferentially normalized by other neurons that are highly active for similar orientations, rather than being normalized by all neurons as it is currently assumed by textbook models.
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