Characterisation of nonlinear receptive fields of visual neurons by convolutional neural network
Characterisation of nonlinear receptive fields of visual neurons by convolutional neural network
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DOI：
10.1038/s41598-019-40535-4
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发表时间：
2019-03
影响因子：
4.6
通讯作者：
Jumpei Ukita;Takashi Yoshida;K. Ohki
中科院分区：
文献类型：
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作者：
Jumpei Ukita;Takashi Yoshida;K. Ohki
全面了解单个神经元的刺激-反应特性对于破解感觉皮层的神经密码是必要的。然而，实现这一目标的障碍是分析神经元反应的非线性的困难。在这里，通过将卷积神经网络（CNN）用于编码视觉皮层神经元的模型，我们开发了一种新的非线性响应表征方法，特别是感受野（RF）的非线性估计，而无需假设非线性的类型。简而言之，在训练CNN预测对自然图像的视觉反应后，我们合成了RF图像，使得图像可以预测性地引起最大反应。我们首先使用具有各种类型非线性的模拟细胞的数据集演示了原理证明。我们可以可视化具有各种类型的非线性的RF，例如平移不变RF或旋转不变RF，这表明该方法可能适用于具有复杂非线性的神经元在更高的视觉区域。接下来，我们将该方法应用于小鼠V1中的神经元数据集。我们可以可视化简单细胞样或复杂细胞样（平移不变）RF，并量化平移不变性的程度。这些结果表明，CNN编码模型是有用的非线性响应分析的视觉神经元和潜在的任何感觉神经元。
A comprehensive understanding of the stimulus-response properties of individual neurons is necessary to crack the neural code of sensory cortices. However, a barrier to achieving this goal is the difficulty of analysing the nonlinearity of neuronal responses. Here, by incorporating convolutional neural network (CNN) for encoding models of neurons in the visual cortex, we developed a new method of nonlinear response characterisation, especially nonlinear estimation of receptive fields (RFs), without assumptions regarding the type of nonlinearity. Briefly, after training CNN to predict the visual responses to natural images, we synthesised the RF image such that the image would predictively evoke a maximum response. We first demonstrated the proof-of-principle using a dataset of simulated cells with various types of nonlinearity. We could visualise RFs with various types of nonlinearity, such as shift-invariant RFs or rotation-invariant RFs, suggesting that the method may be applicable to neurons with complex nonlinearities in higher visual areas. Next, we applied the method to a dataset of neurons in mouse V1. We could visualise simple-cell-like or complex-cell-like (shift-invariant) RFs and quantify the degree of shift-invariance. These results suggest that CNN encoding model is useful in nonlinear response analyses of visual neurons and potentially of any sensory neurons.