How well do deep neural networks trained on object recognition characterize the mouse visual system
How well do deep neural networks trained on object recognition characterize the mouse visual system
复制标题
经过对象识别训练的深度神经网络对小鼠视觉系统的表征效果如何
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Alexander S. Ecker
中科院分区：
文献类型：
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作者：
Santiago A. Cadena;Fabian H Sinz;Taliah Muhammad;E. Froudarakis;Erick Cobos;Edgar Y. Walker;Jacob Reimer;M. Bethge;A. Tolias;Alexander S. Ecker
最近对灵长类动物视觉系统神经反应建模的工作受益于在大规模物体识别上训练的深度神经网络，并发现人工神经网络层与腹侧视觉流沿着的大脑区域之间存在分层对应关系。然而，我们既不知道这种任务优化的网络是否能够实现啮齿动物视觉系统的同样良好的模型，也不知道是否存在类似的分层对应关系。在这里，我们通过提取在ImageNet上训练的卷积神经网络（CNN）的几层特征来解决小鼠视觉系统中的这些问题，以预测四个视觉区域（V1，LM，AL，RL）中数千个神经元对自然图像的反应。我们发现CNN特征优于经典的亚基能量模型，但没有发现证据表明我们通过与CNN层的层次结构相对应来记录区域的顺序。此外，具有随机权重的相同CNN为预测神经响应提供了等效有用的特征空间。我们的研究结果表明，物体识别作为一个高层次的任务，并没有提供更多的歧视性功能来表征鼠标视觉系统比随机网络。与灵长类动物不同，可能需要对行为学相关的视觉引导行为进行训练-超越静态物体识别-以揭示小鼠视觉皮层的功能组织。
Recent work on modeling neural responses in the primate visual system has benefited from deep neural networks trained on large-scale object recognition, and found a hierarchical correspondence between layers of the artificial neural network and brain areas along the ventral visual stream. However, we neither know whether such task-optimized networks enable equally good models of the rodent visual system, nor if a similar hierarchical correspondence exists. Here, we address these questions in the mouse visual system by extracting features at several layers of a convolutional neural network (CNN) trained on ImageNet to predict the responses of thousands of neurons in four visual areas (V1, LM, AL, RL) to natural images. We found that the CNN features outperform classical subunit energy models, but found no evidence for an order of the areas we recorded via a correspondence to the hierarchy of CNN layers. Moreover, the same CNN but with random weights provided an equivalently useful feature space for predicting neural responses. Our results suggest that object recognition as a high-level task does not provide more discriminative features to characterize the mouse visual system than a random network. Unlike in the primate, training on ethologically relevant visually guided behaviors – beyond static object recognition – may be needed to unveil the functional organization of the mouse visual cortex.
DOI：
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发表时间：
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期刊：
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影响因子：
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作者：
通讯作者：
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影响因子：
4.3
作者：
Cadena, Santiago A.;Denfield, George H.;Ecker, Alexander S.
通讯作者：
Ecker, Alexander S.
影响因子：
16.2
作者：
Kell, Alexander J. E.;Yamins, Daniel L. K.;McDermott, Josh H.
通讯作者：
McDermott, Josh H.