One of the central goals in systems neuroscience is to understand how information is encoded in the brain, and the standard approach is to identify the relation between a stimulus and a neural response. However, the feature of a stimulus is typically defined by the researcher's hypothesis, which may cause biases in the research conclusion. To demonstrate potential biases, we simulate four likely scenarios using deep neural networks trained on the image classification dataset CIFAR-10 and demonstrate the possibility of selecting suboptimal/irrelevant features or overestimating the network feature representation/noise correlation. Additionally, we present studies investigating neural coding principles in biological neural networks to which our points can be applied. This study aims to not only highlight the importance of careful assumptions and interpretations regarding the neural response to stimulus features but also suggest that the comparative study between deep and biological neural networks from the perspective of machine learning can be an effective strategy for understanding the coding principles of the brain.

中文翻译：

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学习生物神经网络编码原理的深度神经网络课程

系统神经科学的中心目标之一是了解信息是如何在大脑中编码的，标准方法是识别刺激和神经反应之间的关系。然而，刺激的特征通常由研究人员的假设定义，这可能会导致研究结论出现偏差。为了证明潜在的偏差，我们使用在图像分类数据集 CIFAR-10 上训练的深度神经网络模拟了四种可能的场景，并展示了选择次优/不相关特征或高估网络特征表示/噪声相关性的可能性。此外，我们提出了研究生物神经网络中的神经编码原理的研究，我们的观点可以应用于这些原理。