Deep neural networks (DNNs) have attained human-level performance on dozens of challenging tasks via an end-to-end deep learning strategy. Deep learning allows data representations that have multiple levels of abstraction; however, it does not explicitly provide any insights into the internal operations of DNNs. Deep learning's success is appealing to neuroscientists not only as a method for applying DNNs to model biological neural systems but also as a means of adopting concepts and methods from cognitive neuroscience to understand the internal representations of DNNs. Although general deep learning frameworks, such as PyTorch and TensorFlow, could be used to allow such cross-disciplinary investigations, the use of these frameworks typically requires high-level programming expertise and comprehensive mathematical knowledge. A toolbox specifically designed as a mechanism for cognitive neuroscientists to map both DNNs and brains is urgently needed. Here, we present DNNBrain, a Python-based toolbox designed for exploring the internal representations of DNNs as well as brains. Through the integration of DNN software packages and well-established brain imaging tools, DNNBrain provides application programming and command line interfaces for a variety of research scenarios. These include extracting DNN activation, probing and visualizing DNN representations, and mapping DNN representations onto the brain. We expect that our toolbox will accelerate scientific research by both applying DNNs to model biological neural systems and utilizing paradigms of cognitive neuroscience to unveil the black box of DNNs.

中文翻译：

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DNNBrain：用于映射深度神经网络和大脑的统一工具箱


深度神经网络 (DNN) 通过端到端深度学习策略，在数十项具有挑战性的任务上达到了人类水平的性能。深度学习允许具有多个抽象级别的数据表示；然而，它没有明确提供对 DNN 内部操作的任何见解。深度学习的成功吸引了神经科学家，不仅作为一种应用 DNN 来建模生物神经系统的方法，而且还作为采用认知神经科学的概念和方法来理解 DNN 内部表示的一种手段。尽管 PyTorch 和 TensorFlow 等通用深度学习框架可用于进行此类跨学科研究，但这些框架的使用通常需要高级编程专业知识和全面的数学知识。迫切需要一个专门为认知神经科学家设计的工具箱来绘制 DNN 和大脑。在这里，我们展示了 DNNBrain，一个基于 Python 的工具箱，旨在探索 DNN 和大脑的内部表示。通过集成 DNN 软件包和完善的脑成像工具，DNNBrain 为各种研究场景提供应用程序编程和命令行接口。其中包括提取 DNN 激活、探测和可视化 DNN 表示，以及将 DNN 表示映射到大脑。我们期望我们的工具箱能够通过应用 DNN 来模拟生物神经系统并利用认知神经科学范式揭开 DNN 的黑匣子来加速科学研究。