In modern neuroscience, observing the dynamics of large populations of neurons is a critical step of understanding how networks of neurons process information. Light-field microscopy (LFM) has emerged as a type of scanless, high-speed, three-dimensional (3D) imaging tool, particularly attractive for this purpose. Imaging neuronal activity using LFM calls for the development of novel computational approaches that fully exploit domain knowledge embedded in physics and optics models, as well as enabling high interpretability and transparency. To this end, we propose a model-based explainable deep learning approach for LFM. Different from purely data-driven methods, the proposed approach integrates wave-optics theory, sparse representation and non-linear optimization with the artificial neural network. In particular, the architecture of the proposed neural network is designed following precise signal and optimization models. Moreover, the network’s parameters are learned from a training dataset using a novel training strategy that integrates layer-wise training with tailored knowledge distillation. Such design allows the network to take advantage of domain knowledge and learned new features. It combines the benefit of both model-based and learning-based methods, thereby contributing to superior interpretability, transparency and performance. By evaluating on both structural and functional LFM data obtained from scattering mammalian brain tissues, we demonstrate the capabilities of the proposed approach to achieve fast, robust 3D localization of neuron sources and accurate neural activity identification.

中文翻译：

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用于光场显微镜成像的基于模型的可解释深度学习

在现代神经科学中，观察大量神经元的动态是理解神经元网络如何处理信息的关键一步。光场显微镜 (LFM) 已成为一种无扫描、高速、三维 (3D) 成像工具，对此用途特别有吸引力。使用 LFM 对神经元活动进行成像需要开发新颖的计算方法，充分利用物理和光学模型中嵌入的领域知识，并实现高可解释性和透明度。为此，我们提出了一种基于模型的可解释的 LFM 深度学习方法。与纯粹的数据驱动方法不同，该方法将波动光学理论、稀疏表示和非线性优化与人工神经网络相结合。特别是，所提出的神经网络的架构是根据精确的信号和优化模型设计的。此外，网络的参数是使用一种新颖的训练策略从训练数据集中学习的，该策略将分层训练与定制的知识蒸馏相结合。这种设计允许网络利用领域知识并学习新功能。它结合了基于模型和基于学习的方法的优点，从而有助于实现卓越的可解释性、透明度和性能。通过评估从分散的哺乳动物脑组织中获得的结构和功能 LFM 数据，我们证明了所提出的方法能够实现快速、稳健的神经元源 3D 定位和准确的神经活动识别。