Kang, I., Zhang, Q., Yu, S.X. et al. Coordinate-based neural representations for computational adaptive optics in widefield microscopy. Nat Mach Intell 6, 714–725 (2024). https://doi.org/10.1038/s42256-024-00853-3 This is a public repository for CoCoA, a self-supervised computational adaptive optics method for widefield microscopy. CoCoA, which stands for Coordinate-based neural representations for Computational Adaptive optics, is designed to jointly estimate wavefront aberration and structures based on widefield fluorescence microscopy. The paper describing CoCoA can be found here, published in Nature Machine Intelligence. Widefield microscopy is widely used for non-invasive imaging of biological structures at subcellular resolution. When applied to complex specimen, its image quality is degraded by sample-induced optical aberration. Adaptive optics can correct wavefront distortion and restore diffraction-limited resolution but require wavefront sensing and corrective devices, increasing system complexity and cost. Here we describe a self-supervised machine learning algorithm, CoCoA, that performs joint wavefront estimation and three-dimensional structural information extraction from a single-input three-dimensional image stack without the need for external training datasets. We implemented CoCoA for widefield imaging of mouse brain tissues and validated its performance with direct-wavefront-sensing-based adaptive optics. Importantly, we systematically explored and quantitatively characterized the limiting factors of CoCoA’s performance. Using CoCoA, we demonstrated in vivo widefield mouse brain imaging using machine learning-based adaptive optics. Incorporating coordinate-based neural representations and a forward physics model, the self-supervised scheme of CoCoA should be applicable to microscopy modalities in general. The code demonstrates the aberration estimation from the provided the aberrated 3D image stack. It then evaluates the estimation accuracy by comparing it with the ground truth. The associated datasets can be found in the '/source/beads/' folder, which contains three distinct sets: Reference image stack; Image stack with external aberration on top of the reference; Ground truth, which represents externally provided aberration data.