We introduce LightFlow, an open-source software package for simulating light wave propagation through custom optical components and systems. Built upon TensorFlow and Keras, it benefits from GPU acceleration and offers a user-friendly and modular architecture. Optical components are represented as layers, simplifying the design and modification of simulation models. Our approach also streamlines the addition of new custom components. LightFlow’s automatic gradient calculation is valuable for computational imaging applications involving optimization algorithms and inverse problems. With its intuitive interface, tested building blocks, and expandable design, LightFlow is well-suited for education and research, from undergraduate to advanced graduate levels. The GPU-accelerated processing enables efficient, real-time visualization of optical simulations, making LightFlow valuable across a broad range of user expertise and applications.
Genetically encoded calcium indicators and optogenetics have revolutionized neuroscience by enabling the detection and modulation of neural activity with single-cell precision using light. To fully leverage the immense potential of these techniques, advanced optical instruments that can place a light on custom ensembles of neurons with a high level of spatial and temporal precision are required. Modern light sculpting techniques that have the capacity to shape a beam of light are preferred because they can precisely target multiple neurons simultaneously and modulate the activity of large ensembles of individual neurons at rates that match natural neuronal dynamics. The most versatile approach, computer-generated holography (CGH), relies on a computer-controlled light modulator placed in the path of a coherent laser beam to synthesize custom three-dimensional (3D) illumination patterns and illuminate neural ensembles on demand. Here, we review recent progress in the development and implementation of fast and spatiotemporally precise CGH techniques that sculpt light in 3D to optically interrogate neural circuit functions.
Gradient descent is an efficient algorithm to optimize differentiable functions with continuous variables, yet it is not suitable for computer generated holography (CGH) with binary light modulators. To address this, we replaced binary pixel values with continuous variables that are binarized with a thresholding operation, and we introduced gradients of the sigmoid function as surrogate gradients to ensure the differentiability of the binarization step. We implemented this method both to directly optimize binary holograms, and to train deep learning-based CGH models. Simulations and experimental results show that our method achieves greater speed, and higher accuracy and contrast than existing algorithms.
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