Imaging Human Learning thanks to compressive sensing. Yes, devious reader of the blog, I see you nodding on how meta that paper could be framed. It's not a sensor that looks at something in the brain, it's a sensor that decodes a scene using an algorithm that somehow parallels elements of the algorithms being imaged. That's a different way of looking at The Great Convergence. Without further ado:
Compressive light-field microscopy for 3D neural activity recording by Nicolas C. Pégard, Hsiou-Yuan Liu, Nick Antipa, Maximillian Gerlock, Hillel Adesnik, and Laura Waller
Understanding the mechanisms of perception, cognition, and behavior requires instruments that are capable of recording and controlling the electrical activity of many neurons simultaneously and at high speeds. All-optical approaches are particularly promising since they are minimally invasive and potentially scalable to experiments interrogating thousands or millions of neurons. Conventional light-field microscopy provides a single-shot 3D fluorescence capture method with good light efficiency and fast speed, but suffers from low spatial resolution and significant image degradation due to scattering in deep layers of brain tissue. Here, we propose a new compressive light-field microscopy method to address both problems, offering a path toward measurement of individual neuron activity across large volumes of tissue. The technique relies on spatial and temporal sparsity of fluorescence signals, allowing one to identify and localize each neuron in a 3D volume, with scattering and aberration effects naturally included and without ever reconstructing a volume image. Experimental results on live zebrafish track the activity of an estimated 800+ neural structures at 100 Hz sampling rate.
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