Optogenetic approaches to study the mammalian brain.

Optogenetics has revolutionized neurobiological research by allowing to disentangle intricate neuronal circuits at a spatio-temporal precision unmatched by other techniques. Here, we review current advances of optogenetic applications in mammals, especially focusing on freely moving animals. State-of-the-art strategies allow the targeted expression of opsins in neuronal subpopulations, defined either by genetic cell type or neuronal projection pattern. Optogenetic manipulations of these subpopulations become particularly powerful when combined with behavioral paradigms and neurophysiological readout techniques. Thereby, specific roles can be assigned to identified cells. All-optical approaches with the opportunity to write complex three dimensional patterns into neuronal networks have recently emerged. While clinical implications of the new tool set seem tempting, we emphasize here the role of optogenetics for basic research.

Author Correction: U-Net: deep learning for cell counting, detection, and morphometry.

In the version of this paper originally published, one of the affiliations for Dominic Mai was incorrect: "Center for Biological Systems Analysis (ZBSA), Albert-Ludwigs-University, Freiburg, Germany" should have been "Life Imaging Center, Center for Biological Systems Analysis, Albert-Ludwigs-University, Freiburg, Germany." This change required some renumbering of subsequent author affiliations. These corrections have been made in the PDF and HTML versions of the article, as well as in any cover sheets for associated Supplementary Information.

U-Net: deep learning for cell counting, detection, and morphometry.

U-Net is a generic deep-learning solution for frequently occurring quantification tasks such as cell detection and shape measurements in biomedical image data. We present an ImageJ plugin that enables non-machine-learning experts to analyze their data with U-Net on either a local computer or a remote server/cloud service. The plugin comes with pretrained models for single-cell segmentation and allows for U-Net to be adapted to new tasks on the basis of a few annotated samples.