Combined statistical-biophysical modeling links ion channel genes to physiology of cortical neuron types.

Neural cell types have classically been characterized by their anatomy and electrophysiology. More recently, single-cell transcriptomics has enabled an increasingly fine genetically defined taxonomy of cortical cell types, but the link between the gene expression of individual cell types and their physiological and anatomical properties remains poorly understood. Here, we develop a hybrid modeling approach to bridge this gap.

[EyeMatics-Multicenter data evaluation of real-world data with interoperable medical informatics].

The evaluation of real-world data (RWD) enables insights to be gained from a wide range of patient data collected in routine clinical practice. In addition, multicenter analyses represent a broad and representative patient population and have the potential to capture the actual treatment situation. As a basis for this, the definition of datasets and an infrastructure for data exchange is necessary.

GABAergic amacrine cells balance biased chromatic information in the mouse retina.

The retina extracts chromatic information present in an animal's environment. How this information is processed in the retina is not well understood. In the mouse, chromatic information is not collected equally throughout the retina.

A chromatic feature detector in the retina signals visual context changes.

The retina transforms patterns of light into visual feature representations supporting behaviour. These representations are distributed across various types of retinal ganglion cells (RGCs), whose spatial and temporal tuning properties have been studied extensively in many model organisms, including the mouse. However, it has been difficult to link the potentially nonlinear retinal transformations of natural visual inputs to specific ethological purposes.