Topic: optogenetic manipulation

Quantification of animal behavior is critical to neuroscience, genetics, and ethology research. Despite the recent development of a deep-learning-based pose estimation model, the usage of those systems has been limited due to the various video recording environment and hardware systems. Here we introduce a novel open-field experimental hardware with multi-vision and a 3D real-time behavior analysis system, based on the efficient and rapid object-detection deep learning algorithm, named AVATAR (AI Vision Analysis for Three-dimensional Action in Real-time). Combining this system with an automate

I will argue that the circuit architecture of the early olfactory system provides an adaptive, efficient mechanism for compressing the vast space of odor mixtures into the responses of a small number of sensors. In this view, the olfactory sensory repertoire employs a disordered code to compress a high dimensional olfactory space into a low dimensional receptor response space while preserving distance relations between odors. The resulting representation is dynamically adapted to efficiently encode the changing environment of volatile molecules. I will show that this adaptive combinatorial