Topic: Allen Mouse Brain Connectivity Atlas

Learning in biological systems takes place in contexts and with dynamics not often accounted for by simple models. I will describe the learning dynamics of two model systems that incorporate either architectural or dynamic constraints from biological observations. In the first case, inspired by the observed mesoscopic structure of the mouse brain as revealed by the Allen Mouse Brain Connectivity Atlas, as well as multiple examples of parallel pathways in mammalian brains, I present a mathematical analysis of learning dynamics in networks that have parallel computational pathways driven by the

Understanding how tasks are processed across the entire brain is a central yet complex question in neuroscience. Recently, the release of brainwide electrophysiological recordings in a standardized behavior task offers an unprecedented opportunity to approach this question. In this paper, we characterize the signal activity of all the cortical regions systematically, and correlate the resulting functional properties with their corresponding anatomical positions. Taking advantage of the proposed brainwide reduced-rank regression encoding model that is unified and well-performing, we measured tw