In this project we address the fundamental question of how information processing takes place in the mammalian brain. The brain of mammals such as mice, rats, non-human primates and humans consists of a myriad of brain cells, which are interconnected to form a gigantic complex excitable network. To a large part signal flow through this distributed network is based on the electrical excitability of the neurons. Despite a wealth of previous neuroscientific studies, both on the anatomy and the function of neurons in various brain regions, we still poorly understand the principles of how signal flow is routed through neuronal networks to generate appropriate behavior. Brain dynamics on the 'mesoscopic' scale, the intermediate level where brain regions communicate via axonal pathways, has remained a particular blind spot of research as it has been difficult to access under relvenat conditions when the brain is performeing a particular task. Understanding how brains efficiently solve computational tasks is important and relevant for the society because (1) improved knowledge about the healthy brain's functioning will also open new opportunities to understand brain disorders and brain diseases, and (2) insights into the operating principles of the brain could inspire new types of information processing devices. The overall objective of the project is to tackle the mesoscopic level of brain dynamics both experimentally and theoretically, adopting a fresh perspective centered on neuronal pathway dynamics. Experimentally, we will utilize and further advance state-of-the-art genetic and optical techniques to create a toolbox for measuring and manipulating signal flow in pathway networks across a broad range of temporal scales. In particular, we will improve fiber-optic based methods for probing the activity of either individual or multiple neuronal pathways with high specificity. Using these tools we will set out to reveal mesoscopic brain dynamics across relevant cortical and subcortical regions in awake, behaving mice. Specifically, we will investigate sensorimotor learning for a reward-based texture discrimination task and rapid sensorimotor control during skilled locomotion. Moreover, by combining fiber-optic methods with two-photon microscopy and fMRI, respectively, we will start linking the meso-level to the micro- and macro-levels. Throughout the project, experiments will be complemented by computational approaches to analyse data, model pathway dynamics, and conceptualize a formal theory of mesoscopic dynamics. This project may transform the field by bridging the hierarchical brain levels and opening significant new avenues to assess physiological as well as pathological signal flow in the brain.
