Artur Schneider

Abstract: Movement is the primary means of an organism interacting with its environment. To study neural processes underlying movement, neuroscientists often pursue the reductionist approach of reducing the variability of the task to a few controllable factors. Controlled but ethologically artificial paradigms delineate animal behavior into individual variables. Strongly limiting the animal's behavior via head-fixation reduces the number of uncontrolled variables in the design. However, it also limits our ability to understand the naturalistic dynamics of movement. Recent studies indicate that signals related to motion are spread throughout the whole brain, even in head-restrained animals. Spontaneous movements outside the task content influence the ongoing neural activity. These findings highlight the enormous contribution of behavior to neural activity and indicate that our interpretation of ongoing processes might be confounded. Thus, there is a drive in neuroscience to study neural processes in more naturalistic environments and freely moving conditions. Measuring animal behavior in such situations is not straightforward. Furthermore, many scientific tools for electrophysiology and optogenetic modulation were initially developed for acute experiments and need to be adopted for chronic, freely moving use. In this work, we developed multiple complementary tools to help study neural processes in freely moving animals. We designed and characterized different multifunctional techniques for combining electrophysiology and optogenetics. A fluidic probe could deliver viral vectors to the recording site; a multifiber approach enabled ultra-precise 3D interrogation of neural circuits. To measure unconstrained movements, we developed FreiPose, a versatile framework to capture 3D motion during freely moving behavior, and combined the movement tracking of rats with electrophysiology. Using a modeling strategy, we described the ongoing neural activity as a combination of simultaneous multiplexed coding of multiple body postures and paw movement parameters. A virtual head-fixation approach was devised of those models to distinguish paw movements from general movement information. Using encoding models of neural activity, we clamped body and head movements to obtain the impact of the paw movements on neuronal activity. Consequently, a large fraction of neurons in the motor cortex was uncovered to be tuned to paw trajectories. This tuning was previously masked by the influence of the varying body posture information. We conclude that measuring the movements of freely moving animals is an essential step toward understanding the underlying neural dynamics. Adding precise descriptions of ongoing behavior into computational models of neural activity will enable us to describe motifs of neural population activity related to sensorimotor integration and decision-making.