Topic: closed-loop feedback

According to a classical control-theoretic perspective on self-movement, our brains pilot our bodies to achieve goals, sometimes using open-loop predictive models and sometimes with closed-loop feedback. From this perspective, synchronization of self-motion with a moving target is a simple control problem, where error between body and target is minimized by some combination of responsive and predictive control. However, self-movement can also result from reconciliation of multisensory prediction errors. When subjected to a visual/proprioceptive conflict, our bodies respond spontaneously and un

Brain-computer interfaces (BCIs) are a powerful tool in both clinical applications and basic science research. Traditionally, BCIs rely on electrical signals collected by electrode arrays, which usually suffer from instability due to electrode movements and cell loss [1]. Recent approaches to address this issue continuously update BCI decoders [2]. By reliably identifying the same cells over days, using $Ca^{+2}$ imaging as BCI input may circumvent these limitations, and enable simultaneous recording from large neuronal populations over extended periods. Nonetheless, $Ca^{+2}$ imaging data typ