ePoster · Computational Neuroscience
Identifying cortical learning algorithms using Brain-Machine Interfaces
Sofia Pereira da Silva, Denis Alevi, Friedrich Schuessler, Henning Sprekeler · Bernstein Conference 2024
By causally mapping neural activity to behavior [1], Brain-Computer Interfaces (BCI) offer a means to study the dynamics of sensorimotor learning. Here, we combine computational modeling and data analysis to study the neural learning algorithm [2] monkeys use to adapt to a changed output mapping in a center-out reaching task. We exploit that the mapping from neural space (ca. 100 dimensions) to the 2D cursor position is a credit assignment problem [3] that is underconstrained, because changes along a large number of output-null dimensions do not influence the behavioral output. We hypothesized