BCI learning phenomena can be explained by gradient-based optimization

Brain-computer interface (BCI) experiments have shown that animals are able to adapt their recorded neural activity in order to receive reward. Recent studies have highlighted two phenomena. First, the speed at which a BCI task can be learned is dependent on how closely the required neural activity aligns with pre-existing activity patterns: learning "out-of-manifold" tasks is slower than "in-manifold" tasks. Second, learning happens by "re-association": the overall distribution of neural activity patterns does not change significantly during task learning. These phenomena have been presented as distinctive aspects of BCI learning. Here we show, using simulations and theoretical analysis, that both phenomena result from the simple assumption that behaviour and representations are improved via gradient-based algorithms. We invoke Occams Razor to suggest that this straightforward explanation should be preferred when accounting for these experimental observations.