Topic: BrainGate2

Advancements in machine learning and artificial intelligence have given rise to promising neural network-based models of the complex nonlinear relationships between movement intention and neural activity. In particular, recurrent neural networks (RNNs) are one commonly used algorithm for brain-computer interface (BCI) control, due to their ability to learn temporal dependencies within the training data. However, most BCI training data contains highly stereotyped temporal and behavioral structure to which RNNs can overfit, causing poor performance in online settings when behavior and temporal c

Dynamical systems theory provides a promising framework for understanding neural activity. LFADS approximates neural recordings using a recurrent neural network (RNN) generator, but analyzing its dynamics has been challenging. We propose a simple modification to LFADS, adding a per-trial inferred bias, that dramatically improves interpretability by replacing complex multi-stable dynamics with simpler contextual dynamics. Both models were applied to neural activity recorded from macaque motor cortex during reaching movements. While the kinematic decoding was similar, they implemented contrasti