Topic: per-trial bias

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