Topic: data augmentation

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

Deep reinforcement learning (RL) has successfully trained machines to excel in video games, board games, and robotics. It has been argued that together, deep RL and robotics can be used to study biological learning by virtue of being embodied intelligence in the real world (Tan et al., 2021). In fact, RL itself was originally formulated to model animal behavior (Sutton & Barto, 1998). Here we go one step further and directly interface deep RL agents with a living neural network: that of the nematode C. elegans. We present a hybrid deep RL - C. elegans closed-loop computational system where