Meta-Learning for Decoding Neural Activity Data With Noisy Labels

Abstract

In neural decoding, a behavioral variable is often generated by manual annotation and the annotated labels could contain extensive label noise, leading to poor model generalizability. Tackling the label noise problem in neural decoding can improve model generalizability and robustness. We use a deep neural network based sample reweighting method to tackle this problem. The proposed method reweights training samples by using a small and clean validation dataset to guide learning. We evaluated the sample reweighting method on simulated neural activity data and calcium imaging data of anterior lateral motor cortex. For the simulated data, the proposed method can accurately predict the behavioral variable even in the scenario that 36 percent of samples in the training dataset are mislabeled. For the anterior lateral motor cortex study, the proposed method can predict trial types with F1 score of around 0.85 even 48 percent of training samples are mislabeled.