Abstract
Shifts in data distribution across time can strongly affect early classification of time-series data. When decoding behavior from neural activity, early detection of behavior may help in devising corrective neural stimulation before the onset of behavior. Recurrent neural networks are common models for sequence data. However, standard recurrent neural networks are not able to handle data with temporal distributional shifts to guarantee robust classification across time. To enable the network to utilize all temporal features of the neural input data, and to enhance the memory of recurrent neural networks, this paper proposes a novel approach: recurrent neural networks with time-varying weights, here termed Time-varying recurrent neural networks. These models are able to not only predict the class of the time-sequence correctly, but also lead to accurate classification earlier in the sequence than standard recurrent neural networks, while also stabilizing gradient dynamics. This paper focuses on early sequential classification of spatially distributed neural activity across time using Time-varying recurrent neural networks applied to a variety of neural data from mice and humans, as subjects perform motor tasks. Time-varying recurrent neural networks detect self-initiated lever-pull behavior up to 6 seconds before behavior onset-3 seconds earlier than standard recurrent neural networks. Finally, this paper explored the contribution of different brain regions on behavior classification using SHapley Additive exPlanation value, and found that the somatosensory and premotor regions play a large role in behavioral classification.