Behavioral Classification of Sequential Neural Activity Using Time Varying Recurrent Neural Networks.

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

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC12398402 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature