A Variational Encoder Framework for Decoding Behavior Choices from Neural Data

In this paper, using an adversarial variational encoder model, we propose a two-step data-driven approach to extract cross-subject feature representations from neural activity in order to decode subjects' behavior choices. First, various characteristics of the recorded behavior are computed and passed as features to a clustering model in order to categorize different behavior choices in each trial and create labels for the data. Then, we utilize a variational encoder to learn the latent space mappings from neural activity. An attached adversary network is used in a discriminative setting to detach the subject's individuality from the representations. Recorded cortical activity from Thy1-GCaMP6s transgenic mice during a motivational licking experiment was used in this study. Experimental results demonstrate the capabilities of the proposed method in extracting discriminative representations from neural data to decode behavior by achieving an average classification accuracy of 88.8% across subjects.