Uncovering the effect of different brain regions on behavioral classification using recurrent neural networks

As our ability to record neural activity from a larger number of brain areas increases, we need to develop tools to understand how this activity is related to ongoing behavior. Recurrent neural networks (RNNs) have been shown to perform successful classification for sequence data. However, they are black box models: once trained, it is difficult to uncover the mechanisms that they are using to classify. In this study, we analyze the effect of RNNs on classifying behavior using a simulated dataset and a widefield neural activity dataset as mice perform a self-initiated behavior. We show that RNNs are comparable to, or outperform, traditional classification methods such as Support Vector Machine (SVM), and can also lead to accurate prediction of behavior. Using dimensionality reduction, we visualize the activity of the RNNs to better understand the classification mechanisms of the RNNs. Finally, we are able to accurately pinpoint the effect of different regions on behavioral classification. This study highlights the utility and interpretability of RNNs while classifying behavior using neural activity from different regions.