Exploiting correlations across trials and behavioral sessions to improve neural decoding.

Traditional neural decoders model the relationship between neural activity and behavior within individual trials of a single experimental session, neglecting correlations across trials and sessions. However, animals exhibit similar neural activities when performing the same behavioral task, and their behaviors are influenced by past experiences from previous trials. To exploit these informative correlations in large datasets, we introduce two complementary models: a multi-session reduced-rank regression model that shares similar behaviorally-relevant statistical structure in neural activity across sessions to improve decoding, and a multi-session state-space model that shares similar behavioral statistical structure across trials and sessions. Applied across 433 sessions spanning 270 brain regions in the International Brain Laboratory public mouse Neuropixels dataset, our decoders demonstrate improved decoding accuracy for four distinct behaviors compared to traditional approaches. These results generalize across additional datasets, species, and behavioral tasks. Unlike existing deep learning approaches, our models are interpretable and efficient, uncovering low-dimensional representations that predict animal decisions, quantifying single-neuron contributions to decoding behaviors, and identifying different activation timescales of neural activity across the brain. Code: https://github.com/yzhang511/neural_decoding.