Across-animal odor decoding by probabilistic manifold alignment

Pedro Herrero-Vidal, Dmitry Rinberg, Cristina Savin

Keywords: neuroscience, probabilistic models, latent dynamical systems, decoding, neural alignment, across-animal neural analysis

Abstract: Identifying the common structure of neural dynamics across subjects is key for extracting unifying principles of brain computation and for many brain machine interface applications. Here, we propose a novel probabilistic approach for aligning stimulus-evoked responses from multiple animals in a common low dimensional manifold and use hierarchical inference to identify which stimulus drives neural activity in any given trial. Our probabilistic decoder is robust to a range of features of the neural responses and significantly outperforms existing neural alignment procedures. When applied to recordings from the mouse olfactory bulb, our approach reveals low-dimensional population dynamics that are odor specific and have consistent structure across animals. Thus, our decoder can be used for increasing the robustness and scalability of neural-based chemical detection.

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TL;DR: A novel probabilistic model can align neural responses and efficiently decode odors across animals.

Code: https://github.com/pedroherrerovidal/amLDS