Across-animal odor decoding by probabilistic manifold alignmentDownload PDF

May 21, 2021 (edited Oct 25, 2021)NeurIPS 2021 SpotlightReaders: Everyone
  • Keywords: neuroscience, probabilistic models, latent dynamical systems, decoding, neural alignment, across-animal neural analysis
  • TL;DR: A novel probabilistic model can align neural responses and efficiently decode odors across animals.
  • 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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  • Code: https://github.com/pedroherrerovidal/amLDS
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