Capturing cross-session neural population variability through self-supervised identification of consistent neuron ensembles

Justin Jude, Matthew G. Perich, Lee E. Miller, Matthias H. Hennig

Research output: Contribution to journalConference articlepeer-review

Abstract

Decoding stimuli or behaviour from recorded neural activity is a common approach to interrogate brain function in research, and an essential part of brain-computer and brain-machine interfaces. Reliable decoding even from small neural populations is possible because high dimensional neural population activity typically occupies low dimensional manifolds that are discoverable with suitable latent variable models. Over time however, drifts in activity of individual neurons and instabilities in neural recording devices can be substantial, making stable decoding over days and weeks impractical. While this drift cannot be predicted on an individual neuron level, population level variations over consecutive recording sessions such as differing sets of neurons and varying permutations of consistent neurons in recorded data may be learnable when the underlying manifold is stable over time. Classification of consistent versus unfamiliar neurons across sessions and accounting for deviations in the order of consistent recording neurons across sessions of recordings may then maintain decoding performance and uncover a task-related neural manifold. Here we show that self-supervised training of a deep neural network can be used to compensate for this inter-session variability. As a result, a sequential autoencoding model can maintain state-of-the-art behaviour decoding performance for completely unseen recording sessions several days into the future. Our approach only requires a single recording session for training the model, and is a step towards reliable, recalibration-free brain computer interfaces.

Original languageEnglish (US)
Pages (from-to)234-257
Number of pages24
JournalProceedings of Machine Learning Research
Volume197
StatePublished - 2023
Event1st Annual NeurIPS Workshop on Symmetry and Geometry in Neural Representations, NeurReps 2022 - New Orleans, United States
Duration: Dec 3 2022 → …

Keywords

  • Electrophysiology
  • Manifold learning
  • Neural decoding
  • Neural population activity
  • Neuroscience
  • Self-supervised learning
  • Sequential autoencoders

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability

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