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

Yizi Zhang1,2, Hanrui Lyu3, Cole Hurwitz4,2

  • 1Department of Statistics, Columbia University, New York, New York, United States of America.

Biorxiv : the Preprint Server for Biology
|September 24, 2024
PubMed
Summary
This summary is machine-generated.

New neural decoding models leverage cross-trial and cross-session correlations to improve accuracy. These interpretable models enhance understanding of neural activity and decision-making in mice.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Machine Learning

Background:

  • Traditional neural decoders analyze single trials, ignoring valuable cross-trial and cross-session neural activity patterns.
  • Animal behavior is influenced by past experiences, suggesting correlations across trials and sessions hold significant information.

Purpose of the Study:

  • To develop novel, interpretable neural decoding models that exploit correlations across multiple trials and sessions.
  • To improve the accuracy of decoding animal behavior from neural activity using large-scale datasets.

Main Methods:

  • Introduced two complementary multi-session models: a reduced-rank model and a state-space model.
  • Applied models to the International Brain Laboratory mouse Neuropixels dataset (433 sessions, 270 brain regions).
  • Compared performance against traditional decoding approaches.

Main Results:

  • Achieved improved decoding accuracy for four distinct behaviors compared to traditional methods.
  • Models are interpretable and efficient, unlike some deep learning approaches.
  • Uncovered latent behavioral dynamics and quantified single-neuron contributions to decoding.

Conclusions:

  • Multi-session models effectively leverage neural correlations across time and sessions for enhanced decoding.
  • These interpretable models offer insights into neural mechanisms of decision-making and brain-wide neural dynamics.
  • The approach provides a powerful tool for analyzing large-scale neuroscience datasets.