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Summary
This summary is machine-generated.

We introduce the Stiefel Manifold Dynamical System (SMDS) to model neural dynamics and capture representational drift. SMDS accurately models brain activity changes over time, outperforming traditional linear dynamical systems.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Dynamical Systems

Background:

  • Understanding neural dynamics is key to brain function.
  • Linear Dynamical Systems (LDS) model neural data but struggle with representational drift.
  • Representational drift involves gradual changes in neural representations over time.

Purpose of the Study:

  • Introduce the Stiefel Manifold Dynamical System (SMDS) to model neural dynamics.
  • Account for representational drift in neural activity across trials.
  • Improve accuracy in capturing non-stationary neural dynamics.

Main Methods:

  • Developed SMDS with orthonormal emission matrices evolving on the Stiefel manifold.
  • Constrained emission matrices to capture smooth changes in neural representations.
  • Applied SMDS to simulated and real neural recording datasets.

Main Results:

  • SMDS outperforms LDS in log-likelihood across datasets.
  • SMDS requires fewer latent dimensions than LDS for comparable activity capture.
  • SMDS quantifies representational drift, revealing gradual changes over minutes.

Conclusions:

  • SMDS offers a robust framework for analyzing neural dynamics and representational drift.
  • The model captures non-stationarity more effectively than traditional LDS.
  • SMDS identifies varying drift rates, with slower drift in significant neural dimensions.