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Variational Online Learning of Neural Dynamics.

Yuan Zhao1,2,3, Il Memming Park1,2,3

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|November 6, 2020
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We developed a new online learning framework for analyzing neural activity during complex behaviors. This method efficiently models neural dynamics and latent states, enabling real-time applications in neuroscience and brain-computer interfaces.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Machine Learning

Background:

  • Advanced neural recording technologies enable studying complex behaviors.
  • Non-linear state space models offer interpretable frameworks for neural data analysis.
  • Learning latent neural states and dynamics is challenging due to unknown system parameters.

Purpose of the Study:

  • To develop a flexible online learning framework for latent non-linear state dynamics.
  • To jointly optimize dynamical system, observation, and recognition models.
  • To enable real-time analysis of neural population activity.

Main Methods:

  • Stochastic gradient variational Bayes approach.
  • Joint optimization of non-linear dynamical system and observation models.
  • Flexible framework incorporating non-trivial observation noise distributions.

Main Results:

  • Constant time and space complexity, suitable for real-time applications.
  • Efficiently learns latent neural states and underlying dynamics.
  • Potential for automated analysis and experimental design.

Conclusions:

  • The developed framework offers a powerful tool for real-time neural data analysis.
  • It facilitates insights into neural dynamics, computation, and brain-computer interface development.
  • Enables testable tracking and modification of behavior through adaptive stimuli.