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Multimodal subspace identification for modeling discrete-continuous spiking and field potential population activity.

Parima Ahmadipour1, Omid G Sani1, Bijan Pesaran2

  • 1Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America.

Journal of Neural Engineering
|November 28, 2023
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Summary

A new multiscale subspace identification (multiscale SID) algorithm efficiently models multimodal neural data, improving brain-machine interfaces (BMIs) and neuroscience research by enabling faster, more accurate analysis of spike-field activity.

Keywords:
Poisson and Gaussian datadynamical systemslocal field potentialsmultimodal dataspiking activityunsupervised learning

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Area of Science:

  • Computational Neuroscience
  • Machine Learning
  • Systems Neuroscience

Background:

  • Learning dynamical latent state models from multimodal neural data (spiking and field potentials) is crucial for understanding brain dynamics and decoding behavior.
  • Efficient unsupervised learning methods are needed for real-time applications like brain-machine interfaces (BMIs), but multimodal spike-field data present challenges due to heterogeneous distributions and timescales.
  • Existing methods struggle with computational efficiency for complex multimodal neural data.

Purpose of the Study:

  • To develop a computationally efficient unsupervised learning method for modeling and dimensionality reduction of multimodal spike-field data.
  • To enable accurate real-time learning for applications such as brain-machine interfaces.
  • To improve the understanding of collective low-dimensional dynamics in neural activity.

Main Methods:

  • Developed a multiscale subspace identification (multiscale SID) algorithm for computationally efficient learning.
  • Derived a new analytical subspace identification (SID) method for combined Poisson and Gaussian observations (spike-field activity).
  • Introduced a constrained optimization approach for learning valid noise statistics, crucial for multimodal inference.

Main Results:

  • Multiscale SID accurately learned dynamical models and extracted low-dimensional dynamics from multimodal spike-field signals.
  • The method effectively fused multimodal information, outperforming single-modality approaches in identifying dynamical modes and predicting behavior.
  • Multiscale SID demonstrated significantly lower training times compared to existing expectation-maximization methods, with comparable or superior accuracy.

Conclusions:

  • Multiscale SID is an accurate and efficient method for modeling multimodal neural data.
  • The algorithm is particularly beneficial for applications requiring efficient learning, such as online adaptive BMIs and reducing offline analysis time.
  • This method advances the ability to track non-stationary neural dynamics and analyze complex brain activity.