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BRAID: Input-driven nonlinear dynamical modeling of neural-behavioral data.

Parsa Vahidi1, Omid G Sani1, Maryam M Shanechi1,2,3

  • 1Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California (USC), Los Angeles, CA.

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PubMed
Summary
This summary is machine-generated.

We developed BRAID, a deep learning framework, to model neural population dynamics by incorporating external inputs. This method accurately captures neural activity and behavior, outperforming existing models.

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Area of Science:

  • Computational Neuroscience
  • Machine Learning
  • Systems Neuroscience

Background:

  • Neural populations exhibit complex dynamics influenced by both intrinsic recurrent structures and external inputs.
  • Traditional models often treat neural populations as autonomous systems, neglecting the significant impact of external inputs on activity and behavior.
  • Understanding the interplay between intrinsic neural dynamics and external influences is crucial for decoding neural computations.

Purpose of the Study:

  • To introduce BRAID, a novel deep learning framework for modeling nonlinear neural dynamics that explicitly incorporates external inputs.
  • To disentangle intrinsic neural population dynamics from the effects of external stimuli.
  • To improve the accuracy and forecasting of neural-behavioral data by accounting for sensory inputs.

Main Methods:

  • Developed BRAID, a deep learning framework utilizing input-driven recurrent neural networks with a forecasting objective.
  • Implemented a multi-stage optimization scheme to prioritize learning behavior-relevant intrinsic dynamics.
  • Validated the framework using nonlinear simulations and real-world motor cortical activity data.

Main Results:

  • BRAID accurately learns intrinsic dynamics shared between neural and behavioral data in simulations.
  • Application to motor cortical activity demonstrates BRAID's superior fit to neural-behavioral data compared to baseline methods.
  • Incorporating sensory stimuli via BRAID significantly improves the forecasting of neural and behavioral outcomes.

Conclusions:

  • BRAID offers a powerful new approach to modeling neural population dynamics by integrating external inputs.
  • The framework effectively separates intrinsic dynamics from input-driven effects, leading to more accurate neural decoding.
  • BRAID advances our ability to understand and predict neural activity and behavior in complex tasks.