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Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • System Identification

Background:

  • Spiking neural networks are crucial for understanding brain computation.
  • Accurate inference of network parameters and dynamics is challenging due to complexity and partial observations.

Purpose of the Study:

  • To benchmark inference algorithms for spiking neural network models.
  • To develop methods for predicting system dynamics under perturbations using deep learning.

Main Methods:

  • Inference on parameters and dynamics of a mean-field approximation of spiking neurons.
  • Benchmarking optimization and Bayesian estimation algorithms against deep neural density estimators.
  • Employing deep neural Ordinary Differential Equations (ODEs) on spiking neurons.

Main Results:

  • Deep neural density estimators outperformed other algorithms for parameter inference.
  • Time-delay embedding improved uncertainty and parameter correlation issues with neural density estimators.
  • Deep neural ODEs successfully predicted system dynamics and vector fields from microscopic states.

Conclusions:

  • Deep neural networks offer powerful tools for predicting brain dynamics and responses to interventions.
  • The developed methods advance system identification in complex neural systems.
  • Further research can refine these techniques for broader applications in neuroscience.