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Multiple Timescale Online Learning Rules for Information Maximization with Energetic Constraints.

Peng Yi1, ShiNung Ching2

  • 1Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, 63130, U.S.A. yipeng@wustl.edu.

Neural Computation
|March 19, 2019
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Summary
This summary is machine-generated.

This study introduces an online neural coding framework that maximizes information transfer from stimuli to neural activity, overcoming biological limitations. It achieves efficient learning with energy constraints using variational bounds and auxiliary variables.

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Area of Science:

  • Computational Neuroscience
  • Information Theory
  • Machine Learning

Background:

  • Understanding neural representations of stimuli is crucial for neural coding.
  • The infomax paradigm suggests neural learning maximizes mutual information between inputs and activities.
  • Existing optimization methods often lack biological plausibility due to batch processing and global awareness.

Purpose of the Study:

  • To develop an online information-based neural coding framework.
  • To address biological incompatibilities in current neural coding optimization methods.
  • To investigate energy-efficient neural coding dynamics.

Main Methods:

  • Utilized variational bounds as a surrogate objective function for online learning.
  • Developed learning dynamics for linear-continuous and discrete spiking neural encoding models.
  • Employed pairwise feedback mechanisms to approximate information quantities.
  • Introduced auxiliary variables evolving on slower timescales for energy efficiency.

Main Results:

  • Demonstrated online information-based neural coding is achievable.
  • Showcased the biological compatibility of the proposed framework.
  • Achieved saddle-point optimization within neural dynamics under energy constraints.
  • Generated neural codes with improved information and energy efficiency.

Conclusions:

  • The proposed framework offers a biologically plausible approach to information-based neural coding.
  • Variational bounds enable online learning policies for neural networks.
  • Auxiliary variables facilitate energy-efficient neural coding dynamics.