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Related Experiment Video

Updated: Feb 18, 2026

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Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network.

Aditya Gilra1,2, Wulfram Gerstner1,2

  • 1Brain-Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.

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|November 28, 2017
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Summary

This study introduces FOLLOW, a novel learning rule enabling spiking neural networks to predict complex body dynamics. This brain-inspired method ensures stable, online learning for motor control applications.

Keywords:
feedbacklearningmotor controlneurosciencenoneplasticityrecurrent neural networksstability

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Area of Science:

  • Computational Neuroscience
  • Machine Learning
  • Robotics

Background:

  • The brain must predict body dynamics for motor control.
  • Learning these dynamics with spiking neural networks using local, stable rules remains a challenge.

Purpose of the Study:

  • To present a supervised learning scheme for spiking neural networks to learn non-linear body dynamics.
  • To introduce a novel, local, online, and stable learning rule called FOLLOW (Feedback-based Online Local Learning Of Weights).

Main Methods:

  • Utilized a supervised learning scheme for feedforward and recurrent connections in heterogeneous spiking neurons.
  • Employed a feedback error signal through fixed random connections with negative gain.
  • Applied Lyapunov stability analysis to demonstrate learning stability.

Main Results:

  • Demonstrated the ability of the FOLLOW rule to learn linear, non-linear, and chaotic dynamics.
  • Successfully modeled the dynamics of a two-link arm.
  • Proved uniform stability and asymptotic error convergence using the Lyapunov method.

Conclusions:

  • The FOLLOW learning rule provides a viable mechanism for spiking neural networks to learn complex body dynamics.
  • This approach offers a stable and efficient method for real-time motor control prediction.
  • The findings contribute to understanding brain-inspired computation for robotics and artificial intelligence.