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Related Experiment Video

Updated: Jul 12, 2026

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Spiking neurons as predictive controllers of linear systems.

Paolo Agliati1, André Urbano2, Pablo Lanillos2

  • 1Department of Machine Learning and Neural Computing, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.

Plos Computational Biology
|July 9, 2026
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Summary

This study introduces a novel, scalable method for spiking neural networks (SNNs) to control systems using sparse neural activity. It enables SNNs to act as efficient, biologically-inspired controllers without continuous energy input.

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Area of Science:

  • Computational Neuroscience
  • Control Theory
  • Neuromorphic Engineering

Background:

  • Neurons communicate using electrical spikes, traditionally difficult to use directly for precise control.
  • Current spiking neural networks (SNNs) often approximate analog control, requiring continuous energy.
  • Existing methods lack scalability and analytical tractability for complex control tasks.

Purpose of the Study:

  • To develop a scalable, biologically-plausible control method for SNNs using sparse spiking activity.
  • To circumvent the limitations of rate-based representations in SNN control.
  • To provide a closed-form mathematical derivation for SNN control principles.

Main Methods:

  • Defined a spiking rule inspired by control theory and neuroscience, where spikes are emitted only when advancing a dynamical system towards a target.
  • Derived SNN connectivity based on this spiking principle.
  • Applied the method to control linear systems, physically constrained systems, and high-dimensional tasks.

Main Results:

  • Demonstrated successful control of linear systems using the derived SNN connectivity.
  • Showcased predictive control exploiting passive system dynamics for constrained systems.
  • Validated scalability to high-dimensional systems and bio-inspired motor control tasks.

Conclusions:

  • The proposed method offers a scalable and efficient approach to spiking control, overcoming limitations of traditional methods.
  • This work provides insights into biological neural control mechanisms and advances neuromorphic hardware design.
  • Maintained mathematical tractability throughout the derivation of network connectivity, dynamics, and control objectives.