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A spiking neural network for active efficient coding.

Thomas Barbier1, Céline Teulière1, Jochen Triesch2

  • 1SIGMA Clermont, Centre National de la Recherche Scientifique, Institut Pascal, Université Clermont Auvergne, Clermont-Ferrand, France.

Frontiers in Robotics and AI
|January 30, 2025
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Summary
This summary is machine-generated.

This study introduces the first Active Efficient Coding (AEC) system using Spiking Neural Networks (SNNs) and event-based cameras for efficient visual processing and action control.

Keywords:
active efficient codingevent-based camerasreinforcement learningspiking neural networkunsupervised learning

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Area of Science:

  • Computational Neuroscience
  • Robotics
  • Artificial Intelligence

Background:

  • Biological vision systems learn to encode visual input and control eye movements simultaneously.
  • The Active Efficient Coding (AEC) framework models this joint learning using traditional cameras.
  • Event-based cameras offer advantages over frame-based systems due to their retina-inspired design.

Purpose of the Study:

  • To propose and implement the first Active Efficient Coding (AEC) system using Spiking Neural Networks (SNNs) driven by event-based camera input.
  • To demonstrate efficient visual representation learning and motor command generation within a fully spiking neural network framework.
  • To explore the capabilities of this novel system in tasks requiring dynamic visual interaction.

Main Methods:

  • Developed a two-layer Spiking Neural Network (SNN) for efficient encoding of event-based camera data.
  • Integrated this SNN with a spiking reinforcement learner to generate motor commands.
  • Designed an intrinsic reward signal computed from the SNN's activity levels to guide learning.
  • Evaluated the system on visual tracking and orientation stabilization tasks.

Main Results:

  • Successfully implemented a fully Spiking Neural Network (SNN) based Active Efficient Coding (AEC) system.
  • Demonstrated the system's ability to perform visual tracking of translating targets.
  • Showcased the system's capability in stabilizing the orientation of rotating targets.
  • Achieved joint learning of visual representations and motor control within the SNN framework.

Conclusions:

  • This work presents the first fully spiking Active Efficient Coding (AEC) model.
  • The proposed system effectively utilizes event-based cameras and Spiking Neural Networks (SNNs) for autonomous visual learning and control.
  • This approach holds promise for developing more efficient and biologically plausible artificial vision systems.