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Spiking Neural Networks in Imaging: A Review and Case Study.

Michael Voudaskas1,2, Jack Iain MacLean1, Neale A W Dutton2

  • 1Institute for Integrated Micro and Nano Systems, The University of Edinburgh, Edinburgh EH9 3BF, UK.

Sensors (Basel, Switzerland)
|November 13, 2025
PubMed
Summary
This summary is machine-generated.

Spiking neural networks (SNNs) show potential for energy-efficient imaging but face challenges with datasets, training, and hardware integration. Future work needs benchmarks and hardware-aware training for broader applications.

Keywords:
Legendre memory unitimagingneural networkneuromorphic engineeringreviewspiking neural networktime of flight

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Neuroscience

Background:

  • Spiking neural networks (SNNs) offer energy-efficient, event-driven computation.
  • SNNs are being explored for various imaging applications.

Purpose of the Study:

  • To review the current state of SNNs in imaging.
  • To identify key challenges and future directions for SNNs in imaging.

Main Methods:

  • Structured literature survey.
  • Comparative meta-analysis of datasets, training strategies, hardware, and applications.
  • Case study on LMU-based depth estimation in direct Time-of-Flight (dToF) imaging.

Main Results:

  • SNN progress is limited by small datasets, inefficient ANN-SNN conversion, and simulation-based evaluations.
  • Accuracy-efficiency trade-offs, latency bottlenecks, and sensor-hardware integration are key issues.
  • Current applications are narrowly focused on classification tasks.

Conclusions:

  • Developing standardized benchmarks is crucial.
  • Hardware-aware training methods are needed for improved performance.
  • Expanding application domains and fostering ecosystem development are essential for SNNs in imaging.