ReviewSensors (Basel, Switzerland)2025
Spiking Neural Networks in Imaging: A Review and Case Study.
Review in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- SAD-SNN: Spatial-Activation Distillation for High-Performance Spiking Neural Networks.Sensors (Basel, Switzerland) · 2026Article
- Weaving Intelligence: Thermally Drawn Multimaterial Fibers Toward AI-Enabled Smart Textiles.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- SE-SNN: Squeeze-and-Excitation-Enhanced Spiking Neural Networks with Learnable Neuron Dynamics for Event-Based Vision.Biomimetics (Basel, Switzerland) · 2026Article
- Spiking Neural Networks with Continual Learning for Steering Angle Regression: A Sustainable AI Perspective.Sensors (Basel, Switzerland) · 2026Article
- Characterization of a Spiking Convolutional Processor for FPGA.Sensors (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
This review examines the state of spiking neural networks (SNNs) for imaging, combining a structured literature survey, a comparative meta-analysis of reported datasets, training strategies, hardware platforms, and applications and a case study on LMU-based depth estimation in direct Time-of-Flight (dToF) imaging. While SNNs demonstrate promise for energy-efficient, event-driven computation, current progress is constrained by reliance on small or custom datasets, ANN-SNN conversion inefficiencies, simulation-based hardware evaluation, and a narrow focus on classification tasks. The analysis highlights scaling trade-offs between accuracy and efficiency, persistent latency bottlenecks, and limited sensor-hardware integration. These findings were synthesised into key challenges and future directions, emphasising benchmarks, hardware-aware training, ecosystem development, and broader application domains.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.