Visual Place Recognition Using Rate-Encoded Spiking Neural Networks with Discrete STDP Learning
Spiking Neural Networks (SNNs) trained through unsupervised Spike-Timing-Dependent Plasticity (STDP) have been explored as solutions to visual loop closure problems, driven by the prospect of efficient on-device inference on neuromorphic devices. State-of-the-art STDP-based models deliver high classification accuracy but fail to reach the high Recall at 100% Precision (R@100P) needed for reliable autonomous navigation. We present a discrete, tensor-native implementation of the STDP-based SNN-VPR pipeline using PyTorch with snnTorch and evaluate it on a 100-place Nordland dataset using 15 independently-trained networks. The contribution of three decisions in the implementation is investiga...
| Authors | Altzi Tsanko, Oikonomou Katerina Maria, Antonios Gasteratos |
|---|---|
| Primary category | cs.NE |
| Published | 15 July 2026 |
| Last updated at source | 15 July 2026 |
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