# Visual Place Recognition Using Rate-Encoded Spiking Neural Networks with Discrete STDP Learning

arXiv: https://arxiv.org/abs/2607.13584 · HTML: https://mlsanity.com/paper/2607.13584/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 25 references that are in the map.
- First posted on arXiv 15 Jul 2026.

## Builds on (most cited first, 15 of 15)

- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition](https://mlsanity.com/paper/2103.01486/) (2021)
- [AnyLoc: Towards Universal Visual Place Recognition](https://mlsanity.com/paper/2308.00688/) (2023)
- [SeqNet: Learning Descriptors for Sequence-based Hierarchical Place Recognition](https://mlsanity.com/paper/2102.11603/) (2021)
- [A Hybrid Compact Neural Architecture for Visual Place Recognition](https://mlsanity.com/paper/1910.06840/) (2019)
- [Spiking Neural Networks for Visual Place Recognition via Weighted Neuronal Assignments](https://mlsanity.com/paper/2109.06452/) (2021)
- [Applications of Spiking Neural Networks in Visual Place Recognition](https://mlsanity.com/paper/2311.13186/) (2023)
- [Ensembles of Compact, Region-specific & Regularized Spiking Neural Networks for Scalable Place Recognition](https://mlsanity.com/paper/2209.08723/) (2022)
- [VPRTempo: A Fast Temporally Encoded Spiking Neural Network for Visual Place Recognition](https://mlsanity.com/paper/2309.10225/) (2023)
- [Place Recognition with Event-based Cameras and a Neural Implementation of SeqSLAM](https://mlsanity.com/paper/1505.04548/) (2015)
- [Event-Driven Neuromorphic Vision Enables Energy-Efficient Visual Place Recognition](https://mlsanity.com/paper/2604.03277/) (2026)
- [Spike-EVPR: Deep Spiking Residual Networks with SNN-Tailored Representations for Event-Based Visual Place Recognition](https://mlsanity.com/paper/2402.10476/) (2024)
- [Spiking Neural Networks: The Future of Brain-Inspired Computing](https://mlsanity.com/paper/2510.27379/) (2025)
- [Spiking Neural Network Feature Discrimination Boosts Modality Fusion](https://mlsanity.com/paper/2502.10423/) (2025)

## Abstract (arXiv)

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 investigated. First, we show how to perform neuron assignment with a closed-form, deterministic tensor pipeline and show that it provides significantly higher R@100P than a standard argmax procedure. However, some of this gain comes from implementation differences compared to prior continuous-time models, which we measure independently. Second, ablation in isolation shows that state reset after each query helps improve R@100P regardless of the way neurons are assigned. Third, velocity-compensated sliding window aggregation over k consecutive frames reaches R@100P = 100.00% at k = 5 for constant-velocity traversal and an additional 0.20 ms latency. Taken together, these findings show the impact of inference stage design decisions in STDP-based SNN-VPR on recall precision, although the separate contribution of each mechanism and implementation differences is only partially disentangled and needs further examination.