使用具有离散STDP学习的速率编码脉冲神经网络进行视觉场所识别
Visual Place Recognition Using Rate-Encoded Spiking Neural Networks with Discrete STDP Learning
- Department of Production and Management Engineering, Democritus University of Thrace（生产与管理工程系，德摩克利特大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究基于STDP的SNN-VPR视觉场所识别，提出离散张量原生实现，用PyTorch和snnTorch在Nordland数据集评估。通过闭式张量管道神经元分配、查询后状态重置及速度补偿滑动窗口聚合提高召回精度，虽各机制贡献待进一步研究，但已展示推理阶段决策影响。
AI中文摘要：
脉冲神经网络（SNN）通过无监督的脉冲时间依赖可塑性（STDP）进行训练，因其有望在神经形态设备上进行高效的片上推理，已被探索用于解决视觉闭环问题。基于STDP的现有模型具有较高的分类准确率，但无法达到可靠自主导航所需的100%精度下的高召回率（R@100P）。我们使用PyTorch和snnTorch提出了基于STDP的SNN-VPR管道的离散张量原生实现，并在100个地点的Nordland数据集上使用15个独立训练的网络对其进行评估。研究了实现过程中三个决策的贡献。首先，展示如何使用闭式确定性张量管道进行神经元分配，其提供的R@100P显著高于标准的argmax过程。其次，单独的消融实验表明，每次查询后重置状态有助于提高R@100P，与神经元分配方式无关。第三，在恒速遍历中，对k个连续帧进行速度补偿的滑动窗口聚合在k = 5时达到R@100P = 100.00%，延迟增加0.20毫秒。这些发现表明了基于STDP的SNN-VPR推理阶段设计决策对召回精度的影响，尽管每种机制的单独贡献和实现差异仅部分解开，需要进一步研究。
英文摘要：
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.
