发表机构
Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRIStAL; Czech Technical University in Prague（里尔大学，法国国家科学研究中心，中央里尔，UMR 9189 CRIStAL; 捷克理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出全脉冲神经形态感知-行动流水线，用于闭环弹球游戏，实现56.1%命中率、21.7毫秒反应和148微瓦功耗，并通过可调策略参数复现人类游戏风格。
AI 中文摘要
生物视觉系统通过处理稀疏、异步的脉冲信号实现连续、低延迟的运动感知，从而在严格的能量约束下实现实时跟踪。受哺乳动物视网膜启发的事件相机，仅以异步事件的形式捕获局部亮度变化，从而复现了这种高效性，为脉冲神经网络（SNNs）提供了并行计算和适应快速变化场景的天然基础。弹球游戏提供了一个可控但动态的测试平台，要求对小型快速移动目标进行精确的运动估计和快速反应。本工作提出了一个全脉冲、实时的感知到行动流水线，用于闭环弹球游戏。动态视觉传感器观察一个小型快速移动的球，SpiNNaker神经形态平台上的一组脉冲时间差编码器网络联合估计其位置、速度和方向。该系统在感受野大小、累积窗口和角度调谐宽度方面进行了表征，以实现实时操作，并在两个物理真实性递增的挡板机制下，与人类玩家进行闭环基准测试。其命中率达到56.1%，几乎是人类平均水平的两倍，反应时间在21.7毫秒内（网络延迟5毫秒），消耗估计148微瓦，使用不到2.5万个神经元，是所基准测试中速度最快、能效最高的事件驱动闭环演示之一。在更真实的挡板动力学下，调整单个可解释的策略参数即可复现从谨慎到激进的人类游戏风格全谱，而无需改变感知流水线。一个物理演示器，在闭环中跟踪真实球并驱动真实挡板，证实了该原理在仿真之外也能运行。其全脉冲、免学习的设计提供了一个紧凑、节能的实时神经形态感知到行动的示例。
英文摘要
Biological visual systems achieve continuous, low-latency motion perception by processing sparse, asynchronous spiking signals, enabling real-time tracking under strict energy constraints. Event-based cameras, inspired by the mammalian retina, replicate this efficiency by capturing only local brightness changes as asynchronous events, offering a natural substrate for spiking neural networks (SNNs) to parallelise computation and adapt to fast-changing scenes. Pinball provides a controlled yet dynamic testbed, requiring precise motion estimation and fast reaction to a small, rapidly moving target. This work presents a fully spiking, real-time perception-to-action pipeline for closed-loop pinball gameplay. A dynamic vision sensor observes a small, fast-moving ball, and a network of spiking Time-Difference Encoders on the SpiNNaker neuromorphic platform jointly estimates its position, speed, and direction. The system is characterised across receptive field size, accumulation window, and angular tuning width for real-time operation, and benchmarked in closed loop against human players across two flipper regimes of increasing physical realism. It achieves a hit rate of 56.1%, nearly double the human average, reacting within 21.7 ms (5 ms network latency) and consuming an estimated 148 μW using fewer than 25k neurons, among the fastest and most energy-efficient event-based closed-loop demonstrators benchmarked. Under more realistic flipper dynamics, tuning a single interpretable policy parameter reproduces the full spectrum of human play styles, from cautious to aggressive, with no change to the perception pipeline. A physical demonstrator, tracking a real ball and actuating real flippers in closed loop, confirms the principle operates beyond simulation. Its fully spiking, learning-free design offers a compact, energy-efficient example of real-time neuromorphic perception-to-action.
