# Can Spiking Neural Networks play pinball? A neuromorphic motion detector for target tracking

arXiv: https://arxiv.org/abs/2609.24403 · HTML: https://mlsanity.com/paper/2609.24403/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 80 references that are in the map.
- First posted on arXiv 21 Sep 2026.

## Builds on (most cited first, 20 of 20)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Real-world Anomaly Detection in Surveillance Videos](https://mlsanity.com/paper/1801.04264/) (2018)
- [DSEC: A Stereo Event Camera Dataset for Driving Scenarios](https://mlsanity.com/paper/2103.06011/) (2021)
- [The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception](https://mlsanity.com/paper/1801.10202/) (2018)
- [EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras](https://mlsanity.com/paper/1802.06898/) (2018)
- [Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Cars](https://mlsanity.com/paper/1804.01310/) (2018)
- [A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)](https://mlsanity.com/paper/1708.04198/) (2017)
- [The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticity](https://mlsanity.com/paper/2201.11063/) (2022)
- [Event-Based Angular Velocity Regression with Spiking Networks](https://mlsanity.com/paper/2003.02790/) (2020)
- [SpikeMS: Deep Spiking Neural Network for Motion Segmentation](https://mlsanity.com/paper/2105.06562/) (2021)
- [EV-Catcher: High-Speed Object Catching Using Low-latency Event-based Neural Networks](https://mlsanity.com/paper/2304.07200/) (2023)
- [Tracking Small and Fast Moving Objects: A Benchmark](https://mlsanity.com/paper/2209.04284/) (2022)
- [From Tiny Machine Learning to Tiny Deep Learning: A Survey](https://mlsanity.com/paper/2506.18927/) (2025)
- [PUCK: Parallel Surface and Convolution-kernel Tracking for Event-Based Cameras](https://mlsanity.com/paper/2205.07657/) (2022)
- [Detection of Fast-Moving Objects with Neuromorphic Hardware](https://mlsanity.com/paper/2403.10677/) (2024)
- [Deep Event-based Object Detection in Autonomous Driving: A Survey](https://mlsanity.com/paper/2405.03995/) (2024)
- [ATIS + SpiNNaker: a Fully Event-based Visual Tracking Demonstration](https://mlsanity.com/paper/1912.01320/) (2019)
- [TDE-3: An improved prior for optical flow computation in spiking neural networks](https://mlsanity.com/paper/2402.11662/) (2024)
- [Neuromorphic dreaming: A pathway to efficient learning in artificial agents](https://mlsanity.com/paper/2405.15616/) (2024)
- [Training slow silicon neurons to control extremely fast robots with spiking reinforcement learning](https://mlsanity.com/paper/2601.21548/) (2026)

## Abstract (arXiv)

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.