# ANTShapes Benchmarking Datasets for Event-Based Neuromorphic Object Classification

arXiv: https://arxiv.org/abs/2608.27150 · HTML: https://mlsanity.com/paper/2608.27150/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 31 references that are in the map.
- First posted on arXiv 27 Aug 2026.

## Builds on (most cited first, 14 of 14)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks](https://mlsanity.com/paper/2007.05785/) (2020)
- [Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades](https://mlsanity.com/paper/1507.07629/) (2015)
- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [Memory and information processing in neuromorphic systems](https://mlsanity.com/paper/1506.03264/) (2015)
- [Surrogate gradients for analog neuromorphic computing](https://mlsanity.com/paper/2006.07239/) (2020)
- [Event-Driven Visual-Tactile Sensing and Learning for Robots](https://mlsanity.com/paper/2009.07083/) (2020)
- [UCF-Crime-DVS: A Novel Event-Based Dataset for Video Anomaly Detection with Spiking Neural Networks](https://mlsanity.com/paper/2503.12905/) (2025)
- [Is Neuromorphic MNIST neuromorphic? Analyzing the discriminative power of neuromorphic datasets in the time domain](https://mlsanity.com/paper/1807.01013/) (2018)
- [LaneSNNs: Spiking Neural Networks for Lane Detection on the Loihi Neuromorphic Processor](https://mlsanity.com/paper/2208.02253/) (2022)
- [Accelerated Analog Neuromorphic Computing](https://mlsanity.com/paper/2003.11996/) (2020)
- [Spiking-DD: Neuromorphic Event Camera based Driver Distraction Detection with Spiking Neural Network](https://mlsanity.com/paper/2407.20633/) (2024)
- [Brain-inspired AI for Edge Intelligence: a systematic review](https://mlsanity.com/paper/2603.26722/) (2026)

## Abstract (arXiv)

Object classification in event-based computer vision is a task that is attracting considerable research attention. Event-based object classification is a fundamental task in the fields of security and applied computer vision, which typically use synchronous frame-based cameras and computing pipelines for operation. This approach has several practical flaws. The size, weight and power consumption of the device could prohibit deployment at the extreme edge or in covert sensing environments. Besides this, there are security concerns inherent in cloud-based or other off-device computation approaches due to the requirement of sending and receiving potentially sensitive data. Furthermore, this transmission of data introduces latency and requires consistent connectivity to the cloud infrastructure to function. The use of Spiking Neural Networks (SNNs) hosted on neuromorphic devices attempts to solve several issues present in this conventional approach. Research into event-based object classification methods are hindered by the lack of high-quality vision datasets to use. To this end, the ANTShapes simulation tool has been previously proposed to create and label event-based vision datasets. In this paper, four novel datasets of varying difficulties are created using the tool and are benchmarked against existing spiking datasets commonly used for event-based vision research (N-MNIST, CIFAR10-DVS, DVSGesture and POKER-DVS). Classification is performed using a convolutional SNN. This work simultaneously provides four datasets with rich details for future experiments to use and validates the output of the ANTShapes dataset simulation tool as being suitable for its purpose.