# Effective AER Object Classification Using Segmented Probability-Maximization Learning in Spiking Neural Networks

arXiv: https://arxiv.org/abs/2002.06199 · HTML: https://mlsanity.com/paper/2002.06199/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2020.

## Built on by (most cited first, 16 of 16)

- [SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence](https://mlsanity.com/paper/2310.16620/) (2023)
- [Neuromorphic Data Augmentation for Training Spiking Neural Networks](https://mlsanity.com/paper/2203.06145/) (2022)
- [Graph-based Asynchronous Event Processing for Rapid Object Recognition](https://mlsanity.com/paper/2308.14419/) (2023)
- [SpikePoint: An Efficient Point-based Spiking Neural Network for Event Cameras Action Recognition](https://mlsanity.com/paper/2310.07189/) (2023)
- [Exploiting Noise as a Resource for Computation and Learning in Spiking Neural Networks](https://mlsanity.com/paper/2305.16044/) (2023)
- [Neuromorphic Drone Detection: an Event-RGB Multimodal Approach](https://mlsanity.com/paper/2409.16099/) (2024)
- [Inherent Redundancy in Spiking Neural Networks](https://mlsanity.com/paper/2308.08227/) (2023)
- [Increasing Liquid State Machine Performance with Edge-of-Chaos Dynamics Organized by Astrocyte-modulated Plasticity](https://mlsanity.com/paper/2111.01760/) (2021)
- [Temporal-Guided Spiking Neural Networks for Event-Based Human Action Recognition](https://mlsanity.com/paper/2503.17132/) (2025)
- [A Synapse-Threshold Synergistic Learning Approach for Spiking Neural Networks](https://mlsanity.com/paper/2206.06129/) (2022)
- [Event Camera Meets Mobile Embodied Perception: Abstraction, Algorithm, Acceleration, Application](https://mlsanity.com/paper/2503.22943/) (2025)
- [TinyLight: Adaptive Traffic Signal Control on Devices with Extremely Limited Resources](https://mlsanity.com/paper/2205.00427/) (2022)
- [Apprenticeship-Inspired Elegance: Synergistic Knowledge Distillation Empowers Spiking Neural Networks for Efficient Single-Eye Emotion Recognition](https://mlsanity.com/paper/2407.09521/) (2024)
- [Membrane Potential Distribution Adjustment and Parametric Surrogate Gradient in Spiking Neural Networks](https://mlsanity.com/paper/2304.13289/) (2023)
- [EventF2S: Asynchronous and Sparse Spiking AER Framework using Neuromorphic-Friendly Algorithm](https://mlsanity.com/paper/2402.10078/) (2024)
- [AW-GATCN: Adaptive Weighted Graph Attention Convolutional Network for Event Camera Data Joint Denoising and Object Recognition](https://mlsanity.com/paper/2505.11232/) (2025)

## Abstract (arXiv)

Address event representation (AER) cameras have recently attracted more attention due to the advantages of high temporal resolution and low power consumption, compared with traditional frame-based cameras. Since AER cameras record the visual input as asynchronous discrete events, they are inherently suitable to coordinate with the spiking neural network (SNN), which is biologically plausible and energy-efficient on neuromorphic hardware. However, using SNN to perform the AER object classification is still challenging, due to the lack of effective learning algorithms for this new representation. To tackle this issue, we propose an AER object classification model using a novel segmented probability-maximization (SPA) learning algorithm. Technically, 1) the SPA learning algorithm iteratively maximizes the probability of the classes that samples belong to, in order to improve the reliability of neuron responses and effectiveness of learning; 2) a peak detection (PD) mechanism is introduced in SPA to locate informative time points segment by segment, based on which information within the whole event stream can be fully utilized by the learning. Extensive experimental results show that, compared to state-of-the-art methods, not only our model is more effective, but also it requires less information to reach a certain level of accuracy.