mlsanityData to 7 Oct 2026

Hyperdimensional Decoding of Spiking Neural Networks

arXiv 2511.08558 ↗First posted 11 Nov 2025cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

This work presents a novel spiking neural network (SNN) decoding method, combining SNNs with Hyperdimensional computing (HDC). The goal is to create a decoding method with high accuracy, high noise robustness, low latency and low energy usage. Compared to analogous architectures decoded with existing approaches, the presented SNN-HDC model attains generally better classification accuracy, lower classification latency and lower estimated energy consumption on multiple test cases from literature. The SNN-HDC achieved estimated energy consumption reductions ranging from 1.24x to 3.67x on the DvsGesture dataset and from 1.38x to 2.27x on the SL-Animals-DVS dataset. The presented decoding method can also efficiently identify unknown classes it has not been trained on. In the DvsGesture dataset the SNN-HDC model can identify 100% of samples from an unseen/untrained class. Given the numerous benefits shown and discussed in this paper, this decoding method represents a very compelling alternative to both rate and latency decoding.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 52 references that are in the map. First posted on arXiv 11 Nov 2025.

Builds on

18 of 52 references.

References · 18

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2015Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccadescited by 308
  3. 2018Deep Learning in Spiking Neural Networkscited by 258
  4. 2018SLAYER: Spike Layer Error Reassignment in Timecited by 257
  5. 2021Training Spiking Neural Networks Using Lessons From Deep Learningcited by 251
  6. 2021A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part I: Models and Data Transformationscited by 98
  7. 2019Graph-Based Object Classification for Neuromorphic Vision Sensingcited by 85
  8. 2017A Survey of Neuromorphic Computing and Neural Networks in Hardwarecited by 82
  9. 2022Event Transformer. A sparse-aware solution for efficient event data processingcited by 48
  10. 2023SEENN: Towards Temporal Spiking Early-Exit Neural Networkscited by 39
  11. 2023Sneaky Spikes: Uncovering Stealthy Backdoor Attacks in Spiking Neural Networks with Neuromorphic Datacited by 18
  12. 2023Low-power event-based face detection with asynchronous neuromorphic hardwarecited by 11
6 more
  1. 2022A Synapse-Threshold Synergistic Learning Approach for Spiking Neural Networkscited by 9
  2. 2023Optimising Event-Driven Spiking Neural Network with Regularisation and Cutoffcited by 8
  3. 2023S-TLLR: STDP-inspired Temporal Local Learning Rule for Spiking Neural Networkscited by 7
  4. 2024EventRPG: Event Data Augmentation with Relevance Propagation Guidancecited by 7
  5. 2024SQUAT: Stateful Quantization-Aware Training in Recurrent Spiking Neural Networkscited by 2
  6. 2023An Inherent Trade-Off in Noisy Neural Communication with Rank-Order Codingcited by 1

Built on by

Not cited yet.