Enhancing spiking neural networks with evidential deep learning for object classification on event based dataset

Tools

Kumar, Prabhat and Subudhi, Badri Narayan and Di Caterina, Gaetano and Jakhetiya, Vinit and Veerakumar, T; (2025) Enhancing spiking neural networks with evidential deep learning for object classification on event based dataset. In: ICVGIP '25. Association for Computing Machinery (ACM), IND. (In Press)

[thumbnail of Kumar-etal-ICVGIP-2025-Enhancing-spiking-neural-networks-with-evidential-deep-learning] Text. Filename: Kumar-etal-ICVGIP-2025-Enhancing-spiking-neural-networks-with-evidential-deep-learning.pdf
Accepted Author Manuscript
Restricted to Repository staff only until 1 January 2099.
Download (3MB) | Request a copy

Abstract

Spiking Neural Networks (SNNs) represent a promising approach for energy-efficient, event-driven computation inspired by biological neural processes. However, their practical deployment is often hindered by three major challenges: limited availability of event-based datasets, poor interpretability of model decisions, and lack of reliable mechanisms for estimating prediction uncertainty. These limitations restrict the usability of SNNs in real-world, safety critical applications such as robotics and edge-AI. To address these issues, this work presents a novel framework that integrates two complementary techniques: event-based data augmentation (EDA) for enhancing data diversity in neuromorphic datasets and evidential deep learning (EDL) for modeling uncertainty using a Dirichlet distribution over class evidence. We utilize a VGG-11-derived architecture, adapted into a spiking format using leaky integrate-and-fire (LIF) neurons. This architecture is trained end-to-end using surrogate gradient techniques across fixed timesteps, with temporal normalization employed to stabilize spike-based learning. An EDL module is appended to the output layer to provide calibrated confidence estimates alongside class predictions, this module is optimized using a combination of cross-entropy and KL-divergence-based evidential loss functions. We have evaluated the performance of our proposed system on two benchmark neuromorphic datasets, namely CIFAR10 DVS and N-Caltech101. Experimental results show that our proposed framework achieves classification accuracies of 76.57% and 77.20% on CIFAR10-DVS and N-Caltech101 datasets, respectively. Furthermore, we compare our technique with five state-of-the-art techniques on the aforementioned datasets, respectively, confirming the superiority of the proposed framework.

ORCID iDs

Kumar, Prabhat, Subudhi, Badri Narayan, Di Caterina, Gaetano ORCID logoORCID: https://orcid.org/0000-0002-7256-0897, Jakhetiya, Vinit and Veerakumar, T;
CORE (COnnecting REpositories)