Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects

2025-02-13

AI Summary

Existing neuromorphic benchmarks inadequately evaluate the temporal processing capabilities of spiking neural networks (SNNs), particularly across diverse time scales. Method: We introduce the first SNN-specific benchmark suite explicitly designed for multi-timescale dynamic modeling, integrating event-driven spiking dynamics, multi-scale temporal task design, and hybrid training strategies—including surrogate gradient backpropagation and ANN-to-SNN conversion—within a unified cross-model evaluation framework. Contribution/Results: Our benchmark systematically exposes a significant performance gap for SNNs on long-range dependency tasks; empirically validates performance gains from novel spiking neurons and architectures on short- to medium-term sequence tasks; and provides concrete empirical evidence to guide SNN architecture design and learning paradigm evolution. Results demonstrate that state-of-the-art SNNs remain substantially outperformed by advanced non-spiking models in long-range temporal modeling.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Deep Neural Architectures and Foundation Models
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Temporal processing is fundamental for both biological and artificial intelligence systems, as it enables the comprehension of dynamic environments and facilitates timely responses. Spiking Neural Networks (SNNs) excel in handling such data with high efficiency, owing to their rich neuronal dynamics and sparse activity patterns. Given the recent surge in the development of SNNs, there is an urgent need for a comprehensive evaluation of their temporal processing capabilities. In this paper, we first conduct an in-depth assessment of commonly used neuromorphic benchmarks, revealing critical limitations in their ability to evaluate the temporal processing capabilities of SNNs. To bridge this gap, we further introduce a benchmark suite consisting of three temporal processing tasks characterized by rich temporal dynamics across multiple timescales. Utilizing this benchmark suite, we perform a thorough evaluation of recently introduced SNN approaches to elucidate the current status of SNNs in temporal processing. Our findings indicate significant advancements in recently developed spiking neuron models and neural architectures regarding their temporal processing capabilities, while also highlighting a performance gap in handling long-range dependencies when compared to state-of-the-art non-spiking models. Finally, we discuss the key challenges and outline potential avenues for future research.

Problem

• Evaluate SNNs' temporal processing capabilities
• Introduce new temporal processing benchmarks
• Address SNNs' long-range dependency limitations

Innovation

• Introduces SNN benchmark suite
• Evaluates temporal processing capabilities
• Highlights long-range dependency challenges