Spiking Neural Network Systems

Recurrent versus parallelizable spiking neural networks: A comparative study

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Abstract

Spiking neural networks (SNNs) have emerged as a biologically plausible computational paradigm with strong links to real-world brain dynamics. Recently, interest has grown in parallelizable State Space Model (SSM)–inspired architectures, which offer improved scalability compared to recurrent networks. While effective at scale, these models represent a step away from biological realism. In particular, the impact of removing recurrent connections and membrane nonlinearities on the temporal processing capabilities of SNNs remains largely unexplored. In this work, we investigate the impact of these changes to the network dynamics on the temporal processing capabilities of SNNs with a focus on recurrent connectivity. To this end, a suite of sequential tasks was used to systematically compare parallelizable SSM-style networks with recurrent SNNs. The results demonstrate that while parallelizable models perform well on tasks with simple or weak temporal dependencies, they struggle to maintain persistent internal state when complex, state-dependent computation is required. In contrast, recurrent architectures exhibit superior memory retention and robustness under these conditions. These findings suggest fundamental limitations of parallelizable SSM-style approaches for sequence tasks that rely on long-term internal memory, highlighting the continued relevance of recurrence in spiking neural computation as suggested by biology.

How to Cite:

Mayr, A., Hitzginger, S. & Legenstein, R., (2026) “Recurrent versus parallelizable spiking neural networks: A comparative study”, Proceedings of the Austrian Symposium on AI, Robotics, and Vision 3(1), 245-252.