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arXiv · 2608.07754

Phase State Space Models: Parallel, Surrogate-Free Training of Spiking Networks

Wilkie Olin-Ammentorp
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Abstract

State-space models (SSMs) provide a powerful theoretical framework to enable parallel training of recurrent networks. We expand on previous work adapting SSMs to spiking models to provide a novel interpretation of resonate-and-fire (R\&F) neural networks which is compatible both with real and spiking inputs, parallel and recurrent execution, has clear connections to hyperdimensional (HD) computing, and maintains biologically-realistic features. We demonstrate an implementation of this approach which integrates an STFT, recurrent memory, and attentional features within a single spike-compatible network.

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Wilkie Olin-Ammentorp. 2026-08-07. Phase State Space Models: Parallel, Surrogate-Free Training of Spiking Networks. https://doi.org/10.1145/3822454.3822484

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