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

• URL: http://arxiv.org/abs/2608.07754v1
• Date: Fri, 07 Aug 2026 20:37:38 GMT
• Title: Phase State Space Models: Parallel, Surrogate-Free Training of Spiking Networks
• Abstract summary: We adapt state-space models (SSMs) to provide a novel interpretation of resonate-and-fire (R&F) neural networks.<n>We demonstrate an implementation of this approach which integrates an STFT, recurrent memory, and attentional features within a single spike-compatible network.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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