Beyond Low-Pass Dynamics: Frequency-Selective Spiking Reservoirs with Resonant Neurons

Authors: Aurora Micheli , Claudio Gallicchio , Nergis Tomen

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Abstract: Spiking reservoir computing combines the training efficiency of fixed recurrent reservoirs with the energy efficiency of event-driven inference, where computation is triggered by sparse spikes rather than executed densely at every timestep. However, existing spiking reservoirs almost exclusively rely on Leaky Integrate-and-Fire (LIF) neurons, whose low-pass dynamics limit their ability to distinguish information encoded at different temporal frequencies. We introduce HRF-Res, a liquid state machine in which LIF neurons are replaced by a heterogeneous population of Harmonic Resonate-and-Fire (HRF) neurons. These neurons exhibit intrinsic oscillatory dynamics and selectively respond to input frequencies, enabling the reservoir to capture frequency-specific temporal patterns. The result is more discriminative representations and higher classification accuracy than LIF-based reservoirs, achieved without sacrificing the energy efficiency of spiking computation, with up to 19x lower energy consumption than an equivalent non-spiking oscillatory reservoir. Evaluated on ten benchmarks spanning time series, sequential images, audio spike trains, and neuromorphic vision, HRF-Res achieves state-of-the-art accuracy among spiking reservoir methods. These results demonstrate that introducing frequency-selective dynamics into spiking reservoirs improves representational quality and classification performance while preserving their inherent energy efficiency.

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Accepted at NeurIPS 2026. 30 pages (10 main text), 4 figures. Code: this https URL

Subjects:

Artificial Intelligence (cs.AI)

Cite as:

arXiv:2610.37906 [cs.AI]

(or

arXiv:2610.37906v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.37906

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arXiv-issued DOI via DataCite

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From: Aurora Micheli [ view email ]

[v1]

Tue, 27 Oct 2026 19:45:43 UTC (267 KB)

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