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

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 30/10/2026, 06:48:25 UTC.

Briefings

A new spiking reservoir computing method called HRF-Res replaces standard Leaky Integrate-and-Fire neurons with Harmonic Resonate-and-Fire neurons. This change allows the system to selectively respond to specific input frequencies, significantly improving classification accuracy on ten benchmarks compared to existing LIF-based methods. The study demonstrates that this approach achieves state-of-the-art performance while maintaining the energy efficiency of spiking computation, resulting in up to 19x lower energy consumption than equivalent non-spiking oscillatory reservoirs.

Key points

• The study introduces HRF-Res, a spiking reservoir computing method using Harmonic Resonate-and-Fire neurons instead of standard Leaky Integrate-and-Fire neurons.
• HRF-Res achieves state-of-the-art accuracy on ten benchmarks, including time series, sequential images, audio spike trains, and neuromorphic vision tasks.

Context

Spiking reservoir computing combines training efficiency with energy efficiency by using sparse spikes for computation. Existing methods rely on LIF neurons which limit frequency discrimination. This research introduces HRF-Res to address that limitation.

Limitations

Results are based on ten benchmarks spanning various domains. The study does not provide causal claims about why HRF-Res outperforms LIF methods, nor does it offer forecasts for future applications beyond the tested benchmarks.

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