arXiv AI October 31, 2026 By Aurora Micheli, Claudio Gallicchio, Nergis Tomen

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

The paper introduces HRF-Res, a spiking reservoir computing model that replaces traditional Leaky Integrate-and-Fire neurons with a heterogeneous mix of Harmonic Resonate-and-Fire neurons. These HRF neurons possess intrinsic oscillatory dynamics that selectively respond to specific input frequencies, allowing the reservoir to capture frequency‑specific temporal patterns. As a result, HRF-Res delivers more discriminative representations and higher classification accuracy across ten diverse benchmarks while maintaining the energy efficiency of spiking computation, achieving up to 19× lower energy consumption than comparable non‑spiking oscillatory reservoirs.

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benchmarks

arXiv Machine Learning

Aug 12

Delays in Spiking Neural Networks: A State Space Model Approach

arXiv:2512. 01906v3 Announce Type: replace Abstract: Spiking neural networks (SNNs) are biologically inspired, event-driven models suited for temporal data processing and energy-efficient neuromorphic computing.

By Sanja Karilanova, Subhrakanti Dey, Ay\c{c}a \"Oz\c{c}elikkale

arXiv Machine Learning

Jul 17

Scalable Training of Continuous-Time Spiking Neural Networks with Differentiable Spike-Time Discretization

arXiv:2607. 14672v1 Announce Type: new Abstract: Continuous-time spiking neural networks (SNNs) provide an event-driven framework for temporal computation, computational neuroscience, and neuromorphic hardware.

By Yusuke Sakemi, Tomoya Takeuchi, Takeo Hosomi, Kazuyuki Aihara

arXiv AI

Oct 27

Power-of-Two Leaky Integrate-and-Fire Neurons for Event-Driven Vision-Language-Action Inference

arXiv:2610. 33658v1 Announce Type: new Abstract: Vision-language-action (VLA) models have demonstrated strong capabilities in robotic manipulation, yet their reliance on dense, continuous-valued computation presents a fundamental challenge for energy-constrained deployment.

By Rui Liu, He Lv, Ziyao Zhang, Rui Ma, Rongxiang Nie

robotics multimodal

arXiv AI

Oct 23

Bio-Inspired Auditory Processing: Coupling Parallel Resonate-and-Fire Dynamics with Ternary Recurrent Networks for Speech Recognition

The paper introduces the Spiking Ternary GRU (stGRU), a bio‑inspired architecture that couples ternary‑weight gated recurrent units with resonate‑and‑fire neurons to enable efficient, matrix‑MAC‑free spiking neural networks for speech recognition. Trained end‑to‑end on LibriSpeech, the stGRU achieves single‑digit word error rates (6.44% on test‑clean) without an external language model, and also attains competitive character error rates on Aishell‑1. The model develops a depth‑graded hierarchy where shallow layers filter acoustic signals and deeper layers integrate semantic context, mirroring hierarchical processing in human cortex.

By Pavlos Poulos, Katelyn Sadorf, Themos Stafylakis, Costas A. Anastassiou

llms nlp efficiency multimodal

arXiv Machine Learning

Jun 3

Quadratic integrate-and-fire neurons exhibit less fragmented loss landscapes and outperform leaky integrate-and-fire neurons in spike-based gradient descent

arXiv:2606. 03935v1 Announce Type: cross Abstract: The ability to train spiking neural networks is essential for modeling biological neural networks as well as for neuromorphic computing.

By Carlo Wenig, Raoul-Martin Memmesheimer, Christian Klos

arXiv Machine Learning

Jun 16

Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons

arXiv:2506. 20015v2 Announce Type: replace Abstract: Neuromorphic computing offers an energy-efficient alternative to conventional deep learning accelerators, particularly for real-time processing of time-series data.

By Dengyu Wu, Jiechen Chen, H. Vincent Poor, Bipin Rajendran, Osvaldo Simeone

efficiency

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Delays in Spiking Neural Networks: A State Space Model Approach

Scalable Training of Continuous-Time Spiking Neural Networks with Differentiable Spike-Time Discretization

Power-of-Two Leaky Integrate-and-Fire Neurons for Event-Driven Vision-Language-Action Inference

Bio-Inspired Auditory Processing: Coupling Parallel Resonate-and-Fire Dynamics with Ternary Recurrent Networks for Speech Recognition

Quadratic integrate-and-fire neurons exhibit less fragmented loss landscapes and outperform leaky integrate-and-fire neurons in spike-based gradient descent

Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons