Quadratic neurons outperform leaky neurons in spike-based training

By PulseAugur Editorial · [2 sources] · 2026-06-02 17:26

Researchers have demonstrated that quadratic integrate-and-fire (QIF) neurons offer a significant advantage over leaky integrate-and-fire (LIF) neurons for training spiking neural networks. Through a comparative study on the Spiking Heidelberg Digits dataset, QIF neurons showed superior performance and more stable training dynamics. The study visualized loss and gradient landscapes, revealing that LIF neurons exhibit fragmented and erratic gradients due to discontinuities, whereas QIF neurons provide a smoother training experience. AI

IMPACT Suggests QIF neurons could enable more stable and effective training for neuromorphic computing applications.

RANK_REASON The cluster contains an academic paper detailing a new finding in neural network research.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

arXiv cs.LG TIER_1 English(EN) · Carlo Wenig, Raoul-Martin Memmesheimer, Christian Klos · 2026-06-03 04:00

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. However, for the extensively used leaky integrate-and-fire (LIF) neurons, arbitrarily small p…
arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Christian Klos · 2026-06-02 17:26

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

The ability to train spiking neural networks is essential for modeling biological neural networks as well as for neuromorphic computing. However, for the extensively used leaky integrate-and-fire (LIF) neurons, arbitrarily small parameter changes can induce spike (dis)appearances…

COVERAGE [2]

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

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

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