AI RESEARCH
Neuronal Self-Adaptation Enhances Capacity and Robustness of Representation in Spiking Neural Networks
arXiv CS.LG
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ArXi:2603.20687v1 Announce Type: new Spiking Neural Networks (SNNs) are promising for energy-efficient, real-time edge computing, yet their performance is often constrained by the limited adaptability of conventional leaky integrate-and-fire (LIF) neurons. Existing LIF models struggle with restricted information capacity and susceptibility to noise, leading to degraded accuracy and compromised robustness. Inspired by the dynamic self-regulation of biological potassium channels, we propose the Potassium-regulated LIF (KvLIF) neuron model. K.