Neuronal Self-Adaptation Enhances Capacity and Robustness of Representation in Spiking Neural Networks
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. KvLIF introduces an auxiliary conductance state that integrates membrane potential and spiking history to adaptively modulate neuronal excitability and reset dynamics. This design extends th...
| Authors | Zhuobin Yang, Yeyao Bao, Liangfu Lv et al. |
|---|---|
| Primary category | cs.LG |
| Published | 21 March 2026 |
| Last updated at source | 21 March 2026 |
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