Adaptive Spiking Neurons for Vision and Language Modeling

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 the dynamic response range of neurons to varying input intensities and effectively suppresses noise-induced spikes. We extensively evaluate KvLIF on both static image and neuromorphic datasets, demonstrating consistent improvements in classification accuracy and superior robustness compared to existing LIF models. Our work bridges biological plausibility with computational efficiency, offering a neuron model that enhances SNN performance while maintaining suitability for low-power neuromorphic deployment.

Spiking neural networks promise brain-inspired neural dynamics, yet native large-scale spiking language-model pretraining remains largely unexplored relative to conversion, distillation, and backbone-adaptation approaches. We diagnose a central obstacle: standard spiking neurons apply the same firing sensitivity to every token, creating dense- or silent-firing regimes that are difficult to resolve with globally tuned parameters alone. This problem is particularly acute in language modeling, where a single sequence contains tokens of vastly different informational roles, making a uniform firing regime poorly matched to the data. We address this with NeuronSpark, a self-attention-free language model built around input-dependent spiking membrane dynamics, where each neuron's decay rate, gain, and firing threshold adapt to the current token content. Together with gated associative memory and adaptive temporal aggregation, this selectivity enables billion-scale pretraining from random initialization without backbone transfer, conversion, or distillation. We relate the neuron to fixed-parameter spiking models and the memory to linear attention. At 1.1B parameters and 2.1B tokens, NeuronSpark reaches a 40.0 eight-task zero-shot average, exceeds comparable-budget Pythia/Mamba checkpoints, retains 95.2%/93.2% of their fully trained scores, and spontaneously develops a fast--slow hierarchy. At 2,048 tokens, parameter-matched VHDL points reduce operation-energy proxy by 90.1%/67.0% and cycles by 94.1%/80.3% versus Transformer/Mamba; a physical FPGA path reaches 24.000s TTFT and 0.082440 TPS.

Training deep spiking neural networks (SNNs) remains challenging due to sharp loss landscapes and temporal inconsistency caused by surrogate gradients. To address these challenges, we propose a unified framework: adaptive and asymmetric surrogate gradients A2SG. The adaptive gradients adjust an effective window for spatio-temporal adaptation, reducing spatial gradient variation and maintaining directional consistency of gradients over time. The asymmetric gradients reflect neuronal dynamics by assigning larger gradients to neurons with higher membrane potentials, and we prove that they yield lower variation than symmetric surrogates. Our analysis further establishes a direct connection between local gradient variation and the curvature of the loss landscape, providing a principled explanation for how A2SG promotes convergence to flatter minima and improves generalization. We conduct extensive experiments on diverse models, including CNN-based and Transformer-based SNNs, across various tasks such as image classification using both static and neuromorphic datasets, as well as segmentation. The results demonstrate that A2SG consistently improves accuracy and energy efficiency, establishing it as a general and reliable solution for training deep SNNs. Our code is available at https://github.com/KIST-NCL/A2SG.git.