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.

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