Who Needs Attention? Spiking Language Modeling via Synaptogenic Adaptive Processing Units

A pure spiking reservoir language model surpasses SpikingSSM and SpikeGPT on WikiText-103, with no attention mechanism, no structured state space prior, and no self-attention — running on one 290 used PC! We introduce SAPU-LM (Synaptogenic Adaptive Processing Unit Language Model), a family of multi-timescale spiking reservoir architectures for next-token prediction. The fundamental building block is the SAPU: a population of leaky integrate-and-fire neurons with trained recurrent weights and a characteristic membrane time constant τ. Multiple SAPUs compose a SAPU-LM; we explore serial chaining (S-SAPU-LM), parallel composition (P-SAPU-LM), the Tiled Parallel SAPU (TPSAPU) with shared weights across τ tiles, and the best-performing variant: the Parallel SAPU (PSAPU) with independent recurrent matrices per τ tile. Results. The champion PSAPU achieves 30. 03 validation PPL / 31. 11 test PPL on WikiText-103 (BPE, 50K vocabulary) with 78M parameters. This is, to our knowledge, the state of the art for pure spiking, no-attention language modeling, surpassing SpikingSSM (33. 94 PPL, AAAI-25) which relies on S4D with HiPPO-initialized state matrices. On the extreme-compression front, TPSAPU achieves 38. 74 test PPL on WikiText-103 word-level with a 50. 4 KB ternary recurrent core, exceeding SpikeGPT (39. 75 PPL, 216M params). Architectural lineage: from a frozen Echo State Network (~19, 500 PPL) to the champion PSAPU (30. 03 PPL) — an ~650× improvement attributable primarily to training reservoir weights via surrogate gradients and using a deep nonlinear decoder. Training Wʳec contributes substantially to performance over a frozen reservoir. Architecture also succeeds at motor control: PSAPU/TPSAPU variants trained on MuJoCo Humanoid-v5 via behavioral cloning achieve 5725. 1±6. 4 reward, exceeding the SAC teacher policy (5715. 9±8. 7). Hardware deployment: All SAPU-LM architectures share the same neuromorphic substrate model: the ternary recurrent core maps to analog resistor-capacitor-comparator circuits; non-spiking components run on a paired microcontroller. A proof-of-concept hardware exporter has been developed. Priority: Concurrent work NeuronSpark (arXiv, March 17 2026) trains a 0. 9B-parameter pure-spiking LM on a Chinese mixed corpus, reporting a pretraining loss of 3. 6. The initial SAPU-LM release (Zenodo, March 1 2026) preceded NeuronSpark by 16 days. Both lines independently converge on multi-timescale neuron dynamics as architecturally important for pure-SNN language modeling; the two designs are complementary (deep stack with selective state-space dynamics vs. shallow parallel reservoir with explicit τ schedule). Code and checkpoints: https: //gitlab. com/AntonioGCGonzalez/synaptogenic-adaptive-processing-unit-language-models