SpikeMoE: Brain-Inspired Competitive Routing for Flexible Spiking Mixture-of-Experts

Xiaoli Liu; Yujie Liang; Jialin Li; Malu Zhang

cs.AIOct 1, 2026

SpikeMoE: Brain-Inspired Competitive Routing for Flexible Spiking Mixture-of-Experts

Authors: Xiaoli Liu, Yujie Liang, Jialin Li, Malu Zhang

Organizations: School of Computer Science and Engineering, University of Electronic Science and Technology of China

Abstract

Spiking Neural Networks (SNNs) enable event-driven computation through biologically inspired dynamics at the neuronal scale, while Mixture-of-Experts (MoE) perform conditional computation through expert selection at the model scale. Integrating their strengths offers potential for flexible neural architectures. A key challenge, however, lies in designing an expert selection mechanism based on spiking activity. To address this, we introduce a spike-based k-WTA Router inspired by competition-inhibition observed in the hippocampal CA1 region. The router incorporates lateral inhibition and refractory period to select Top-K experts according to discrete spike counts. Building on this, we present SpikeMoE, a framework that integrates neuronal-scale spiking dynamics with model-scale expert selection. To address incomplete multisensory inputs in multimodal tasks, we further equip SpikeMoE with a two-stage missing-modality modeling module that combines empirical prototypes from an observed-modality pool with modality-specific learnable embeddings to construct missing-modality representations. Experiments on vision, language, and multimodal benchmarks demonstrate that SpikeMoE achieves state-of-the-art performance among the SNN baselines, matches or exceeds the performance of ANN counterparts, and maintains robustness across diverse missing-modality conditions. These results demonstrate a favorable trade-off between performance and energy efficiency, validating the integration of spiking dynamics with sparse expert computation and highlighting SpikeMoE as a promising approach to energy-efficient brain-inspired computing.

Figures & tables

Figure 1: (a) Overall general framework; (b) Spike expert neuron.

Figure 2: (a) Common inhibitory patterns in the hippocampus; (b) Competitive selection in k-WTA Router; (c) Expert selection.

Figure 3: Two-stage missing modality modeling.

Figure 4: Visualization of routers as masks. The mask position (black) indicates router of 0 here. We show the dynamic sparsity for the selected experts (vertical) and time-steps (horizontal).

Methods	Architecture	Param (M)	Energy Con- sumption (mJ)	Time Step	Top-1 Acc (%)
Spikformer ( Zhou et al., 2022 )	Spikformer-8-384	16.81	12.43	4	70.24
Spikformer ( Zhou et al., 2022 )	Spikformer-8-512	29.68	18.82	4	73.38
Spikformer+SEMM ( Zhou et al., 2024 )	Spikformer-8-384	16.05	11.50*	4	72.86
Spikformer+SEMM ( Zhou et al., 2024 )	Spikformer-8-512	28.22	17.63*	4	75.93
	Spikformer-8-384	16.03	11.39	4	73.22
Spikformer+k-WTA Router	Spikformer-8-512	28.19	16.72	4	75.97

Table 1: Results on ImageNet. Model-

L

D

represents a model with

L

encoder blocks and

D

channels.

Model	Energy (mJ)	Time	MNLI -m/mm	QQP F1	QNLI	SST-2	CoLA	STS-B	MRPC F1	RTE	Avg.
BERT base ( Devlin et al., 2019 )	51.41	/	83.8/83.4	90.5	90.7	92.3	60.0	89.4	89.8	69.3	83.2
BERT 3L ( Devlin et al., 2019 )	12.90	/	77.1/77.1	85.2	85.8	88.1	31.7	85.7	86.4	66.4	75.9
Q2BERT ( Zhang et al., 2020 )	/	/	47.2/47.3	67.0	61.3	80.6	0.0	4.7	81.2	52.7	49.1
ELMo ( Peters et al., 2018 )	/	/	68.6/-	86.2	71.1	91.5	44.1	70.4	76.6	53.4	70.2
SpikeBERT ( Lv et al., 2023 )	14.30	4	71.4/71.0	68.2	66.4	85.4	16.9	18.7	82.0	57.5	59.7
LIF-BERT* ( Gerstner et al., 2014 ; Xing et al., 2024 )	/	4	35.4/35.2	0.0	50.5	50.9	0.0	0.0	81.2	52.7	34.6

Table 2: Performance comparison on GLUE benchmark.

Figure 5: UMAP visualization of multi-modal embedding space: CMU-MOSI.

Missing Ratio	Metric	ANNs				SNNs			Ours
Missing Ratio	Metric	ShaSpec ( Wang et al., 2023a )	TF ( Zadeh et al., 2017 )	mmFormer ( Zhang et al., 2022 )	FuseMoE ( Han et al., 2024 )	Weight Attention ( Liu et al., 2022 )	SCA ( Guo et al., 2023 )	S-CMRL ( He et al., 2025 )	SpikeMoE
0%	F1	41.78 $\pm$ 0.64	43.28 $\pm$ 0.36	50.22 $\pm$ 0.20	57.02 $\pm$ 0.39	50.41 $\pm$ 0.44	46.95 $\pm$ 0.21	49.01 $\pm$ 0.29	59.59 $\pm$ 0.23
0%	AUC	65.08 $\pm$ 0.39	64.48 $\pm$ 0.51	74.62 $\pm$ 0.49	77.26 $\pm$ 0.20	72.09 $\pm$ 0.38	70.33 $\pm$ 0.46	73.94 $\pm$ 0.50	78.48 $\pm$ 0.34
10%	F1	43.40 $\pm$ 0.74	40.14 $\pm$ 0.13	48.66 $\pm$ 1.20	55.81 $\pm$ 0.45	48.19 $\pm$ 0.52	46.81 $\pm$ 0.71	47.50 $\pm$ 0.06	56.51 $\pm$ 0.12
10%	AUC	65.45 $\pm$ 0.56	61.54 $\pm$ 0.66	73.21 $\pm$ 0.76	77.05 $\pm$ 0.93	74.68 $\pm$ 0.17	70.51 $\pm$ 0.02	72.12 $\pm$ 0.27	77.45 $\pm$ 0.67
20%	F1	39.15 $\pm$ 0.13	42.64 $\pm$ 0.30	48.51 $\pm$ 1.15	53.42 $\pm$ 0.58	47.46 $\pm$ 0.02	47.43 $\pm$ 0.29	47.35 $\pm$ 0.35	53.73 $\pm$ 0.92

Table 3: Experiment Setting I: Main results on CMU-MOSI. Top-3 results are highlighted as first , second and third .

Testing Modality			Metric	ANNs				SNNs			Ours
Video	Text	Audio	Metric	ShaSpec ( Wang et al., 2023a )	TF ( Zadeh et al., 2017 )	mmFormer ( Zhang et al., 2022 )	FuseMoE ( Han et al., 2024 )	Weight Attention ( Liu et al., 2022 )	SCA ( Guo et al., 2023 )	S-CMRL ( He et al., 2025 )	SpikeMoE
✓	✓		F1	41.27 $\pm$ 0.81	34.96 $\pm$ 0.72	48.25 $\pm$ 0.11	30.51 $\pm$ 2.53	25.75 $\pm$ 0.53	47.55 $\pm$ 0.56	48.86 $\pm$ 0.34	69.56 $\pm$ 0.23
			AUC	61.28 $\pm$ 0.88	59.37 $\pm$ 1.12	72.72 $\pm$ 0.08	55.35 $\pm$ 1.11	54.39 $\pm$ 0.12	71.98 $\pm$ 0.43	73.22 $\pm$ 0.06	74.29 $\pm$ 0.18
✓		✓	F1	36.99 $\pm$ 2.03	34.83 $\pm$ 0.54	35.79 $\pm$ 0.55	24.29 $\pm$ 4.23	22.18 $\pm$ 0.37	46.97 $\pm$ 0.08	23.82 $\pm$ 0.39	47.81 $\pm$ 0.61
			AUC	55.79 $\pm$ 2.53	57.87 $\pm$ 0.32	52.21 $\pm$ 0.31	56.24 $\pm$ 1.96	50.45 $\pm$ 0.08	68.56 $\pm$ 0.23	51.55 $\pm$ 0.28	69.04 $\pm$ 0.18
	✓	✓	F1	43.72 $\pm$ 1.64	37.25 $\pm$ 0.53	45.53 $\pm$ 0.33	20.68 $\pm$ 2.14	49.40 $\pm$ 0.23	19.18 $\pm$ 0.47	48.09 $\pm$ 0.21	49.47 $\pm$ 0.38

Table 4: Experiment Setting II: Main results on CMU-MOSI. Top-3 results are highlighted as first , second and third .

Methods	Missing Ratio=50%
Methods	F1	AUC
Fully k-WTA Router	$43.54\pm 0.03$	$67.30\pm 0.71$
w/o Lateral Inhibition	$41.37\pm 0.71$	$66.02\pm 0.49$
w/o Refractory Period	$42.50\pm 0.34$	$66.12\pm 0.56$
w/o Inhibition Strength	$40.41\pm 0.43$	$64.59\pm 0.80$

Table 5: Ablation on k-WTA Router.

Figure 6: Visualization of expert activation.

Figure 7: Ablation on two-stage strategy.

Figure 8: Sensitivity analysis on CMU-MOSEI (Missing ratio=50%).

Appendix figures & tables7 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 9: UMAP visualization of multi-modal embedding space: CMU-MOSEI.

Metric	ShaSpec	TF	mmFormer	FuseMoE	WeightAttention	SCA	S-CMRL	SpikeMoE
	ANNs				SNNs			Ours
Ops (M)	21.73	48.42	1577.63	199.8	47.07	32.57	289.60	45.22
Params	2,177,406	4,211,331	11,373,315	243,543,180	131,585	264,192	2,685,729	2,472,370
AUC	55.60 $\pm$ 0.12	60.49 $\pm$ 0.80	61.87 $\pm$ 0.67	67.28 $\pm$ 0.28	65.81 $\pm$ 0.05	64.49 $\pm$ 0.43	65.71 $\pm$ 0.43	67.30 $\pm$ 0.71
Energy (mJ)	20.13	25.19	40.39	11.71	10.38	16.95	14.67	5.29

Appendix

Table 6: Complexity and Energy Consumption analysis

Parameter	Value
Number of Expert	4 for ImageNet-1K and GLUE, 6 for CMU-MOSI and CMU-MOSEI, 4 for UrbanSound8k-AV
Top-K Expert	2
Timesteps $T$	4 for ImageNet-1K, GLUE and UrbanSound8K-AV; 6 for CMU-MOSI and CMU-MOSEI
Learning Rate	5e-5
Hidden Size	128
MoE layers	2

Appendix

Table 7: Hyperparameter setting

Missing Ratio	Metric	ANNs				SNNs			Ours
Missing Ratio	Metric	ShaSpec ( Wang et al., 2023a )	TF ( Zadeh et al., 2017 )	mmFormer ( Zhang et al., 2022 )	FuseMoE ( Han et al., 2024 )	Weight Attention ( Liu et al., 2022 )	SCA ( Guo et al., 2023 )	S-CMRL ( He et al., 2025 )	SpikeMoE
0%	F1	47.48 $\pm$ 0.27	38.89 $\pm$ 0.54	56.75 $\pm$ 0.47	57.04 $\pm$ 0.15	59.40 $\pm$ 0.42	59.36 $\pm$ 0.06	58.09 $\pm$ 0.19	59.80 $\pm$ 0.17
0%	AUC	67.93 $\pm$ 0.56	64.26 $\pm$ 0.61	76.42 $\pm$ 0.24	78.54 $\pm$ 0.42	73.59 $\pm$ 0.08	74.41 $\pm$ 0.13	72.63 $\pm$ 0.21	78.63 $\pm$ 0.53
10%	F1	47.41 $\pm$ 0.18	34.94 $\pm$ 1.10	55.37 $\pm$ 0.47	55.90 $\pm$ 0.55	54.97 $\pm$ 0.35	56.81 $\pm$ 0.35	56.23 $\pm$ 0.32	56.83 $\pm$ 0.23
10%	AUC	69.26 $\pm$ 0.22	61.05 $\pm$ 0.34	75.36 $\pm$ 0.53	76.38 $\pm$ 0.39	72.18 $\pm$ 0.34	73.12 $\pm$ 0.23	73.17 $\pm$ 0.09	76.47 $\pm$ 0.49
20%	F1	46.54 $\pm$ 1.04	35.37 $\pm$ 0.94	52.91 $\pm$ 0.75	54.44 $\pm$ 0.27	53.14 $\pm$ 0.17	54.21 $\pm$ 0.11	52.84 $\pm$ 0.45	54.35 $\pm$ 0.57

Appendix

Table 8: Experiment Setting I: Main results on CMU-MOSEI. Top-3 results are highlighted as first , second and third .

Testing Modality			Metric	ANNs				SNNs			Ours
Video	Text	Audio	Metric	ShaSpec ( Wang et al., 2023a )	TF ( Zadeh et al., 2017 )	mmFormer ( Zhang et al., 2022 )	FuseMoE ( Han et al., 2024 )	Weight Attention ( Liu et al., 2022 )	SCA ( Guo et al., 2023 )	S-CMRL ( He et al., 2025 )	SpikeMoE
✓	✓		F1	40.75 $\pm$ 0.14	31.38 $\pm$ 0.61	56.02 $\pm$ 0.56	55.85 $\pm$ 0.69	33.34 $\pm$ 0.28	52.80 $\pm$ 0.76	55.62 $\pm$ 0.34	56.25 $\pm$ 0.32
			AUC	64.47 $\pm$ 0.10	56.98 $\pm$ 0.20	76.60 $\pm$ 0.39	75.81 $\pm$ 0.82	65.91 $\pm$ 0.45	71.60 $\pm$ 0.04	73.21 $\pm$ 0.45	77.09 $\pm$ 0.44
✓		✓	F1	38.15 $\pm$ 0.92	31.73 $\pm$ 0.16	37.12 $\pm$ 0.66	49.30 $\pm$ 0.67	22.00 $\pm$ 0.49	29.01 $\pm$ 0.04	22.10 $\pm$ 0.20	49.50 $\pm$ 0.36
			AUC	58.41 $\pm$ 0.10	58.41 $\pm$ 0.17	56.72 $\pm$ 0.84	56.32 $\pm$ 0.62	50.91 $\pm$ 0.57	51.26 $\pm$ 0.25	50.30 $\pm$ 0.06	57.08 $\pm$ 0.24
	✓	✓	F1	40.82 $\pm$ 0.12	31.86 $\pm$ 0.23	56.06 $\pm$ 0.29	57.02 $\pm$ 0.60	56.06 $\pm$ 0.48	33.90 $\pm$ 0.21	21.93 $\pm$ 0.83	58.53 $\pm$ 0.02

Appendix

Table 9: Experiment Setting II: Main results on CMU-MOSEI. Top-3 results are highlighted as first , second and third .

Figure 10: (a) Performance on UrbanSound8k-AV across Missing Ratios; (b) Ablation study on UrbanSound8k-AV (Experiment Setting I, Missing Ratio 50%).

Figure 11: (a) Latency Breakdown across Expert Counts; (b) Impact of wta-timesteps on Latency.

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Spiking Neural Networks (SNNs) provide an energy-efficient paradigm for visual recognition. We present SpikingMoE, which integrates a spike-driven Transformer with a Mixture-of-Experts (MoE) framework for dynamic computation. Inspired by the lateral geniculate nucleus (LGN), a spike-driven prompt (SDprompt) enables input-dependent expert routing in a biologically plausible manner. By replacing standard MLPs with spike-compatible expert modules and enforcing binary spike communication, SpikingMoE is designed for neuromorphic hardware. Experiments on CIFAR-10 and CIFAR-100 achieve 94.09% and 74.54% top-1 accuracy, showing that modular expert routing can be incorporated while retaining reasonable performance. To our knowledge, SpikingMoE is the first open-source SNN framework that integrates MoE into a spike-driven Transformer with LGN-inspired routing.

Abstract

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