SpikingNav achieves competitive clean performance and stronger robustness with fewer parameters and lower per-step computation than a matched ANN baseline, and physical hardware validation shows that SpikingNav can be instantiated on a real neuromorphic substrate for cyber-physical systems.
Abstract
Embodied navigation requires an agent to make sequential decisions from egocentric observations in a physical environment. Existing Artificial Neural Network (ANN)-based navigation models have achieved strong performance, yet they often rely on dense computation and may degrade under visual corruptions. Spiking neural networks (SNNs) provide event-driven computation and intrinsic temporal dynamics, which are promising for compact and robust navigation on resource-constrained platforms. However, whether spike-based sensing and policy dynamics can improve robustness in visually rich embodied navigation remains an open problem. This paper proposes SpikingNav, a spiking framework for robust indoor embodied navigation. It contains a Spiking Sensing Encoder (SSE) and a Spiking Policy Network (SPN). The SSE extracts task-conditioned visual features with a spike-based backbone. The SPN maintains a recurrent policy state through membrane integration, thresholding, and spike-triggered reset. In this way, SpikingNav exploits the dynamic properties and spike activations of SNNs to improve navigation performance and robustness. We evaluate SpikingNav on PointNav and ObjectNav under clean observations and visual corruptions. SpikingNav achieves competitive clean performance and stronger robustness with fewer parameters and lower per-step computation than a matched ANN baseline. For instance, SpikingNav improves ObjectNav success from 31.05% to 34.12%, and raises the average success under visual corruptions from 8.45% to 13.71%, demonstrating the benefits of spike-based sensing and policy dynamics. We further validate the deployability of our spike-based sensing method on the Thruster-V2 neuromorphic chip. This physical hardware validation shows that SpikingNav can be instantiated on a real neuromorphic substrate for cyber-physical systems.
Experimental evaluations demonstrate that the neuromorphic SNN-based approach significantly reduces inference latency and energy consumption compared to conventional neural network baselines, making it suitable for real-time autonomous navigation tasks.
Arvind Kumar Singh, Lakshmi Narayanan· International Journal of Int...· 0 citations
SpikeWorld, a 1.45M-parameter sparse spiking model jointly trained for heterogeneous sensory prediction, semantics, image-text binding and action-conditioned dynamics, is introduced, showing that the contribution is not superior linear identification, but its integration with a frozen multimodal spiking checkpoint.
SpikingMOT is proposed as a spike-driven tracker that adaptively models sparse trajectory dynamics with spiking neural networks (SNNs) and brings SNNs into MOT, opening a promising direction for efficient tracking.
Yiding Sun, Xiangyang Yang, Dongxu Zhang et al.· 1 citation
Findings show the impact of inference stage design decisions in STDP-based SNN-VPR on recall precision, although the separate contribution of each mechanism and implementation differences is only partially disentangled and needs further examination.
Spiking neural networks (SNNs) have demonstrated competence in board games, but their application to real-time strategy (RTS) games—which demand simultaneous multi-unit control, resource management, and long-horizon planning—remains unexplored. We present the first SNN agent capable of playing a complete RTS game. Our agent uses 2,494 leaky integrate-and-fire (LIF) neurons organized in a hierarchical architecture with a recurrent strategic core, trained via proximal policy optimization (PPO) with a three-stage curriculum. In a controlled comparison with parameter-matched multilayer perceptron (MLP) and gated recurrent unit (GRU) baselines across 5 random seeds, the unmodified SNN achieves the highest peak tournament performance but collapses in all 5 seeds during extended training, while a GRU baseline achieves perfect stability (\(100\%\) win rate, 0/5 collapses). A stabilized variant combining cosine learning rate decay, an entropy floor, and checkpoint reversion eliminates collapse entirely (0/5 seeds) and dominates a 12-agent tournament (\(89.5 \pm 8.8\%\) win rate, rank 1). A per-mechanism ablation identifies checkpoint reversion as the critical stabilizer. A timestep ablation shows that 8 internal steps match 16-step peak performance with reduced collapse (2/5 vs. 5/5), ruling out a pure compute-volume confound. The entire SNN agent fits within a single Loihi 2 neuromorphic core (2,494 of 8,192 neurons per core), avoiding the inter-core routing and synchronization overhead that is often the real bottleneck on neuromorphic chips, and suggesting that neuromorphic hardware can host competitive, stable RTS agents.
Chang Liu· International Conference on...· 0 citations
Spiking neural networks (SNNs) have garnered significant attention in reinforcement learning tasks for their low power consumption. However, traditional spiking reinforcement learning (SRL) methods, which rely on local-connected encoding and fixed-threshold learning, struggle to capture the inter-dimensional correlations of input information within short timesteps, limiting the network’s expressive capacity at low timesteps. While increasing timesteps can significantly enhance performance, excessive timesteps result in substantial delays. To address this contradiction and enhance the expressive and decision-making capabilities of SNNs within short timesteps, we propose Mask-Adaptive Global Connection (MAGC), a novel encoding method that efficiently captures long-range dependencies via sparse, adaptively masked connections—enabling global feature interaction in a single timestep. Additionally, dynamic-threshold spiking neurons are introduced to effectively capture and distinguish subtle changes in input signals at each timestep, thereby enhancing the spatial-temporal state representation during spike information transmission. Extensive experimental results demonstrate that the proposed method achieves performance comparable to state-of-the-art algorithms using only a single timestep, while significantly reducing inference latency and energy consumption. When extended to multiple timesteps, our approach consistently outperforms existing methods, showing substantial improvements across eight continuous control tasks from OpenAI Gym.
Rong Xiao, Zhiyuan Hu, Ping He et al.· IEEE Transactions on Image P...· 0 citations
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