ArticleFrontiers in neuroscience2026
Spike-based Q-learning in a non-von Neumann architecture.
Article in Frontiers in neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- DeltaQ: Value-Guided Hebbian Learning in Spiking Neuronal Networks for Multi-Goal Navigation.bioRxiv : the preprint server for biology · 2026Article
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14 authors.
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Abstract
Non-von Neumann architectures overcome the memory-compute separation of von Neumann systems by distributing computation and memory locally, thereby reducing data-transfer bottlenecks and power consumption. These features are particularly advantageous for reinforcement learning (RL) workloads that rely on frequent value-function updates across large state-action spaces. When combined with event-driven spiking neural networks (SNNs), non-von Neumann architectures can further improve overall computational efficiency by leveraging the sparse nature of spike-based processing. In this study, we propose a hardware-feasible SNN-based non-von Neumann architecture that performs Q-learning, one of the most widely known reinforcement learning algorithms. The proposed architecture maps states and actions to individual neurons using one-hot encoding and locally stores each state-action pair's
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