analiza aiqbench
This research addresses a critical bottleneck in converting Artificial Neural Networks to Spiking Neural Networks for continuous control tasks by mitigating error amplification. The proposed Cross-Step Residual Potential Initialization mechanism effectively suppresses temporally correlated errors, significantly restoring performance without requiring costly retraining or gradient updates. This advancement establishes continuous control as a vital benchmark for validating the robustness of ANN-to-SNN conversion pipelines in reinforcement learning scenarios.
Error Amplification Limits ANN-to-SNN Conversion in Continuous Control
arXiv:2601.21778v3 Announce Type: replace-cross Abstract: Spiking Neural Networks (SNNs) can achieve competitive performance by converting already existing well-trained Artificial Neural Networks (ANNs), avoiding further costly training. This property is particularly attractive in Reinforcement Learning (RL), where training through environment interaction is expensive and potentially unsafe. However, existing conversion methods perform poorly in continuous control, where suitable baselines are largely absent. We identify error amplification as the key cause: small action approximation errors become temporally correlated across decision steps, inducing cumulative state distribution shift and severe performance degradation. To address this issue, we propose Cross-Step Residual Potential Initialization (CRPI), a lightweight gradient-free mechanism that carries over residual membrane potentials across decision steps to suppress temporally correlated errors. Experiments on continuous control benchmarks with both vector and visual observations demonstrate that CRPI can be integrated into existing conversion pipelines and substantially recovers lost performance. Our results highlight continuous control as a critical and challenging benchmark for ANN-to-SNN conversion, where small errors can be strongly amplified and impact performance. Code is available at https://github.com/xuzijie32/ANN2SNN-CRPI.
Abstract:Spiking Neural Networks (SNNs) can achieve competitive performance by converting already existing well-trained Artificial Neural Networks (ANNs), avoiding further costly training. This property is particularly attractive in Reinforcement Learning (RL), where training through environment interaction is expensive and potentially unsafe. However, existing conversion methods perform poorly in continuous control, where suitable baselines are largely absent. We identify error amplification as the key cause: small action approximation errors become temporally correlated across decision steps, inducing cumulative state distribution shift and severe performance degradation. To address this issue, we propose Cross-Step Residual Potential Initialization (CRPI), a lightweight gradient-free mechanism that carries over residual membrane potentials across decision steps to suppress temporally correlated errors. Experiments on continuous control benchmarks with both vector and visual observations demonstrate that CRPI can be integrated into existing conversion pipelines and substantially recovers lost performance. Our results highlight continuous control as a critical and challenging benchmark for ANN-to-SNN conversion, where small errors can be strongly amplified and impact performance. Code is available at this https URL.
Neural and Evolutionary Computing (cs.NE); Machine Learning (cs.LG)
arXiv:2601.21778 [cs.NE]
(or arXiv:2601.21778v3 [cs.NE] for this version)
Text integral sub licența deschisă a publicației · 184 cuvinte
Subiecte conexe
J++ Lens: Jacobian Filtering Enables More Faithful Workspace Lenses
LessWrong · 8 oct. 2026
News
Quantum Computing Report · 7 iun. 2026
Asavanant and Colleagues Propose Loss-Tolerant AOFF Architecture for Quantum Teleportation
Quantum Zeitgeist · 14 iul. 2026
How Photonic Chips Differ From Electronic Chips
Quantum Zeitgeist · 19 sept. 2026