Error Amplification Limits ANN-to-SNN Conversion in Continuous Control

2026-01-29

AI Summary

In continuous control tasks, ANN-to-SNN conversion often suffers from performance degradation due to temporal accumulation of action approximation errors, which induces significant state distribution shift. This work identifies, for the first time, this error amplification over time as a key cause of conversion failure and introduces Cross-step Residual Potential Initialization (CRPI)—a lightweight, training-free mechanism that mitigates error propagation by transferring residual membrane potentials across decision steps. CRPI is agnostic to the underlying training procedure and seamlessly integrates into existing ANN-to-SNN conversion pipelines, supporting both vector-based and visual observation inputs in continuous control settings. Extensive experiments demonstrate that CRPI substantially recovers performance lost during conversion across multiple benchmarks, thereby establishing continuous control as a critical and challenging testbed for evaluating ANN-to-SNN conversion methods.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Intelligent Robots: Behavior Learning & Control
• Machine Learning: Life-Long and Continual Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Social networks and social learning

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 training-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.

Problem

• ANN-to-SNN conversion
• continuous control
• error amplification
• temporal correlation
• state distribution shift

Innovation

• Error Amplification
• ANN-to-SNN Conversion
• Continuous Control
• Spiking Neural Networks
• Residual Potential Initialization