[Paper] Error Amplification Limits ANN-to-SNN Conversion in Continuous Control

Source: arXiv - 2601.21778v1

Overview

The paper Error Amplification Limits ANN‑to‑SNN Conversion in Continuous Control shows why converting a high‑performing artificial neural network (ANN) into a spiking neural network (SNN) – a popular way to get low‑power, event‑driven agents – often fails on continuous‑control tasks such as robot locomotion or autonomous driving. The authors pinpoint error amplification across time steps as the root cause and introduce a lightweight, training‑free fix called Cross‑Step Residual Potential Initialization (CRPI) that dramatically restores performance.

Key Contributions

• Error‑amplification analysis: Demonstrates how tiny action‑approximation errors in converted SNNs become temporally correlated, causing a drift in the state distribution and large performance drops in continuous‑control environments.
• CRPI mechanism: Proposes a simple residual‑potential carry‑over scheme that preserves membrane potential information across decision steps, breaking the error‑correlation loop without any extra training.
• Extensive empirical validation: Shows on a suite of MuJoCo and DeepMind Control Suite benchmarks (both vector‑ and image‑based observations) that CRPI can be dropped into existing ANN‑to‑SNN pipelines and recover up to 80 % of the original ANN performance.
• Benchmark contribution: Highlights continuous control as a “stress test” for ANN‑to‑SNN conversion, providing baseline results that were previously missing in the literature.

Methodology

1. Baseline conversion pipeline – The authors start from a well‑trained feed‑forward or convolutional ANN (trained with standard RL algorithms) and apply the common rate‑coding conversion: each ANN activation is mapped to a firing rate of a leaky‑integrate‑fire (LIF) neuron, with a fixed simulation window per decision step.
2. Diagnosing error amplification – By tracking the difference between ANN and SNN actions over many timesteps, they observe that errors are not independent; instead, a mis‑predicted action nudges the environment into a new state where the SNN’s approximation error tends to repeat, leading to a feedback loop.
3. Cross‑Step Residual Potential Initialization (CRPI) – Instead of resetting membrane potentials to zero after each decision step (the usual practice), CRPI adds the residual potential left over from the previous step to the initial potential of the next step. This preserves sub‑threshold information that would otherwise be lost, smoothing the transition between consecutive actions. The method requires only a single extra line of code and no gradient updates.
4. Integration & evaluation – CRPI is inserted into three popular conversion toolkits (e.g., SNN‑Torch, BindsNET) and tested on:
   • Vector‑observation tasks (e.g., HalfCheetah, Walker2d)
   • Visual‑observation tasks (e.g., DeepMind Control Suite with pixel inputs)

Results & Findings

• Performance recovery: CRPI consistently restores ≈85‑95 % of the original ANN reward across tasks.
• Latency impact: Because CRPI does not increase the number of simulation timesteps, inference latency stays unchanged.
• Energy efficiency: The converted SNNs retain the low‑power advantage of event‑driven inference; the added residual carry‑over is a negligible overhead in hardware.

Ablation studies show that resetting potentials each step is the primary driver of error amplification, and that CRPI’s benefit is robust to different membrane time constants and firing thresholds.

Practical Implications

• Safer, cheaper RL deployment: Developers can now leverage existing high‑performing ANN policies and port them to ultra‑low‑power neuromorphic chips (e.g., Intel Loihi, BrainChip Akida) for real‑world robots or drones without costly on‑device RL training.
• Plug‑and‑play conversion: Since CRPI is a one‑line modification to the inference loop, it can be added to existing conversion pipelines or libraries with virtually no engineering effort.
• Improved reliability for continuous control: Systems that must react continuously (e.g., motor control, autonomous navigation) can now trust SNN policies to stay close to their ANN counterparts, reducing the risk of catastrophic drift.
• Benchmarking guidance: The paper’s benchmark suite gives practitioners a reference point for evaluating conversion quality on their own continuous‑control problems.

Limitations & Future Work

• Scope limited to feed‑forward policies: The study focuses on feed‑forward (or shallow recurrent) networks; deeper recurrent architectures (e.g., LSTM‑based policies) remain untested.
• Simulation‑only validation: Experiments are performed in software simulators; real‑hardware validation on neuromorphic chips is left for future work.
• Fixed timestep assumption: CRPI assumes a constant decision‑step duration; adaptive timestep strategies could further reduce error but are not explored.
• Potential interaction with quantization: The authors note that combining CRPI with weight‑quantization or spike‑based learning may introduce new dynamics that merit investigation.

Authors

• Zijie Xu
• Zihan Huang
• Yiting Dong
• Kang Chen
• Wenxuan Liu
• Zhaofei Yu

Paper Information

• arXiv ID: 2601.21778v1
• Categories: cs.NE, cs.LG
• Published: January 29, 2026
• PDF: Download PDF