[Paper] Plug-and-Play Homeostatic Spark: Zero-Cost Acceleration for SNN Training Across Paradigms

Source: arXiv - 2512.05015v1

Overview

Training spiking neural networks (SNNs) has long been hampered by slow convergence and fragile optimization, even though SNNs promise event‑driven, low‑power inference. The paper introduces Adaptive Homeostatic Spiking Activity Regulation (AHSAR) – a plug‑and‑play “zero‑cost” module that keeps each layer’s firing activity in a healthy range, dramatically speeding up training without touching the network architecture, loss function, or gradient computation.

Key Contributions

• AHSAR layer‑wise homeostasis: a lightweight, parameter‑free mechanism that rescales neuron thresholds based on real‑time firing‑rate deviations.
• Cross‑layer diffusion: a bounded smoothing operation that prevents any single layer from becoming a bottleneck or exploding in activity.
• Global epoch‑wise gain: an adaptive scalar that blends validation progress with overall activity energy, automatically steering the network toward the optimal operating point.
• Training‑paradigm agnostic: works with surrogate‑gradient back‑propagation, ANN‑to‑SNN conversion, and biologically‑inspired learning rules alike.
• Broad empirical validation: consistent speed‑ups and accuracy gains across multiple SNN depths, widths, time‑step settings, and datasets (both frame‑based RGB and event‑based DVS).
• Zero additional parameters & negligible compute: the method adds virtually no runtime overhead, making it suitable for on‑device training or rapid prototyping.

Methodology

1. Forward‑pass monitoring – For each layer, AHSAR records the average firing rate (spikes per timestep).
2. Centered deviation → threshold scaling – The deviation from a target firing‑rate band is passed through a bounded non‑linear function (e.g., a tanh) that produces a multiplicative factor for the neuron’s firing threshold. Raising the threshold suppresses excess spikes; lowering it encourages activity when the layer is too quiet.
3. Cross‑layer diffusion – The per‑layer scaling factors are smoothed across neighboring layers using a lightweight diffusion kernel, ensuring the network does not develop sharp activity cliffs.
4. Global gain update – At the end of each epoch, a global gain is adjusted slowly (e.g., via an exponential moving average) based on two signals: (a) validation loss improvement and (b) the total “activity energy” (sum of squared firing rates). This gain uniformly scales all layer thresholds, nudging the whole network toward a balanced firing regime.
5. No trainable weights – All operations are deterministic and differentiable, so the standard gradient flow remains untouched. The only “learning” happens in the homeostatic state variables that are updated analytically.

Results & Findings

*OOD = out‑of‑distribution.

Key takeaways

• Consistent convergence acceleration across all training paradigms (surrogate‑gradient, conversion, STDP‑like rules).
• Higher final accuracy despite the same number of training epochs, indicating better optimization landscapes.
• Improved robustness to distribution shifts, likely because the network never over‑specializes to a narrow firing‑rate regime.

Practical Implications

• Faster prototyping: Developers can train deeper SNNs in a fraction of the time, making iterative design cycles more feasible.
• Energy‑aware training: Since AHSAR adds virtually no compute, the overall training energy budget stays low—critical for edge‑device learning scenarios.
• Plug‑and‑play library integration: The method can be wrapped as a thin PyTorch/TensorFlow module that automatically hooks into any existing SNN codebase, requiring only a few lines of configuration.
• Better hardware mapping: Maintaining moderate firing rates reduces spike traffic, which translates to lower bandwidth and memory pressure on neuromorphic chips (e.g., Loihi, Intel’s Neuromorphic Platform).
• Robust deployment: The observed OOD gains suggest that models trained with AHSAR will be more tolerant to sensor noise, lighting changes, or domain drifts—common in robotics and IoT applications.

Limitations & Future Work

• Target band selection: The current implementation uses a fixed target firing‑rate band; adapting this band per task or dataset could yield further gains.
• Theoretical analysis: While empirical results are strong, a formal convergence proof or information‑theoretic justification is still missing.
• Scalability to ultra‑large SNNs: Experiments stop at ~10 M synapses; testing on massive networks (e.g., brain‑scale simulations) remains an open question.
• Integration with quantization: Future work could explore how AHSAR interacts with weight/threshold quantization, a common requirement for neuromorphic ASICs.

Bottom line: AHSAR offers a zero‑cost, universally compatible boost for SNN training, turning the often‑painful convergence problem into a manageable engineering detail. For developers looking to harness the low‑power promise of spiking networks without sacrificing training speed or robustness, this plug‑and‑play homeostatic regulator is a compelling addition to the SNN toolbox.

Authors

• Rui Chen
• Xingyu Chen
• Yaoqing Hu
• Shihan Kong
• Zhiheng Wu
• Junzhi Yu

Paper Information

• arXiv ID: 2512.05015v1
• Categories: cs.NE
• Published: December 4, 2025
• PDF: Download PDF