激活能剪枝用于脉冲神经网络：通过脉冲计数显著性实现无监督个性化
Activation-Energy Pruning for Spiking Neural Networks: Unsupervised Personalization via Spike-Count Saliency
- Technion – Israel Institute of Technology（以色列理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文研究激活能剪枝在脉冲神经网络中的应用，发现基于梯度的剪枝方法失效，而激活能剪枝在高稀疏度下提升性能，支持经验依赖性皮层特化。
AI中文摘要：
激活能剪枝——移除其幅度与累积突触前脉冲计数的乘积低于阈值的权重——已被确立为传统深度神经网络的一种有效的无监督个性化策略。本文探讨了将相同标准应用于脉冲神经网络（SNNs）时会发生什么，在SNNs中，激活能不仅仅是一个有用的启发式方法，而是一个与每个突触的代谢成本成比例的实际物理量。答案在三个方面令人惊讶。首先，在传统网络上表现良好的基于梯度的剪枝方法（SNIP、GraSP、幅度剪枝）在SNNs上始终表现不佳，在所有测试的架构和数据集上，在σ=0.2稀疏度下准确率降至接近随机水平。我们将此归因于替代梯度显著性估计与二进制脉冲序列表示之间的系统性不兼容，尽管我们不能排除替代的替代选择或超参数设置可能部分缓解该影响的可能性。其次，应用于神经形态基准的激活能剪枝在高稀疏度下优于源模型（在N-MNIST上，σ=0.8时准确率为98.4±0.4%对97.2±0.7%），这一现象在传统网络设置中没有对应物。我们将这一结果解释为与经验依赖性皮层特化一致：移除仅对非目标类别活跃的连接可能减少跨类别干扰并产生更清晰的目标表示，尽管我们注意到这是一种解释性类比而非机制性证明。
英文摘要：
Activation-energy pruning -- removing weights whose product of magnitude and cumulative pre-synaptic spike count falls below a threshold -- was established as an effective unsupervised personalization strategy for conventional deep neural networks~\citep{BINGHAM2025101242}. This paper asks what happens when the same criterion is applied to spiking neural networks (SNNs), where activation energy is not merely a useful heuristic but a literal physical quantity proportional to the metabolic cost of each synapse. The answer is surprising on three counts. First, gradient-based pruning methods that perform competitively on conventional networks (SNIP, GraSP, magnitude pruning) consistently underperform on SNNs, collapsing to near-chance accuracy by $σ= 0.2$ sparsity across all tested architectures and datasets. We trace this to a systematic incompatibility between surrogate-gradient saliency estimation and the binary spike-train representation, though we cannot rule out that alternative surrogate choices or hyperparameter settings might partially mitigate the effect. Second, activation-energy pruning applied to a neuromorphic benchmark \emph{improves} over the source model at high sparsity ($98.4 \pm 0.4\%$ vs.\ $97.2 \pm 0.7\%$ at $σ= 0.8$ on N-MNIST), a phenomenon with no counterpart in the conventional network setting. We interpret this result as consistent with experience-dependent cortical specialisation: removing connections active only for non-target classes may reduce cross-class interference and produce a cleaner target representation, though we note this is an interpretive analogy rather than a mechanistic demonstration.
