Activation-Energy Pruning for Spiking Neural Networks: Unsupervised Personalization via Spike-Count Saliency
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 unde...
| Authors | Joseph Bingham |
|---|---|
| Primary category | cs.LG |
| Published | 11 August 2026 |
| Last updated at source | 11 August 2026 |
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