Title: Budgeted Broadcast: An Activity-Dependent Pruning Rule for Neural Network Efficiency
Primary area: learning theory
Keywords: learning rules, neuroAI, activity-dependent pruning, structural plasticity

Abstract: Most pruning methods remove parameters ranked by impact on loss (e.g., magnitude or gradient). We propose Budgeted Broadcast (BB), which gives each unit a local traffic budget—the product of its long-term on-rate $a_i$ and fan-out $k_i$. A constrained-entropy analysis shows that maximizing coding entropy under a global traffic budget yields a selectivity–audience balance, $\log\tfrac{1-a_i}{a_i}=\beta k_i$. BB enforces this balance with simple local actuators that prune either fan-in (to lower activity) or fan-out (to reduce broadcast). In practice, BB increases coding entropy and decorrelation and improves accuracy at matched sparsity across Transformers for ASR, ResNets for face identification, and 3D U-Nets for synapse prediction, sometimes exceeding dense baselines. On electron microscopy images, it attains state-of-the-art F1 and PR-AUC under our evaluation protocol. BB is easy to integrate and suggests a path towards learning more diverse and efficient representations.