发表机构
KEIM Institute, Albstadt-Sigmaringen University; Department of Computer Science, Chemnitz University of Technology（凯姆研究所，阿尔布施塔特 - 西格马林根大学; 计算机科学系，开姆尼茨工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究生物系统在解剖学和代谢限制下，测试兴奋性竞争Hebbian规则能否支持突触资源分配，通过与其他规则对比实验发现，Hebbian学习可在保持功能性能时权衡任务信息与表征成本，支持其作为突触资源分配机制的解释。
AI 中文摘要
引言：生物系统面临解剖学和代谢方面的限制，包括昂贵的突触维持和有限的连接性。这些限制有利于将行为相关信息压缩为低冗余模式的神经编码。我们测试兴奋性竞争Hebbian规则是否能在这种约束下支持突触资源分配，以及由此产生的表征是否比参考学习规则占据更有利的性价比机制。方法：使用从变分信息瓶颈导出的基于互信息的度量来量化表征成本。实验使用来自三个视听基准（AVE、Kinetics-Sounds、VGGSound100）的固定视听嵌入来分离下游关联可塑性。在匹配的稀疏性和架构约束下，将Hebbian学习与密集差分目标传播（DDTP）和反向传播（BP）进行比较。结果：在主要的压缩比较中，Hebbian学习比稀疏BP和DDTP实现了更低任务信息成本（CTI），同时达到了与具有非负权重的浅BP相当的CTI值。Hebbian学习不是统一提高分类性能，而是在任务相关信息和表征成本之间进行权衡，在几种设置下以可比的功能性能产生更低的CTI。讨论：结果表明存在性价比权衡而非统一的精度提升。对于给定水平的任务相关信息，Hebbian表征在保持功能性能的同时保留较少的输入信息，尽管在某些数据集上精度略有降低。这些发现支持将Hebbian学习解释为突触资源分配机制，而不是最大化视听分类精度的一般策略。
英文摘要
Introduction: Biological systems face anatomical and metabolic constraints, including costly synaptic maintenance and limited connectivity. These constraints favor neural codes that compress behaviorally relevant information into low-redundancy patterns. We test whether an excitatory competitive Hebbian rule can support synaptic resource allocation under such constraints and whether the resulting representations occupy a more favorable cost-performance regime than reference learning rules. Methods: Representational cost is quantified using mutual-information-based measures derived from the Variational Information Bottleneck. Experiments use fixed audiovisual embeddings from three audiovisual benchmarks (AVE, Kinetics-Sounds, VGGSound100) to isolate downstream associative plasticity. Hebbian learning is compared with Dense Difference Target Propagation (DDTP) and backpropagation (BP) under matched sparsity and architectural constraints. Results: Hebbian learning achieves lower task-information cost (CTI) than sparse BP and DDTP in the main compressed comparisons, while reaching CTI values comparable to shallow BP with nonnegative weights. Rather than uniformly improving classification performance, Hebbian learning shifts the trade-off between task-relevant information and representational cost, yielding lower CTI at comparable functional performance in several settings. Discussion: The results indicate a cost-performance trade-off rather than uniform accuracy gains. For a given level of task-relevant information, Hebbian representations retain less input information while preserving functional performance, although accuracy is slightly reduced on some datasets. These findings support interpreting Hebbian learning as a mechanism for synaptic resource allocation rather than as a general strategy for maximizing audiovisual classification accuracy.
