感觉对齐感受野的计算价值取决于神经元表达性

中文摘要 AI 辅助

生物感觉神经元具有选择性感受野，这些感受野沿着有意义的刺激坐标（如频率、运动方向或视网膜拓扑位置）进行组织。这种结构可能源于高效编码以及活动、连接性和布线的生物约束，正如跨模态的简单神经元计算研究所显示的那样。这引发了一个问题：结构化感受野是否在资源效率本身之外赋予计算优势，并且当单个神经元具有高度表达性时，这种优势是否仍然存在？我们在表达性漏记忆神经元（Expressive Leaky Memory neurons）的循环网络中解决这个问题，其中我们可以独立改变神经元的复杂性和前馈感受野的组织方式。在听觉和基于事件的视觉分类任务中，与任务相关感觉坐标对齐的感受野相对于预算匹配的随机感受野提高了测试准确率。当感觉坐标被打乱或感受野遵循与任务无关的坐标时，这种优势消失，表明收益来自与任务几何形状的对齐，而非仅仅是受限的连接。增加神经元的复杂性降低了结构化感受野的性能优势。最后，通用的突触稀疏性正则化诱导输入选择性并部分恢复性能，但仍远低于显式结构化感受野，表明仅靠稀疏性不足以恢复任务对齐感受野的全部计算收益。总之，我们的结果表明，适当的感受野可以作为超越稀疏性本身的计算先验，并且它们的价值取决于单个神经元的计算表达性。

英文摘要

Biological sensory neurons have selective receptive fields organized along meaningful stimulus coordinates, such as frequency, motion direction, or retinotopic position. Such structure may arise from efficient coding and biological constraints on activity, connectivity, and wiring, as computational studies of simple neurons have shown across modalities. This raises a question: do structured receptive fields confer a computational advantage beyond resource efficiency itself, and does this advantage persist when individual neurons are highly expressive? We address this question in recurrent networks of Expressive Leaky Memory neurons, where we can independently vary neuronal complexity and the organization of feed-forward receptive fields. Across auditory and event-based visual classification tasks, receptive fields aligned with a task-relevant sensory coordinate improve test accuracy relative to budget-matched random receptive fields. This advantage disappears when sensory coordinates are scrambled, or when receptive fields follow task-irrelevant coordinates, showing that the benefit comes from alignment with task geometry rather than restricted connectivity alone. Increasing neuronal complexity reduces the performance advantage of structured receptive fields. Finally, generic synaptic sparsity regularization induces input selectivity and partially recovers performance, but remains substantially below explicitly structured receptive fields, suggesting that sparsity alone is insufficient to recover the full computational benefit of task-aligned receptive fields. Together, our results show that appropriate receptive fields can serve as a computational prior beyond sparsity itself, and that their value depends on the computational expressivity of individual neurons.

发表机构

• University of Tübingen（蒂宾根大学）
• Max Planck Institute for Biological Cybernetics（马克斯·普朗克生物控制论研究所）

机构由 AI 辅助整理，请以论文原文为准。