学习新任务让猴子视觉皮层神经反应更冗余，反而提升信息量。
Task learning increases information redundancy of neural responses in macaque visual cortex
- 通过追踪猕猴V4区神经群体活动，研究学习对信息冗余的影响。
- 训练数周及单次试次中，神经反应冗余显著增加。
- 冗余不降低信息量，反而增强单个神经元携带信息能力，适合神经科学与认知计算研究者。
大脑如何为新任务优化感官信息以支持决策？一种假说认为学习会减少神经表征的冗余以提高效率，另一种基于贝叶斯推断的假说则预测学习会增加冗余，将信息分散到多个神经元。我们通过追踪猕猴在训练期间视觉辨别任务中皮层区域V4的种群反应，检验了这些假说。结果强烈支持贝叶斯预测：任务学习在数周训练过程中以及单次试次内均显著增加了神经反应的冗余。这种冗余并未降低信息量，反而提升了单个神经元所携带的信息。这些发现表明，大脑的感觉处理反映的是一种生成而非判别性推理过程。
原文摘要 · Abstract (English)
How does the brain optimize sensory information for decision-making in new tasks? One hypothesis suggests learning reduces redundancy in neural representations to improve efficiency, while another, based on Bayesian inference, predicts learning increases redundancy by distributing information across neurons. We tested these hypotheses by tracking population responses in macaque cortical area V4 as monkeys learned visual discrimination tasks. We found strong support for the Bayesian predictions: task learning increased redundancy in neural responses over weeks of training and within single trials. This redundancy did not reduce information but instead increased the information carried by individual neurons. These insights suggest sensory processing in the brain reflects a generative rather than discriminative inference process.
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