用神经网络模拟大脑萎缩，发现重学是导致语义痴呆症状的关键。
Revisiting the Role of Relearning in Semantic Dementia
- 通过逐步删除神经元并重学，模拟大脑萎缩过程。
- 模型重现了语义痴呆特有的原型错误和跨类别混淆。
- 支持重学在慢性认知病中起核心作用，适合研究神经退行性疾病的学者。
语义痴呆（SD）患者表现出前颞叶神经元显著萎缩及类别知识渐进性丧失等行为障碍。尽管急性脑损伤（如中风）中存在遗忘知识的再学习现象，但慢性认知疾病中的再学习尚未得到广泛支持。先前研究发现深层线性人工神经网络会经历类似人类的语义学习阶段。本文利用深层线性网络验证假设：疾病进展中的再学习而非特定萎缩导致了与SD相关的特定行为模式。在网络训练出各类层次化物体的共同语义特征后，逐次删除神经元以模拟萎缩，并在删除后重新训练模型。结果显示，具备再学习能力的模型能重现SD特有的原型错误与跨类别混淆。这表明，在缺乏输出非线性的情况下，再学习对复现SD行为模式至关重要。研究结果支持一种由持续再学习驱动的SD进展理论。未来研究应重新审视再学习在认知疾病中的作用。
原文摘要 · Abstract (English)
Patients with semantic dementia (SD) present with remarkably consistent atrophy of neurons in the anterior temporal lobe and behavioural impairments, such as graded loss of category knowledge. While relearning of lost knowledge has been shown in acute brain injuries such as stroke, it has not been widely supported in chronic cognitive diseases such as SD. Previous research has shown that deep linear artificial neural networks exhibit stages of semantic learning akin to humans. Here, we use a deep linear network to test the hypothesis that relearning during disease progression rather than particular atrophy cause the specific behavioural patterns associated with SD. After training the network to generate the common semantic features of various hierarchically organised objects, neurons are successively deleted to mimic atrophy while retraining the model. The model with relearning and deleted neurons reproduced errors specific to SD, including prototyping errors and cross-category confusions. This suggests that relearning is necessary for artificial neural networks to reproduce the behavioural patterns associated with SD in the absence of \textit{output} non-linearities. Our results support a theory of SD progression that results from continuous relearning of lost information. Future research should revisit the role of relearning as a contributing factor to cognitive diseases.
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