在生物和人工神经网络中表示的普遍性
Eghbal Hosseini1,2, Colton Casto1,2,3,4, Noga Zaslavsky2,5
1Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
bioRxiv : the preprint server for biology
|January 7, 2025
概括
高性能的人工神经网络 (ANN) 和大脑汇聚在类似的表示上,解释了它们的对齐. 这种趋同,称为表示普遍性,为使用ANN研究生物计算提供了一种新方法.
科学领域:
- 计算神经科学是一种计算神经科学.
- 认知科学是一种认知科学.
- 人工智能的人工智能是人工智能.
背景情况:
- 在自然数据上训练的人工神经网络 (ANN) 通常与生物系统的行为和神经表示保持一致.
- 这种ANN与生物系统之间的调整的原因仍然是一个活跃的研究领域.
研究的目的:
- 调查ANN与生物系统之间的对齐是否是由于共享表示的趋同.
- 开发一种方法来量化模型间代表性协议及其对模型与大脑对齐的影响.
- 识别驱动模型和大脑对表征的同意或不同意的刺激特征.
主要方法:
- 开发了一种新的方法来识别系统地变化的刺激.
- 使用自然主义数据在语言和视觉领域测试了该方法.
- 分析了刺激特征,区分高和低一致的句子和图像集.
主要成果:
- 证明高性能ANN和大脑在类似的表示上趋同.
- 显示,来自高和低一致性集的刺激可预测地调节模型与大脑的对齐.
- 确定了特定的刺激特征,区分高和低一致性表示.
结论:
- 代表的普遍性是模型与大脑对齐的一个关键因素.
- 人工神经网络为揭示生物表征和计算结构提供了一个强大的工具.
- 这种方法为理解认知背后的计算原理提供了一个新的途径.
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