解开叠加:可解释的大脑编码模型与稀有的概念原子
bioRxiv : the preprint server for biology
|February 9, 2026
概括
我们开发了一个稀疏概念编码模型来解释大脑活动. 该模型增强了对大脑如何处理自然语言概念的理解,改进了现有的人工神经网络方法.
科学领域:
- 神经科学是一个神经科学.
- 认知科学 认知科学
- 人工智能的人工智能
背景情况:
- 使用人工神经网络 (ANN) 功能的编码模型预测大脑对刺激的反应.
- 解释这些模型是具有挑战性的,因为特征叠加和纠在密集的嵌入.
- 这种纠阻止了语义特征和语音选择性的清晰识别.
研究的目的:
- 开发一种新的编码模型,以提高大脑反应的可解释性.
- 为了解决密集嵌入中特征纠的限制.
- 为了从voxel权重中直接读取概念选择性.
主要方法:
- 介绍了Sparse概念编码模型 (SCEM).
- 将密集的嵌入物转化为更高维度的,稀疏的,学习概念原子的非负空间.
- 将SCEM应用于听故事的功能磁共振成像 (fMRI) 数据.
主要成果:
- SCEM的预测性能与传统的密集模型相美.
- 该模型显著提高了神经表征的可解释性.
- 能够解开重叠的皮质表示 (例如时间,空间,数).
结论:
- 该SCEM提供了一个可扩展和可解释的桥梁在ANN特征和大脑表示.
- 为概念图的新型神经科学分析提供了一个框架.
- 促进对人类大脑中语义特征编码的更深入的理解.
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