一个基于LSTM的对抗性变异自编码框架,用于自我监督的行为选择的神经解码.
Shiva Salsabilian1, Christian Lee2, David Margolis2
1Integrated Systems and NeuroImaging Laboratory, Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ 08854, United States of America.
Journal of neural engineering
|April 15, 2024
概括
这项研究引入了一个新的框架,用于无监督的行为分析和主体不变的神经解码. 该方法在跨主体神经解码中达到89.7%的准确性,增强神经活动和行为之间的联系.
科学领域:
- 神经科学是一个神经科学.
- 机器学习 机器学习
- 计算生物学 计算生物学
背景情况:
- 将神经数据与行为联系起来是具有挑战性的,因为无监督的行为分析和特定主体的神经变异存在困难.
- 开发通用的神经解码模型需要提取主体不变特征.
研究的目的:
- 为无监督行为数据分析和自动标签生成提供数据驱动的解决方案.
- 为一般化神经解码模型提取主体不变特征.
- 改善神经解码中的跨学科转移学习.
主要方法:
- 一种无监督的自动编码方法,用于行为数据转换和集群友好的功能空间生成.
- 使用软集群分配损失来改进特征表示的集群的代改进.
- 使用基于长期短期记忆的对抗变异自编码器 (LSTM-AVAE) 进行对抗学习,以捕获跨主体共享的神经信息.
主要成果:
- 拟议的模型在鼠标皮质录音上的跨主体神经解码中实现了89.7%的准确性.
- 反对网络有效地消除了神经表征中的对象依赖性,改善了转移学习.
- LSTM-AVAE在捕捉神经数据中的时间依赖性方面表现出有效性.
结论:
- 该框架对于无监督的行为数据集群和标签生成是有效的.
- 跨主体神经解码的高精度表明该模型在将神经活动与行为联系起来方面的潜力.
- 这种方法为通用的神经解码和理解神经与行为关系提供了一个强大的解决方案.
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