具有非线性注射读数的表达力学模型能够从神经活动中可靠地恢复潜伏特征
Christopher Versteeg1, Andrew R Sedler1,2, Jonathan D McCart1,2
1Wallace H. Coulter Department of Biomedical Engineering Emory University and Georgia Institute of Technology Atlanta, GA, USA.
ArXiv
|September 25, 2023
概括
我们开发了ODIN,这是一种分析神经动态的新方法. 它使用更少的维度准确地重建神经活动,有助于理解神经计算.
科学领域:
- 神经科学是一个神经科学.
- 计算神经科学是一种神经科学.
- 机器学习 机器学习
背景情况:
- 大规模的神经记录为神经电路机制提供了洞察力.
- 神经动力学是通过低维的潜在空间模型近似的.
- 从潜伏到神经空间的映射会影响解释性,并且可能缺乏注射性.
结论:
- ODIN是一个有前途的方法来提炼可解释的神经动态.
- 通过揭示潜在的动态,提高对神经计算的理解.
- 实现了最先进的重建准确度,并减少了维度.
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