Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

BRAID: Input-driven nonlinear dynamical modeling of neural-behavioral data.

... International Conference on Learning Representations·2026
Same author

Probabilistic Geometric Principal Component Analysis with application to neural data.

... International Conference on Learning Representations·2026
Same author

Unsupervised learning of multiscale switching dynamical system models from multimodal neural data.

Journal of neural engineering·2026
Same author

Author Correction: Challenges and opportunities of acquiring cortical recordings for chronic adaptive deep brain stimulation.

Nature biomedical engineering·2026
Same author

Modulation of Nonlinear Neural Dynamics for Closed-Loop Deep Brain Stimulation Systems.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Dynamical Modeling of Behaviorally Relevant Spatiotemporal Patterns in Neural Imaging Data.

Proceedings of machine learning research·2025

相关实验视频

Updated: Jan 16, 2026

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

600

BRAID:神经行为数据的输入驱动的非线性动态建模.

Parsa Vahidi1, Omid G Sani1, Maryam M Shanechi1,2,3

  • 1Electrical and Computer Engineering, University of Southern California (USC), Los Angeles, CA.

ArXiv
|October 3, 2025
PubMed
概括
此摘要是机器生成的。

我们开发了BRAID,这是一个深度学习框架,通过结合外部输入来模拟神经动态. 这种方法准确地捕捉神经行为关系,并通过将内在动态与输入效应分开来改善预测.

更多相关视频

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.8K
Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
07:41

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Published on: June 5, 2017

10.3K

相关实验视频

Last Updated: Jan 16, 2026

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

600
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.8K
Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
07:41

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Published on: June 5, 2017

10.3K

科学领域:

  • 计算神经科学是一种神经科学.
  • 机器学习 机器学习
  • 系统神经科学 系统神经科学

背景情况:

  • 神经群体表现出由外部输入影响的复杂动态.
  • 传统模型往往忽视了这些输入对神经活动和行为的影响.
  • 了解内在的神经动态对于解释行为至关重要.

研究的目的:

  • 介绍BRAID,这是一个深度学习框架,用于建模非线性神经动态.
  • 明确地将外部输入纳入神经人口模型.
  • 从输入效应中分离内在的神经动力学,以改善行为预测.

主要方法:

  • 开发了BRAID,这是一个使用输入驱动的循环神经网络的深度学习框架.
  • 纳入了一个预测目标,将动态与输入分开.
  • 使用多阶段优化方案来优先考虑与行为相关的内在动态.
  • 通过非线性模拟验证并应用于运动皮质活动数据.

主要成果:

  • 在模拟中,BRAID准确地学习神经和行为数据之间共享的内在动态.
  • 将BRAID应用于运动皮质活动,通过结合感官刺激,改善了数据的拟合.
  • 与基线方法相比,该框架增强了神经行为数据的预测.

结论:

  • BRAID提供了一种通过整合外部输入来建模神经动态的新方法.
  • 该方法有效地将内在动态与输入影响脱而出.
  • BRAID提高了对神经活动和行为的理解和预测.