Variational Joint Filtering
Variational Joint Filtering
复制标题
变分联合过滤
DOI：
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复制
发表时间：
2017
期刊：
影响因子：
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通讯作者：
Il Memming Park
中科院分区：
文献类型：
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作者：
Yuan Zhao;Il Memming Park
记录复杂行为中大型神经群活动的新技术为研究感知、认知和决策基础的神经计算提供了令人兴奋的机会。非线性状态空间模型将直观的动态系统与概率观测模型相结合，提供了一个可解释的信号处理框架，可以为神经动力学、神经计算、神经修复的发展以及通过反馈控制的治疗提供见解。然而，这也带来了学习潜在神经状态和潜在动力系统的挑战，因为对于先验的神经系统来说，这两者都是未知的。我们开发了一个灵活的在线学习框架，用于潜在非线性状态动力学和过滤潜在状态。该方法采用随机梯度变分贝叶斯方法，对非线性动力系统、观测模型和黑箱识别模型的参数进行联合优化。与以前的方法不同，我们的框架可以包含观测噪声的非平凡分布，并且具有恒定的时间和空间复杂性。这些特点使我们的方法适用于实时应用，并有可能自动化分析和实验设计，以可测试地跟踪和修改使用旨在影响学习的刺激的行为。
New technologies for recording the activity of large neural populations during complex behavior provide exciting opportunities for investigating the neural computations that underlie perception, cognition, and decision-making. Nonlinear state-space models provide an interpretable signal processing framework by combining an intuitive dynamical system with a probabilistic observation model, which can provide insights into neural dynamics, neural computation, and development of neural prosthetics and treatment through feedback control. It yet brings the challenge of learning both latent neural state and the underlying dynamical system because neither is known for neural systems a priori. We developed a flexible online learning framework for latent nonlinear state dynamics and filtered latent states. Using the stochastic gradient variational Bayes approach, our method jointly optimizes the parameters of the nonlinear dynamical system, the observation model, and the black-box recognition model. Unlike previous approaches, our framework can incorporate non-trivial distributions of observation noise and has constant time and space complexity. These features make our approach amenable to real-time applications and the potential to automate analysis and experimental design in ways that testably track and modify behavior using stimuli designed to influence learning.
DOI：
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发表时间：
2008
期刊：
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影响因子：
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作者：
Jonathan W. Pillow;Jonathon Shlens;Liam Paninski;A. Sher;A. Litke;E. Chichilnisky;E. Simoncelli
通讯作者：
Jonathan W. Pillow;Jonathon Shlens;Liam Paninski;A. Sher;A. Litke;E. Chichilnisky;E. Simoncelli
影响因子：
2.5
作者：
Truccolo, W;Eden, UT;Brown, EN
通讯作者：
Brown, EN