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.
翻译:在复杂行为期间,用于记录大型神经群活动的新技术为调查作为认知、认知和决策基础的神经计算提供了令人振奋的机会。非线性国家空间模型提供了一个可解释的信号处理框架,将直观动态系统与概率观测模型相结合,能够提供神经动态、神经计算以及神经假体和通过反馈控制进行治疗的洞察力。它也带来了学习潜在神经状态和内在动态系统的挑战,因为神经系统既不为人知。我们为潜在的非线性状态动态和过滤潜潜伏状态开发了一个灵活的在线学习框架。我们的方法联合优化了非线性动态系统、观察模型和黑箱识别模型的参数。与以往的方法不同,我们的框架可以包含观测噪音的非三角分布,并且具有恒定的时间和空间复杂性。这些特征使我们的方法适应实时应用,并有可能进行自动分析与实验设计,从而以可测试的方式跟踪和修改使用Simli系统设计的影响,从而跟踪和修改行为。