Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA.
Department of Neurosurgery, Emory University, Atlanta, GA, USA.
Nat Methods. 2018 Oct;15(10):805-815. doi: 10.1038/s41592-018-0109-9. Epub 2018 Sep 17.
Neuroscience is experiencing a revolution in which simultaneous recording of thousands of neurons is revealing population dynamics that are not apparent from single-neuron responses. This structure is typically extracted from data averaged across many trials, but deeper understanding requires studying phenomena detected in single trials, which is challenging due to incomplete sampling of the neural population, trial-to-trial variability, and fluctuations in action potential timing. We introduce latent factor analysis via dynamical systems, a deep learning method to infer latent dynamics from single-trial neural spiking data. When applied to a variety of macaque and human motor cortical datasets, latent factor analysis via dynamical systems accurately predicts observed behavioral variables, extracts precise firing rate estimates of neural dynamics on single trials, infers perturbations to those dynamics that correlate with behavioral choices, and combines data from non-overlapping recording sessions spanning months to improve inference of underlying dynamics.
神经科学正在经历一场革命，在这场革命中，同时记录数千个神经元正在揭示从单个神经元反应中不明显的群体动态。这种结构通常是从多次试验的平均值中提取出来的，但要更深入地理解，就需要研究在单次试验中检测到的现象，这是具有挑战性的，因为神经群体的采样不完整、试验间的变异性以及动作电位时间的波动。我们引入了通过动态系统进行的潜在因素分析，这是一种从单次神经元放电数据中推断潜在动态的深度学习方法。当应用于各种猕猴和人类运动皮质数据集时，通过动态系统进行的潜在因素分析可以准确地预测观察到的行为变量，从单次试验中提取神经动态的精确发放率估计，推断与行为选择相关的对这些动态的干扰，并结合来自跨越数月的非重叠记录会话的数据来提高对基础动态的推断。
