标题： LFADS - 通过动态系统进行潜在因子分析 显示英文标题

标题： LFADS - Latent Factor Analysis via Dynamical Systems

摘要： 神经科学正在经历一场数据革命，其中同时记录数百或数千个神经元。 目前，对于如何分析此类数据尚无一致意见。 在此，我们引入LFADS（通过动态系统进行潜在因子分析），一种从同时记录的单次试验、高维神经脉冲数据中推断潜在动态的方法。 LFADS是一种基于变分自编码器的序列模型。 通过针对观测数据生成的动态系统假设， LFADS将观测到的脉冲减少为一组低维时间因子、每试验初始条件和推断输入。 我们在合成数据上将LFADS与现有方法进行比较，并表明它在推断神经放电率和潜在动态方面显著优于这些方法。 显示英文摘要

摘要： Neuroscience is experiencing a data revolution in which many hundreds or thousands of neurons are recorded simultaneously. Currently, there is little consensus on how such data should be analyzed. Here we introduce LFADS (Latent Factor Analysis via Dynamical Systems), a method to infer latent dynamics from simultaneously recorded, single-trial, high-dimensional neural spiking data. LFADS is a sequential model based on a variational auto-encoder. By making a dynamical systems hypothesis regarding the generation of the observed data, LFADS reduces observed spiking to a set of low-dimensional temporal factors, per-trial initial conditions, and inferred inputs. We compare LFADS to existing methods on synthetic data and show that it significantly out-performs them in inferring neural firing rates and latent dynamics.