标题： Langevin 流用于建模神经潜在动力学 显示英文标题

标题： Langevin Flows for Modeling Neural Latent Dynamics

摘要： 神经种群表现出潜在的动力学结构，这些结构驱动着随时间变化的尖峰活动，促使人们寻找能够捕捉内在网络动力学和外部未观测影响的模型。 在这项工作中，我们引入了LangevinFlow，这是一种顺序变分自编码器，其中潜在变量的时间演化由欠阻尼朗之万方程控制。 我们的方法结合了物理先验——如惯性、阻尼、学习到的势能函数和随机力——以表示神经系统中的自主和非自主过程。 关键的是，势能函数被参数化为一组局部耦合振子，使模型偏向于生物神经种群中观察到的振荡和流动行为。 我们的模型具有一个循环编码器、一个单层Transformer解码器以及潜在空间中的朗之万动力学。 实证上，我们的方法在由洛伦兹吸引子生成的合成神经种群上优于最先进的基线，紧密匹配真实尖峰率。 在神经潜在基准（NLB）上，该模型在四个具有挑战性的数据集上实现了优越的保留神经元似然性（每尖峰比特数）和前向预测准确性。 它在解码行为指标（如手部速度）方面也与或超过其他方法相当。 总体而言，这项工作引入了一个灵活的、受物理启发的高性能框架，用于建模复杂的神经种群动力学及其未观测影响。 显示英文摘要

摘要： Neural populations exhibit latent dynamical structures that drive time-evolving spiking activities, motivating the search for models that capture both intrinsic network dynamics and external unobserved influences. In this work, we introduce LangevinFlow, a sequential Variational Auto-Encoder where the time evolution of latent variables is governed by the underdamped Langevin equation. Our approach incorporates physical priors -- such as inertia, damping, a learned potential function, and stochastic forces -- to represent both autonomous and non-autonomous processes in neural systems. Crucially, the potential function is parameterized as a network of locally coupled oscillators, biasing the model toward oscillatory and flow-like behaviors observed in biological neural populations. Our model features a recurrent encoder, a one-layer Transformer decoder, and Langevin dynamics in the latent space. Empirically, our method outperforms state-of-the-art baselines on synthetic neural populations generated by a Lorenz attractor, closely matching ground-truth firing rates. On the Neural Latents Benchmark (NLB), the model achieves superior held-out neuron likelihoods (bits per spike) and forward prediction accuracy across four challenging datasets. It also matches or surpasses alternative methods in decoding behavioral metrics such as hand velocity. Overall, this work introduces a flexible, physics-inspired, high-performing framework for modeling complex neural population dynamics and their unobserved influences.