标题： 基于漂移-扩散模型的神经开关建模 显示英文标题

标题： Modeling Neural Switching via Drift-Diffusion Models

摘要： 神经编码是神经科学中的一个领域，专注于描述刺激信息如何被编码到神经元的脉冲活动中。 当存在多个刺激时，一种称为复用的理论认为，神经元会在编码不同刺激之间时间上切换，从而产生波动的放电模式。 在这里，我们提出了一种新的统计框架来分析速率波动，并判断神经元是否通过复用的方式对多个刺激进行编码。 我们采用机械论的方法来建模复用，构建了一个非马尔可夫内源性状态空间模型。 具体来说，我们认为复用源于刺激之间的竞争，这些刺激被建模为潜在的漂移-扩散过程。 我们提出了一个新的MCMC算法，用于对类似类型的状态空间模型进行后验推断，在这种情况下，由于参数之间的强依赖关系，典型的MCMC方法会失败。 此外，我们开发了替代模型，代表了一类广泛的替代编码理论，并使用WAIC进行模型比较，以确定数据是否表明复用的发生超过神经编码的替代理论。 利用所提出的框架，我们提供了下丘内复用的证据以及关于切换动态的新见解。 显示英文摘要

摘要： Neural encoding is a field in neuroscience that focuses on characterizing how information from stimuli is encoded in the spiking activity of neurons. When more than one stimulus is present, a theory known as multiplexing posits that neurons temporally switch between encoding various stimuli, creating a fluctuating firing pattern. Here, we propose a new statistical framework to analyze rate fluctuations and discern whether neurons employ multiplexing as a means of encoding multiple stimuli. We adopt a mechanistic approach to modeling multiplexing by constructing a non-Markovian endogenous state-space model. Specifically, we posit that multiplexing arises from competition between the stimuli, which are modeled as latent drift-diffusion processes. We propose a new MCMC algorithm for conducting posterior inference on similar types of state-space models, where typical state-space MCMC methods fail due to strong dependence between the parameters. In addition, we develop alternative models that represent a wide class of alternative encoding theories and perform model comparison using WAIC to determine whether the data suggest the occurrence multiplexing over alternative theories of neural encoding. Using the proposed framework, we provide evidence of multiplexing within the inferior colliculus and novel insight into the switching dynamics.