Neural encoding, or neural representation, is a field in neuroscience that focuses on characterizing how information is encoded in the spiking activity of neurons. Currently, little is known about how sensory neurons can preserve information from multiple stimuli given their broad receptive fields. Multiplexing is a neural encoding theory that posits that neurons temporally switch between encoding various stimuli in their receptive field. Here, we construct a statistically falsifiable single-neuron model for multiplexing using a competition-based framework. The spike train models are constructed using drift-diffusion models, implying an integrate-and-fire framework to model the temporal dynamics of the membrane potential of the neuron. In addition to a multiplexing-specific model, we develop alternative models that represent alternative encoding theories (normalization, winner-take-all, subadditivity, etc.) with some level of abstraction. Using information criteria, we perform model comparison to determine whether the data favor multiplexing over alternative theories of neural encoding. Analysis of spike trains from the inferior colliculus of two macaque monkeys provides tenable evidence of multiplexing and offers new insight into the timescales at which switching occurs.

翻译：神经编码，或称神经表征，是神经科学中专注于刻画信息如何在神经元放电活动中编码的领域。目前，关于感觉神经元如何在具有广泛感受野的情况下保持来自多个刺激的信息，人们知之甚少。多路复用是一种神经编码理论，它假设神经元在其感受野内通过时间切换来编码不同刺激。本文中，我们基于竞争框架构建了一个统计上可证伪的单神经元多路复用模型。放电序列模型采用漂移扩散模型构建，这意味着使用整合发放框架来模拟神经元膜电位的时间动态。除了专门针对多路复用的模型外，我们还开发了代表其他编码理论（归一化、赢者通吃、次可加性等）的替代模型，并保持了一定的抽象层次。通过信息准则，我们进行了模型比较，以确定数据是否支持多路复用而非其他神经编码理论。对两只猕猴下丘放电序列的分析为多路复用提供了可靠的证据，并为切换发生的时间尺度提供了新的见解。