Developing computational models of neural response is crucial for understanding sensory processing and neural computations. Current state-of-the-art neural network methods use temporal filters to handle temporal dependencies, resulting in an unrealistic and inflexible processing paradigm. Meanwhile, these methods target trial-averaged firing rates and fail to capture important features in spike trains. This work presents the temporal conditioning spiking latent variable models (TeCoS-LVM) to simulate the neural response to natural visual stimuli. We use spiking neurons to produce spike outputs that directly match the recorded trains. This approach helps to avoid losing information embedded in the original spike trains. We exclude the temporal dimension from the model parameter space and introduce a temporal conditioning operation to allow the model to adaptively explore and exploit temporal dependencies in stimuli sequences in a {\it natural paradigm}. We show that TeCoS-LVM models can produce more realistic spike activities and accurately fit spike statistics than powerful alternatives. Additionally, learned TeCoS-LVM models can generalize well to longer time scales. Overall, while remaining computationally tractable, our model effectively captures key features of neural coding systems. It thus provides a useful tool for building accurate predictive computational accounts for various sensory perception circuits.

翻译：开发神经响应的计算模型对于理解感觉处理和神经计算至关重要。当前最先进的神经网络方法使用时间滤波器来处理时间依赖性，导致不现实且不灵活的处理范式。同时，这些方法以试验平均的发放率为目标，未能捕捉脉冲序列中的重要特征。本文提出时间条件化脉冲潜变量模型（TeCoS-LVM）来模拟对自然视觉刺激的神经响应。我们使用脉冲神经元产生直接匹配记录序列的脉冲输出，这有助于避免丢失嵌入在原始脉冲序列中的信息。我们将时间维度从模型参数空间中排除，并引入时间条件化操作，使模型能够以自然范式自适应地探索和利用刺激序列中的时间依赖性。我们表明，与强大的替代方法相比，TeCoS-LVM模型能够产生更逼真的脉冲活动，并准确拟合脉冲统计数据。此外，学习后的TeCoS-LVM模型能够很好地泛化到更长的时间尺度。总体而言，在保持计算可行性的同时，我们的模型有效捕捉了神经编码系统的关键特征，从而为构建各种感知回路的精确预测计算模型提供了有用工具。