蝾螈视网膜神经节细胞动态感受野的结构化贝叶斯建模

中文摘要 AI 辅助

视觉系统中的神经元对特定的时空刺激特征具有选择性，这些特征由其感受野描述。从少量试验中估计感受野意味着每个像素在每个时间步需要一个系数——这是一个需要正则化的高维问题。LASSO等稀疏正则化器能够处理维度问题，但在每个时间点独立选择像素，没有任何机制保持区域在空间上的连贯性或时间上的平滑性；即使真实响应平滑演化，它也可能不连续地碎片化或重组，而这一演化模式正是感受野估计应捕获的，因此这是一种失败。我们将动态感受野估计表述为一个高维贝叶斯问题：一个泊松模型，在空间上结合高斯马尔可夫随机场，在时间上结合自回归过程，从而使估计的场在空间和时间上平滑且连贯。在对155个蝾螈视网膜神经节细胞的记录中，对每个神经元独立拟合该模型可恢复出连贯的表面，而像素级泊松LASSO比较则返回碎片化的表面。通过空间平均的时间响应总结每个神经元的表面，并使用基于模型的函数聚类程序对这些曲线进行聚类，BIC选择了三种平衡的时间响应表型（85、32、38个神经元），而聚类原始表面则产生退化分组。一项已知真实值的模拟研究证实了相同的模式，该模型在恢复和估计精度上优于未正则化的泊松GLM、LASSO和弹性网，尽管LASSO在控制假阳性方面更好。逐神经元的场识别、与LASSO的对比以及函数聚类群体分型构成了本文的贡献。

英文摘要

Neurons in the visual system are selective for specific spatial and temporal stimulus features, described by their \emph{receptive field}. Estimating one means a coefficient per pixel per time bin from few trials -- a high-dimensional problem requiring regularization. Sparse regularizers such as the LASSO handle the dimension but select pixels independently at each time point, with nothing to keep the region coherent in space or smooth in time; it can fragment or reorganize discontinuously even when the true response evolves smoothly, a failure since this evolving pattern is what a receptive-field estimate should capture. We formulate dynamic receptive-field estimation as a high-dimensional Bayesian problem: a Poisson model combining a Gaussian Markov random field in space with an autoregressive process in time, so the estimated field is smooth and coherent across space and time. On recordings from $155$ salamander retinal ganglion cells, fitting this model independently per neuron recovers a coherent surface, where a pixel-level Poisson-LASSO comparison instead returns a fragmented one. Summarizing each neuron's surface by its space-averaged temporal response and clustering these curves with a model-based functional-clustering procedure, BIC selects three balanced temporal-response phenotypes ($85$, $32$, $38$ neurons), against a degenerate grouping from clustering the raw surfaces. A simulation study with known ground truth confirms the same pattern, with the model beating an unregularized Poisson GLM, LASSO, and the elastic net on recovery and estimation accuracy, though LASSO controls false positives better. The per-neuron field identification, its contrast with LASSO, and the functional-clustering population typing constitute this paper's contribution.

发表机构

• Texas A&M University（德克萨斯农工大学）

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