Understanding how neurons coordinate their activity is a fundamental question in neuroscience, with implications for learning, memory, and neurological disorders. Calcium imaging has emerged as a powerful method to observe large-scale neuronal activity in freely moving animals, providing time-resolved recordings of hundreds of neurons. However, fluorescence signals are noisy and only indirectly reflect underlying spikes of neuronal activity, complicating the extraction of reliable patterns of neuronal coordination. We introduce a fully Bayesian, semiparametric model that jointly infers spiking activity and identifies functionally coherent neuronal ensembles from calcium traces. Our approach models each neuron's spiking probability through a latent Gaussian process and encourages anatomically coherent clustering using a location-dependent stick-breaking prior. A spike-and-slab Dirichlet process captures heterogeneity in spike amplitudes while filtering out negligible events. We consider calcium imaging data from the hippocampal CA1 region of a mouse as it navigates a circular arena, a setting critical for understanding spatial memory and neuronal representation of environments. Our model uncovers spatially structured co-activation patterns among neurons and can be employed to reveal how ensemble structures vary with the animal's position.

翻译：理解神经元如何协调其活动是神经科学中的一个基本问题，对学习、记忆和神经系统疾病的研究具有重要意义。钙成像已成为观察自由活动动物大规模神经元活动的一种强大方法，可提供数百个神经元的时间分辨记录。然而，荧光信号存在噪声，且仅间接反映神经元活动的潜在放电事件，这使得提取可靠的神经元协调模式变得复杂。我们提出了一种全贝叶斯半参数模型，能够从钙信号中联合推断放电活动并识别功能一致的神经元集群。我们的方法通过一个潜在高斯过程对每个神经元的放电概率进行建模，并利用位置依赖的断棒先验促进解剖学上一致的聚类。一个钉板狄利克雷过程用于捕捉放电幅度的异质性，同时滤除可忽略的事件。我们分析了一只小鼠在圆形竞技场中导航时海马CA1区的钙成像数据，该场景对于理解空间记忆和环境的神经元表征至关重要。我们的模型揭示了神经元之间具有空间结构的共激活模式，并可用于揭示集群结构如何随动物位置而变化。