Understanding the role of neuronal activity in cognition and behavior is a key question in neuroscience. Previously, in vivo studies have typically inferred behavior from electrophysiological data using probabilistic approaches including Bayesian decoding. While providing useful information on the role of neuronal subcircuits, electrophysiological approaches are often limited in the maximum number of recorded neurons as well as their ability to reliably identify neurons over time. This can be particularly problematic when trying to decode behaviors that rely on large neuronal assemblies or rely on temporal mechanisms, such as a learning task over the course of several days. Calcium imaging of genetically encoded calcium indicators has overcome these two issues. Unfortunately, because calcium transients only indirectly reflect spiking activity and calcium imaging is often performed at lower sampling frequencies, this approach suffers from uncertainty in exact spike timing and thus activity frequency, making rate-based decoding approaches used in electrophysiological recordings difficult to apply to calcium imaging data. Here we describe a probabilistic framework that can be used to robustly infer behavior from calcium imaging recordings and relies on a simplified implementation of a naive Baysian classifier. Our method discriminates between periods of activity and periods of inactivity to compute probability density functions (likelihood and posterior), significance and confidence interval, as well as mutual information. We next devise a simple method to decode behavior using these probability density functions and propose metrics to quantify decoding accuracy. Finally, we show that neuronal activity can be predicted from behavior, and that the accuracy of such reconstructions can guide the understanding of relationships that may exist between behavioral states and neuronal activity.

了解神经元活动在认知和行为中的作用是神经科学的一个关键问题。之前，体内研究通常使用贝叶斯解码等概率方法从电生理数据中推断行为。虽然提供有关神经元子电路作用的有用信息，但电生理学方法通常受到记录神经元最大数量及其随着时间的推移可靠识别神经元的能力的限制。当尝试解码依赖于大型神经元集合或依赖于时间机制的行为（例如持续几天的学习任务）时，这可能尤其成问题。基因编码钙指示剂的钙成像克服了这两个问题。不幸的是，由于钙瞬变仅间接反映尖峰活动，并且钙成像通常以较低的采样频率进行，因此这种方法在精确的尖峰时间和活动频率方面存在不确定性，使得电生理记录中使用的基于速率的解码方法难以应用于钙成像数据。在这里，我们描述了一个概率框架，可用于从钙成像记录中稳健地推断行为，并依赖于朴素贝叶斯分类器的简化实现。我们的方法区分活动时段和不活动时段，以计算概率密度函数（似然和后验）、显着性和置信区间以及互信息。接下来，我们设计一种简单的方法来使用这些概率密度函数来解码行为，并提出量化解码准确性的指标。最后，我们证明神经元活动可以从行为中预测，并且这种重建的准确性可以指导理解行为状态和神经元活动之间可能存在的关系。