Identification of Pattern Completion Neurons in Neuronal Ensembles Using Probabilistic Graphical Models
Identification of Pattern Completion Neurons in Neuronal Ensembles Using Probabilistic Graphical Models
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DOI：
10.1523/jneurosci.0051-21.2021
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发表时间：
2021-10-13
影响因子：
5.3
通讯作者：
Yuste, Rafael
中科院分区：
文献类型：
--
作者：
Carrillo-Reid, Luis;Han, Shuting;Yuste, Rafael
神经元集合是具有协调活动的神经元组，可以代表感觉、运动或认知状态。关于神经元集合如何构建、回忆和参与复杂行为指导的研究一直受到缺乏实验和分析工具的限制，这些工具无法可靠地识别和操纵有能力激活整个集合的神经元。这种模式完成神经元也被提出作为人工和生物神经网络的关键元素。事实上，越来越多的证据表明，针对模式完成神经元可以激活神经元集合并触发行为，这突出了模式完成神经元的相关性。作为一种可靠地检测模式完成神经元的方法，我们使用条件随机场（CRF），一种概率图模型。我们应用CRF在实验中使用来自雄性小鼠的初级视觉皮层的体内双光子钙成像来识别集合中的模式完成神经元，并用双光子光遗传学证实CRF预测。为了测试CRF的更广泛适用性，我们还分析了公开可用的钙成像数据（艾伦研究所脑天文台数据集），并证明CRF可以可靠地识别预测视觉刺激特定特征的神经元。最后，为了探索CRF的可扩展性，我们将其应用于计算机网络模拟，并表明CRF识别的模式完成神经元增强了功能连接性。这些结果表明，CRF的特点和选择性地操纵神经回路的潜力。
Neuronal ensembles are groups of neurons with coordinated activity that could represent sensory, motor, or cognitive states. The study of how neuronal ensembles are built, recalled, and involved in the guiding of complex behaviors has been limited by the lack of experimental and analytical tools to reliably identify and manipulate neurons that have the ability to activate entire ensembles. Such pattern completion neurons have also been proposed as key elements of artificial and biological neural networks. Indeed, the relevance of pattern completion neurons is highlighted by growing evidence that targeting them can activate neuronal ensembles and trigger behavior. As a method to reliably detect pattern completion neurons, we use conditional random fields (CRFs), a type of probabilistic graphical model. We apply CRFs to identify pattern completion neurons in ensembles in experiments using in vivo two-photon calcium imaging from primary visual cortex of male mice and confirm the CRFs predictions with two-photon optogenetics. To test the broader applicability of CRFs we also analyze publicly available calcium imaging data (Allen Institute Brain Observatory dataset) and demonstrate that CRFs can reliably identify neurons that predict specific features of visual stimuli. Finally, to explore the scalability of CRFs we apply them to in silico network simulations and show that CRFs-identified pattern completion neurons have increased functional connectivity. These results demonstrate the potential of CRFs to characterize and selectively manipulate neural circuits.