Identification of Pattern Completion Neurons in Neuronal Ensembles Using Probabilistic Graphical Models

被引:11
|
作者
Carrillo-Reid, Luis [1 ,3 ]
Han, Shuting [1 ]
O'Neil, Darik [1 ]
Taralova, Ekaterina [1 ,2 ]
Jebara, Tony [2 ]
Yuste, Rafael [1 ]
机构
[1] Columbia Univ, Dept Biol Sci, New York, NY 10027 USA
[2] Columbia Univ, Dept Comp Sci, New York, NY 10027 USA
[3] Univ Nacl Autonoma Mexico, Neurobiol Inst, Juriquilla 76230, Queretaro, Mexico
来源
JOURNAL OF NEUROSCIENCE | 2021年 / 41卷 / 41期
基金
美国国家科学基金会;
关键词
Conditional random fields; graph theory; neuronal ensembles; pattern completion; probabilistic graphical models; two-photon optogenetics; FUNCTIONAL CONNECTIVITY; NEURAL-NETWORKS; SMALL-WORLD; ORGANIZATION; DYNAMICS; CHAINS; MEMORY; STATES;
D O I
10.1523/JNEUROSCI.0051-21.2021
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
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.
引用
收藏
页码:8577 / 8588
页数:12
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