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

• Published in: The Journal of neuroscience 2021-10, Vol.41 (41), p.8577-8588
• Main Authors: Carrillo-Reid, Luis, Han, Shuting, O'Neil, Darik, Taralova, Ekaterina, Jebara, Tony, Yuste, Rafael
• Format: Article
• Language: English
• Subjects: Animals; Brain; Calcium; Calcium imaging; Circuits; Cognitive ability; Conditional random fields; Cortex (somatosensory); Genetics; Graph theory; Identification methods; Imaging; In vivo methods and tests; Information processing; Male; Mice; Mice, Inbred C57BL; Microscopy, Fluorescence, Multiphoton - methods; Models, Neurological; Neural networks; Neuroimaging; Neurons; Neurons - chemistry; Neurons - physiology; Optics; Optogenetics - methods; Pattern Recognition, Visual - physiology; Photic Stimulation - methods; Photons; Probability; Prostheses; Prosthetics; Therapeutic applications; Visual cortex; Visual Cortex - chemistry; Visual Cortex - cytology; Visual Cortex - physiology; Visual pathways; Visual stimuli
• ISSN: 0270-6474; 1529-2401
• DOI: 10.1523/jneurosci.0051-21.2021

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 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 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. We describe a graph theory method to identify and optically manipulate neurons with pattern completion capability in mouse cortical circuits. Using calcium imaging and two-photon optogenetics we confirm that key neurons identified by this method can recall entire neuronal ensembles. This method could be broadly applied to manipulate neuronal ensemble activity to trigger behavior or for therapeutic applications in brain prostheses.