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

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.SIGNIFICANCE STATEMENT 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 in vivo 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.

Comprehensive machine learning analysis of Hydra behavior reveals a stable basal behavioral repertoire.

Animal behavior has been studied for centuries, but few efficient methods are available to automatically identify and classify it. Quantitative behavioral studies have been hindered by the subjective and imprecise nature of human observation, and the slow speed of annotating behavioral data. Here, we developed an automatic behavior analysis pipeline for the cnidarian Hydra vulgaris using machine learning. We imaged freely behaving Hydra, extracted motion and shape features from the videos, and constructed a dictionary of visual features to classify pre-defined behaviors. We also identified unannotated behaviors with unsupervised methods. Using this analysis pipeline, we quantified 6 basic behaviors and found surprisingly similar behavior statistics across animals within the same species, regardless of experimental conditions. Our analysis indicates that the fundamental behavioral repertoire of Hydra is stable. This robustness could reflect a homeostatic neural control of "housekeeping" behaviors which could have been already present in the earliest nervous systems.

Modelling and visualizing morphology in the fungus Alternaria.

Alternaria is one of the most cosmopolitan fungal genera encountered and impacts humans and human activities in areas of material degradation, phytopathology, food toxicology, and respiratory disease. Contemporary methods of taxon identification rely on assessments of morphology related to sporulation, which are critical for accurate diagnostics. However, the morphology of Alternaria is quite complex, and precise characterization can be laborious, time-consuming, and often restricted to experts in this field. To make morphology characterization easier and more broadly accessible, a generalized statistical model was developed for the three-dimensional geometric structure of the sporulation apparatus. The model is inspired by the widely used grammar-based models for plants, Lindenmayer-systems, which build structure by repeated application of rules for growth. Adjusting the parameters of the underlying probability distributions yields variations in the morphology, and thus the approach provides an excellent tool for exploring the morphology of Alternaria under different assumptions, as well as understanding how it is largely the consequence of local rules for growth. Further, different choices of parameters lead to different model groups, which can then be visually compared to published descriptions or microscopy images to validate parameters for species-specific models. The approach supports automated analysis, as the models can be fit to image data using statistical inference, and the explicit representation of the geometry allows the accurate computation of any morphological quantity. Furthermore, because the model can encode the statistical variation of geometric parameters for different species, it will allow automated species identification from microscopy images using statistical inference. In summary, the approach supports visualization of morphology, automated quantification of phenotype structure, and identification based on form.