Classification of Spatiotemporal Neural Activity Patterns in Brain Imaging Data.

ABSTRACT: Various patterns of neural activity are observed in dynamic cortical imaging data. Such patterns may reflect how neurons communicate using the underlying circuitry to perform appropriate functions; thus it is crucial to investigate the spatiotemporal characteristics of the observed neural activity patterns. In general, however, neural activities are highly nonlinear and complex, so it is a demanding job to analyze them quantitatively or to classify the patterns of observed activities in various types of imaging data. Here, we present our implementation of a novel method that successfully addresses the above issues for precise comparison and classification of neural activity patterns. Based on two-dimensional representations of the geometric structure and temporal evolution of activity patt

SUBMITTER: Song M 

PROVIDER: S-EPMC5974089 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature