Third-order motifs are sufficient to fully and uniquely characterize spatiotemporal neural network activity.

ABSTRACT: Neuroscientific analyses balance between capturing the brain's complexity and expressing that complexity in meaningful and understandable ways. Here we present a novel approach that fully characterizes neural network activity and does so by uniquely transforming raw signals into easily interpretable and biologically relevant metrics of network behavior. We first prove that third-order (triple) correlation describes network activity in its entirety using the triple correlation uniqueness theorem. Triple correlation quantifies the relationships among three events separated by spatial and temporal lags, which are triplet motifs. Classifying these motifs by their event sequencing leads to fourteen qualitatively distinct motif classes that embody well-studied network behaviors including synchro

SUBMITTER: Deshpande SS 

PROVIDER: S-EPMC9816122 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature