Temporal resolution shapes inferred higher-order interactions and information compression in neural population activity: A data-driven approach using minimally complex models

Studying higher order interactions in complex interacting systems based on limited data is inherently challenging. An additional, often overlooked factor is the temporal resolution at which data is presented for analysis. This study examines how temporal resolution influences the emergence of higher-order statistical dependencies and information representation in population level neuronal activity. Using a minimally biased data driven statistical inference framework, Minimally Complex Models, we studied grid-cell populations in the medial entorhinal cortex across a broad range of temporal resolutions. Results demonstrate that at intermediate temporal resolutions (100 milliseconds), the inferred model captures significant higher order dependencies alongside efficient information representations, whereas coarse resolutions lead to poor informative representation. These findings establish temporal resolution as a decisive factor in the inference of higher order neural structure and highlight that resolution-aware modeling is crucial for accurate analysis and characterization of collective dynamics in complex neuronal systems.