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

Studying limited datasets derived from complex interacting systems, such as neural population activity, represents an important and challenging task. The level of detail in recorded activities is highly influenced by the temporal resolution at which the activities are studied. However, does more detail always lead to a correct assessment of the system? This study examines how temporal resolution influences the inference 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 based on 23 grid cell recordings including different animals and behavioral states show that at an intermediate temporal resolution (𝒪(102) ms), the inferred model captures significant higher order dependencies alongside efficient information representations, whereas the finest and the coarsest temporal resolutions lead to poor information representation. The informative temporal resolution can be identified by two criteria: the bin size at which the inferred interaction structure is most consistent across population sizes, and the peak of the model's information representations, which agree closely across recordings. These findings indicate that temporal resolution is a critical 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.