A framework to identify structured behavioral patterns within rodent spatial trajectories
نویسندگان
چکیده
Abstract Animal behavior is highly structured. Yet, structured behavioral patterns—or “statistical ethograms”—are not immediately apparent from the full spatiotemporal data that scientists usually collect. Here, we introduce a framework to quantitatively characterize rodent during spatial (e.g., maze) navigation, in terms of movement building blocks or motor primitives . The hypothesis pursue characterized by small number primitives, which are combined over time produce open-ended movements. We assume be organized two sparsity principles: each controlled using limited subset (sparse superposition) and primitive active only for time-limited, time-contiguous portions movements activity). formalize this sparse dictionary learning method, use extract position velocity collected successively reconstruct past trajectories predict novel ones. Three main results validate our approach. First, robustly reconstructed incomplete data, performing better than approaches based on standard dimensionality reduction methods, such as principal component analysis, single sparsity. Second, extracted one experimental session generalize afford accurate reconstruction across successive sessions same modified mazes. Third, approach associated with maze correlates independent measures complexity, hence showing formalism sensitive essential aspects task structure. introduced here can used neuroscientists an aid neural analysis. Indeed, enable quantitative characterization complexity similarity between different mazes patterns multiple trials (i.e., habit formation). provide example uses computational framework, how it identify behavioural effects analyze stereotyped behavior, classify choices place grid cell displacement environments.
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ژورنال
عنوان ژورنال: Scientific Reports
سال: 2021
ISSN: ['2045-2322']
DOI: https://doi.org/10.1038/s41598-020-79744-7