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Luisa Ramirez

Publications and source records attributed to Luisa Ramirez.

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Range residency determines how movement persistence shapes encounter rates

Encounters between individuals link movement behavior to population-level processes such as predation and disease transmission. For many animal species, movement can be modeled as a multiscale stochastic process, dominated by directional persistence at short time scales and range residency at long time scales. Their separate effects on encounters are well understood: range residency can raise or lower encounter rates depending on home-range overlap, whereas moving with higher directional persistence systematically increases them. However, how directional persistence and range residency jointly determine encounters remains unknown. We present an analytical encounter theory for movement models that combine both features. In this framework, we derive a threshold in home-range overlap above which directional persistence diminishes, rather than enhances, encounters. At shorter time scales, attraction toward the home-range center displaces individual locations, altering encounter rates even when range residency is not measurable in movement tracks. Movement models fitted to short tracks can therefore describe trajectories accurately yet under- or overestimate the encounters derived from those trajectories, depending on home range spatial configuration. Encounter rates are a more demanding target for inference than movement parameters themselves.

q-bio.PE

Probabilistic models, compressible interactions, and neural coding

In physics we often use very simple models to describe systems with many degrees of freedom, but it is not clear why or how this success can be transferred to the more complex biological context. We consider models for the joint distribution of many variables, as with the combinations of spiking and silence in large networks of neurons. In this probabilistic framework, we argue that simple models are possible if the mutual information between two halves of the system is consistently sub--extensive, and if this shared information is compressible. These conditions are not met generically, but they are met by real world data such as natural images and the activity in a population of retinal output neurons. We introduce compression strategies that combine the information bottleneck with an iteration scheme inspired by the renormalization group, and find that the number of parameters needed to describe the distribution of joint activity scales with the square of the number of neurons, even though the interactions are not well approximated as pairwise. Our results also show that this shared information is essentially equal to the information that individual neurons carry about natural visual inputs, which has surprising implications for the neural code.

q-bio.NC