Using a Neuroscience Approach to Uncover Patterns of Collective Behavior in Pulsing Corals

Meeting Abstract

9-8  Thursday, Jan. 5 09:45 – 10:00  Using a Neuroscience Approach to Uncover Patterns of Collective Behavior in Pulsing Corals SAMSON, J. E.*; MILLER, L. A.; UNC Chapel Hill; UNC Chapel Hill

Xeniid corals, a family of soft corals, display a unique behavior: individual polyps within a colony actively pulse, increasing the local water flux and thus mass transfer (i.e. nutrient and gas exchange). From observations in the lab and in the field, it seems that this individual pulsing behavior generates collective pulsing patterns on the colony scale. Since cnidarians (corals, jellyfish, anemones, and their relatives) lack a centralized nervous system or integration center, it is unclear how collective behavior arises within a colony. In this study, we examined whether recurring pulsing patterns could be observed and quantified within a small colony (i.e. whether the colony functions as a predictable network of polyps). Using a neuroscience approach to analyze our video data, we found repeated pulsing patterns when looking at four neighboring polyps within a colony. Several hypotheses can be proposed to explain the observed pulsing patterns: 1) the collective behavior is the result of random individual behavior, 2) the collective behavior follows a Markov model, 3) the individual polyps act as independent oscillators with set intrinsic pulsing frequencies, and 4) the individual polyps act as coupled oscillators. For each hypothesis, we built a model that we compared to the collected data. We had to reject all hypotheses but the fourth, meaning pulsing behavior in small coral colonies can be modeled using coupled oscillators, although it is still unclear what the coupling consists of (neural, chemical, external, etc.). Future research will delve further into this coupling mode. Additionally, We are investigating the potential benefits of this collective pulsing behavior on nutrient and gas exchange rates using computational fluid dynamic models.

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