More Like this

1. Evolution of Reproductive Behavior

   https://doi.org/10.1534/genetics.119.302263  

   Anholt, Robert R.; O’Grady, Patrick; Wolfner, Mariana F.; Harbison, Susan T. (January 2020, Genetics)
2. Measuring the dimensionality of behavior

   https://doi.org/10.1073/pnas.2205791119  

   Ganguli, Surya (October 2022, Proceedings of the National Academy of Sciences)
3. On the dimensionality of behavior

   https://doi.org/10.1073/pnas.2021860119  

   Bialek, William (May 2022, Proceedings of the National Academy of Sciences)

   There is a growing effort in the “physics of behavior” that aims at complete quantitative characterization of animal movements under more complex, naturalistic conditions. One reaction to the resulting explosion of high-dimensional data is the search for low-dimensional structure. Here I try to define more clearly what we mean by the dimensionality of behavior, where observable behavior may consist of either continuous trajectories or sequences of discrete states. This discussion also serves to isolate situations in which the dimensionality of behavior is effectively infinite. 

   more » « less
4. Nanoscale hygromechanical behavior of lignin

   https://doi.org/10.1007/s10570-018-2045-3  

   Hess, Kristen M.; Killgore, Jason P.; Srubar, Wil V. (November 2018, Cellulose)
5. Unsupervised manifold learning of collective behavior

   https://doi.org/10.1371/journal.pcbi.1007811  

   Titus, Mathew; Hagstrom, George; Watson, James R. (February 2021, PLOS Computational Biology)

   Grilli, Jacopo (Ed.)

   Collective behavior is an emergent property of numerous complex systems, from financial markets to cancer cells to predator-prey ecological systems. Characterizing modes of collective behavior is often done through human observation, training generative models, or other supervised learning techniques. Each of these cases requires knowledge of and a method for characterizing the macro-state(s) of the system. This presents a challenge for studying novel systems where there may be little prior knowledge. Here, we present a new unsupervised method of detecting emergent behavior in complex systems, and discerning between distinct collective behaviors. We require only metrics, d (1) , d (2) , defined on the set of agents, X , which measure agents’ nearness in variables of interest. We apply the method of diffusion maps to the systems ( X , d ( i ) ) to recover efficient embeddings of their interaction networks. Comparing these geometries, we formulate a measure of similarity between two networks, called the map alignment statistic (MAS). A large MAS is evidence that the two networks are codetermined in some fashion, indicating an emergent relationship between the metrics d (1) and d (2) . Additionally, the form of the macro-scale organization is encoded in the covariances among the two sets of diffusion map components. Using these covariances we discern between different modes of collective behavior in a data-driven, unsupervised manner. This method is demonstrated on a synthetic flocking model as well as empirical fish schooling data. We show that our state classification subdivides the known behaviors of the school in a meaningful manner, leading to a finer description of the system’s behavior. 

   more » « less