@article{13127, abstract = {Cooperative disease defense emerges as group-level collective behavior, yet how group members make the underlying individual decisions is poorly understood. Using garden ants and fungal pathogens as an experimental model, we derive the rules governing individual ant grooming choices and show how they produce colony-level hygiene. Time-resolved behavioral analysis, pathogen quantification, and probabilistic modeling reveal that ants increase grooming and preferentially target highly-infectious individuals when perceiving high pathogen load, but transiently suppress grooming after having been groomed by nestmates. Ants thus react to both, the infectivity of others and the social feedback they receive on their own contagiousness. While inferred solely from momentary ant decisions, these behavioral rules quantitatively predict hour-long experimental dynamics, and synergistically combine into efficient colony-wide pathogen removal. Our analyses show that noisy individual decisions based on only local, incomplete, yet dynamically-updated information on pathogen threat and social feedback can lead to potent collective disease defense.}, author = {Casillas Perez, Barbara E and Bod'Ová, Katarína and Grasse, Anna V and Tkačik, Gašper and Cremer, Sylvia}, issn = {2041-1723}, journal = {Nature Communications}, publisher = {Springer Nature}, title = {{Dynamic pathogen detection and social feedback shape collective hygiene in ants}}, doi = {10.1038/s41467-023-38947-y}, volume = {14}, year = {2023}, } @article{406, abstract = {Recent developments in automated tracking allow uninterrupted, high-resolution recording of animal trajectories, sometimes coupled with the identification of stereotyped changes of body pose or other behaviors of interest. Analysis and interpretation of such data represents a challenge: the timing of animal behaviors may be stochastic and modulated by kinematic variables, by the interaction with the environment or with the conspecifics within the animal group, and dependent on internal cognitive or behavioral state of the individual. Existing models for collective motion typically fail to incorporate the discrete, stochastic, and internal-state-dependent aspects of behavior, while models focusing on individual animal behavior typically ignore the spatial aspects of the problem. Here we propose a probabilistic modeling framework to address this gap. Each animal can switch stochastically between different behavioral states, with each state resulting in a possibly different law of motion through space. Switching rates for behavioral transitions can depend in a very general way, which we seek to identify from data, on the effects of the environment as well as the interaction between the animals. We represent the switching dynamics as a Generalized Linear Model and show that: (i) forward simulation of multiple interacting animals is possible using a variant of the Gillespie’s Stochastic Simulation Algorithm; (ii) formulated properly, the maximum likelihood inference of switching rate functions is tractably solvable by gradient descent; (iii) model selection can be used to identify factors that modulate behavioral state switching and to appropriately adjust model complexity to data. To illustrate our framework, we apply it to two synthetic models of animal motion and to real zebrafish tracking data. }, author = {Bod’Ová, Katarína and Mitchell, Gabriel and Harpaz, Roy and Schneidman, Elad and Tkacik, Gasper}, journal = {PLoS One}, number = {3}, publisher = {Public Library of Science}, title = {{Probabilistic models of individual and collective animal behavior}}, doi = {10.1371/journal.pone.0193049}, volume = {13}, year = {2018}, } @article{720, abstract = {Advances in multi-unit recordings pave the way for statistical modeling of activity patterns in large neural populations. Recent studies have shown that the summed activity of all neurons strongly shapes the population response. A separate recent finding has been that neural populations also exhibit criticality, an anomalously large dynamic range for the probabilities of different population activity patterns. Motivated by these two observations, we introduce a class of probabilistic models which takes into account the prior knowledge that the neural population could be globally coupled and close to critical. These models consist of an energy function which parametrizes interactions between small groups of neurons, and an arbitrary positive, strictly increasing, and twice differentiable function which maps the energy of a population pattern to its probability. We show that: 1) augmenting a pairwise Ising model with a nonlinearity yields an accurate description of the activity of retinal ganglion cells which outperforms previous models based on the summed activity of neurons; 2) prior knowledge that the population is critical translates to prior expectations about the shape of the nonlinearity; 3) the nonlinearity admits an interpretation in terms of a continuous latent variable globally coupling the system whose distribution we can infer from data. Our method is independent of the underlying system’s state space; hence, it can be applied to other systems such as natural scenes or amino acid sequences of proteins which are also known to exhibit criticality.}, author = {Humplik, Jan and Tkacik, Gasper}, issn = {1553734X}, journal = {PLoS Computational Biology}, number = {9}, publisher = {Public Library of Science}, title = {{Probabilistic models for neural populations that naturally capture global coupling and criticality}}, doi = {10.1371/journal.pcbi.1005763}, volume = {13}, year = {2017}, } @article{1021, abstract = {Most flows in nature and engineering are turbulent because of their large velocities and spatial scales. Laboratory experiments on rotating quasi-Keplerian flows, for which the angular velocity decreases radially but the angular momentum increases, are however laminar at Reynolds numbers exceeding one million. This is in apparent contradiction to direct numerical simulations showing that in these experiments turbulence transition is triggered by the axial boundaries. We here show numerically that as the Reynolds number increases, turbulence becomes progressively confined to the boundary layers and the flow in the bulk fully relaminarizes. Our findings support that turbulence is unlikely to occur in isothermal constant-density quasi-Keplerian flows.}, author = {Lopez Alonso, Jose M and Avila, Marc}, issn = {00221120}, journal = {Journal of Fluid Mechanics}, pages = {21 -- 34}, publisher = {Cambridge University Press}, title = {{Boundary layer turbulence in experiments on quasi Keplerian flows}}, doi = {10.1017/jfm.2017.109}, volume = {817}, year = {2017}, } @article{1420, abstract = {Selection, mutation, and random drift affect the dynamics of allele frequencies and consequently of quantitative traits. While the macroscopic dynamics of quantitative traits can be measured, the underlying allele frequencies are typically unobserved. Can we understand how the macroscopic observables evolve without following these microscopic processes? This problem has been studied previously by analogy with statistical mechanics: the allele frequency distribution at each time point is approximated by the stationary form, which maximizes entropy. We explore the limitations of this method when mutation is small (4Nμ < 1) so that populations are typically close to fixation, and we extend the theory in this regime to account for changes in mutation strength. We consider a single diallelic locus either under directional selection or with overdominance and then generalize to multiple unlinked biallelic loci with unequal effects. We find that the maximum-entropy approximation is remarkably accurate, even when mutation and selection change rapidly. }, author = {Bod'ová, Katarína and Tkacik, Gasper and Barton, Nicholas H}, journal = {Genetics}, number = {4}, pages = {1523 -- 1548}, publisher = {Genetics Society of America}, title = {{A general approximation for the dynamics of quantitative traits}}, doi = {10.1534/genetics.115.184127}, volume = {202}, year = {2016}, }