Local interaction rules and collective motion in black neon tetra (Hyphessobrycon herbertaxelrodi) and zebrafish (Danio rerio).

2019 ◽  
Vol 133 (2) ◽  
pp. 143-155 ◽  
Author(s):  
Vicenç Quera ◽  
Elisabet Gimeno ◽  
Francesc S. Beltran ◽  
Ruth Dolado
Keyword(s):  
Danio Rerio ◽  
Collective Motion ◽  
Local Interaction

scholarly journals Deep attention networks reveal the rules of collective motion in zebrafish

2018 ◽  
Author(s):  
Francisco J.H. Heras ◽  
Francisco Romero-Ferrero ◽  
Robert C. Hinz ◽  
Gonzalo G. de Polavieja
Keyword(s):  
Danio Rerio ◽  
Collective Behavior ◽  
Collective Motion ◽  
Weighted Average ◽  
Data Driven ◽  
List Type ◽  
Attention Networks ◽  
Interaction Information ◽  
Large Groups ◽  
Simple Models

AbstractA variety of simple models has been proposed to understand the collective motion of animals. These models can be insightful but lack important elements necessary to predict the motion of each individual in the collective. Adding more detail increases predictability but can make models too complex to be insightful. Here we report that deep attention networks can obtain in a data-driven way a model of collective behavior that is simultaneously predictive and insightful thanks to an organization in modules. The model obtains that interactions between two zebrafish, Danio rerio, in a large groups of 60-100, can be approximately be described as repulsive, attractive or as alignment, but only when moving slowly. At high velocities, interactions correspond only to alignment or alignment mixed with repulsion at close distances. The model also shows that each zebrafish decides where to move by aggregating information from the group as a weighted average over neighbours. Weights are higher for neighbours that are close, in a collision path or moving faster in frontal and lateral locations. These weights effectively select 5 relevant neighbours on average, but this number is dynamical, changing between a single neighbour to up to 12, often in less than a second. Our results suggest that each animal in a group decides by dynamically selecting information from the group.HighlightsAt 30 days postfertilization, zebrafish, Danio rerio, can move in very cohesive and predictable large groupsDeep attention networks obtain a predictive and understadable model of collective motionWhen moving slowly, interations between pairs of zebrafish have clear components of repulsion, attraction and alignmentWhen moving fast, interactions correspond to alignment and a mixture of alignment and repulsion at close distancesZebrafish turn left or right depending on a weighted average of interaction information with other fish, with weights higher for close fish, those in a collision path or those moving fast in front or to the sidesAggregation is dynamical, oscillating between 1 and 12 neighbouring fish, with 5 on average

scholarly journals From Schooling to Shoaling: Patterns of Collective Motion in Zebrafish (Danio rerio)

PLoS ONE ◽  
2012 ◽  
Vol 7 (11) ◽  
pp. e48865 ◽  
Author(s):  
Noam Miller ◽  
Robert Gerlai
Keyword(s):  
Danio Rerio ◽  
Collective Motion

scholarly journals Individual variation in local interaction rules can explain emergent patterns of spatial organization in wild baboons

2017 ◽  
Vol 284 (1853) ◽  
pp. 20162243 ◽  
Author(s):  
D. R. Farine ◽  
A. Strandburg-Peshkin ◽  
I. D. Couzin ◽  
T. Y. Berger-Wolf ◽  
M. C. Crofoot
Keyword(s):  
Individual Variation ◽  
Spatial Organization ◽  
Collective Motion ◽  
Theoretical Models ◽  
Local Interaction ◽  
Gps Tracking ◽  
Neighbourhood Size ◽  
Group Members ◽  
Global Patterns ◽  
Spatial Positioning

Researchers have long noted that individuals occupy consistent spatial positions within animal groups. However, an individual's position depends not only on its own behaviour, but also on the behaviour of others. Theoretical models of collective motion suggest that global patterns of spatial assortment can arise from individual variation in local interaction rules. However, this prediction remains untested. Using high-resolution GPS tracking of members of a wild baboon troop, we identify consistent inter-individual differences in within-group spatial positioning. We then apply an algorithm that identifies what number of conspecific group members best predicts the future location of each individual (we call this the individual's neighbourhood size ) while the troop is moving. We find clear variation in the most predictive neighbourhood size, and this variation relates to individuals' propensity to be found near the centre of their group. Using simulations, we show that having different neighbourhood sizes is a simple candidate mechanism capable of linking variation in local individual interaction rules—in this case how many conspecifics an individual interacts with—to global patterns of spatial organization, consistent with the patterns we observe in wild primates and a range of other organisms.

RELAXATION AND COLLECTIVE MOTION IN SUPERCONDUCTORS. A TWO-FLUID DESCRIPTION

1978 ◽  
Vol 39 (C6) ◽  
pp. C6-488-C6-489 ◽  
Author(s):  
C. J. Pethick ◽  
H. Smith
Keyword(s):  
Collective Motion ◽  
Two Fluid

DAMPING OF COLLECTIVE MOTION, MEMORY EFFECTS AND ENTROPY

1987 ◽  
Vol 48 (C2) ◽  
pp. C2-55-C2-57
Author(s):  
K. GÜTTER
Keyword(s):  
Collective Motion ◽  
Memory Effects

Estimation of capacity of information media with local interaction

2001 ◽  
Vol 10 (0) ◽  
pp. 212-216
Author(s):  
І. Я. Кіцула
Keyword(s):  
Local Interaction ◽  
Information Media

Analysis of the Bath Motion in the MM-SQC Dynamics Using Unsupervised Machine Learning Dimensionality Reduction Approaches: Principal Component Analysis

2020 ◽  
Author(s):  
Jiawei Peng ◽  
Yu Xie ◽  
Deping Hu ◽  
Zhenggang Lan
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Collective Motion ◽  
Principal Component ◽  
Component Analysis ◽  
Nonadiabatic Dynamics ◽  
Trajectory Data ◽  
Unsupervised Machine Learning ◽  
Physical Knowledge ◽  
Vibronic Couplings

The system-plus-bath model is an important tool to understand nonadiabatic dynamics for large molecular systems. The understanding of the collective motion of a huge number of bath modes is essential to reveal their key roles in the overall dynamics. We apply the principal component analysis (PCA) to investigate the bath motion based on the massive data generated from the MM-SQC (symmetrical quasi-classical dynamics method based on the Meyer-Miller mapping Hamiltonian) nonadiabatic dynamics of the excited-state energy transfer dynamics of Frenkel-exciton model. The PCA method clearly clarifies that two types of bath modes, which either display the strong vibronic couplings or have the frequencies close to electronic transition, are very important to the nonadiabatic dynamics. These observations are fully consistent with the physical insights. This conclusion is obtained purely based on the PCA understanding of the trajectory data, without the large involvement of pre-defined physical knowledge. The results show that the PCA approach, one of the simplest unsupervised machine learning methods, is very powerful to analyze the complicated nonadiabatic dynamics in condensed phase involving many degrees of freedom.

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