AIMC Topic: Social Behavior

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Automated monitoring of honey bees with barcodes and artificial intelligence reveals two distinct social networks from a single affiliative behavior.

Scientific reports
Barcode-based tracking of individuals is revolutionizing animal behavior studies, but further progress hinges on whether in addition to determining an individual's location, specific behaviors can be identified and monitored. We achieve this goal usi...

Social behavioral profiling by unsupervised deep learning reveals a stimulative effect of dopamine D3 agonists on zebrafish sociality.

Cell reports methods
It has been a major challenge to systematically evaluate and compare how pharmacological perturbations influence social behavioral outcomes. Although some pharmacological agents are known to alter social behavior, precise description and quantificati...

Noninvasive Tracking of Every Individual in Unmarked Mouse Groups Using Multi-Camera Fusion and Deep Learning.

Neuroscience bulletin
Accurate and efficient methods for identifying and tracking each animal in a group are needed to study complex behaviors and social interactions. Traditional tracking methods (e.g., marking each animal with dye or surgically implanting microchips) ca...

Can nurses in clinical practice ascribe responsibility to intelligent robots?

Nursing ethics
BACKGROUND: The twenty first- century marked the exponential growth in the use of intelligent robots and artificial intelligent in nursing compared to the previous decades. To the best of our knowledge, this article is first in responding to question...

Any colour you like: fish interacting with bioinspired robots unravel mechanisms promoting mixed phenotype aggregations.

Bioinspiration & biomimetics
Collective behaviours in homogeneous shoals provide several benefits to conspecifics, although mixed-species aggregations have been reported to often occur. Mixed aggregations may confer several beneficial effects such as antipredator and foraging ad...

SLEAP: A deep learning system for multi-animal pose tracking.

Nature methods
The desire to understand how the brain generates and patterns behavior has driven rapid methodological innovation in tools to quantify natural animal behavior. While advances in deep learning and computer vision have enabled markerless pose estimatio...

Automatic mapping of multiplexed social receptive fields by deep learning and GPU-accelerated 3D videography.

Nature communications
Social interactions powerfully impact the brain and the body, but high-resolution descriptions of these important physical interactions and their neural correlates are lacking. Currently, most studies rely on labor-intensive methods such as manual an...

The creation of phenomena in interactive biorobotics.

Biological cybernetics
In so-called interactive biorobotics, robotic models of living systems interact with animals in controlled experimental settings. By observing how the focal animal reacts to the stimuli delivered by the robot, one tests hypotheses concerning the dete...

Persuasive robots should avoid authority: The effects of formal and real authority on persuasion in human-robot interaction.

Science robotics
Social robots must take on many roles when interacting with people in everyday settings, some of which may be authoritative, such as a nurse, teacher, or guard. It is important to investigate whether and how authoritative robots can influence people ...

DeepEthogram, a machine learning pipeline for supervised behavior classification from raw pixels.

eLife
Videos of animal behavior are used to quantify researcher-defined behaviors of interest to study neural function, gene mutations, and pharmacological therapies. Behaviors of interest are often scored manually, which is time-consuming, limited to few ...