AIMC Topic: Behavior, Animal

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Probabilistic generative modeling and reinforcement learning extract the intrinsic features of animal behavior.

Neural networks : the official journal of the International Neural Network Society
It is one of the ultimate goals of ethology to understand the generative process of animal behavior, and the ability to reproduce and control behavior is an important step in this field. However, it is not easy to achieve this goal in systems with co...

Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders.

PLoS computational biology
Recent neuroscience studies demonstrate that a deeper understanding of brain function requires a deeper understanding of behavior. Detailed behavioral measurements are now often collected using video cameras, resulting in an increased need for comput...

Cross-species behavior analysis with attention-based domain-adversarial deep neural networks.

Nature communications
Since the variables inherent to various diseases cannot be controlled directly in humans, behavioral dysfunctions have been examined in model organisms, leading to better understanding their underlying mechanisms. However, because the spatial and tem...

Skilled reach training enhances robotic gait training to restore overground locomotion following spinal cord injury in rats.

Behavioural brain research
Rehabilitative training has been shown to improve motor function following spinal cord injury (SCI). Unfortunately, these gains are primarily task specific; where reach training only improves reaching, step training only improves stepping and stand t...

Quantifying finer-scale behaviours using self-organising maps (SOMs) to link accelerometery signatures with behavioural patterns in free-roaming terrestrial animals.

Scientific reports
Collecting quantitative information on animal behaviours is difficult, especially from cryptic species or species that alter natural behaviours under observation. Using harness-mounted tri-axial accelerometers free-roaming domestic cats (Felis Catus)...

Zebrafish behavior feature recognition using three-dimensional tracking and machine learning.

Scientific reports
In this work, we aim to construct a new behavior analysis method by using machine learning. We used two cameras to capture three-dimensional (3D) tracking data of zebrafish, which were analyzed using fuzzy adaptive resonance theory (FuzzyART), a type...

Multi-View Mouse Social Behaviour Recognition With Deep Graphic Model.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Home-cage social behaviour analysis of mice is an invaluable tool to assess therapeutic efficacy of neurodegenerative diseases. Despite tremendous efforts made within the research community, single-camera video recordings are mainly used for such ana...

Low-dimensional learned feature spaces quantify individual and group differences in vocal repertoires.

eLife
Increases in the scale and complexity of behavioral data pose an increasing challenge for data analysis. A common strategy involves replacing entire behaviors with small numbers of handpicked, domain-specific features, but this approach suffers from ...

Real-Time Closed-Loop Feedback in Behavioral Time Scales Using DeepLabCut.

eNeuro
Computer vision approaches have made significant inroads into offline tracking of behavior and estimating animal poses. In particular, because of their versatility, deep-learning approaches have been gaining attention in behavioral tracking without a...

Action detection using a neural network elucidates the genetics of mouse grooming behavior.

eLife
Automated detection of complex animal behaviors remains a challenging problem in neuroscience, particularly for behaviors that consist of disparate sequential motions. Grooming is a prototypical stereotyped behavior that is often used as an endopheno...