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Behavior, Animal

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Automation of training and testing motor and related tasks in pre-clinical behavioural and rehabilitative neuroscience.

Experimental neurology
Testing and training animals in motor and related tasks is a cornerstone of pre-clinical behavioural and rehabilitative neuroscience. Yet manually testing and training animals in these tasks is time consuming and analyses are often subjective. Conseq...

Unsupervised manifold learning of collective behavior.

PLoS computational biology
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 generat...

Real-time volumetric reconstruction of biological dynamics with light-field microscopy and deep learning.

Nature methods
Light-field microscopy has emerged as a technique of choice for high-speed volumetric imaging of fast biological processes. However, artifacts, nonuniform resolution and a slow reconstruction speed have limited its full capabilities for in toto extra...

DeepLabStream enables closed-loop behavioral experiments using deep learning-based markerless, real-time posture detection.

Communications biology
In general, animal behavior can be described as the neuronal-driven sequence of reoccurring postures through time. Most of the available current technologies focus on offline pose estimation with high spatiotemporal resolution. However, to correlate ...

Human-dog relationships as a working framework for exploring human-robot attachment: a multidisciplinary review.

Animal cognition
Robotic agents will be life-long companions of humans in the foreseeable future. To achieve such successful relationships, people will likely attribute emotions and personality, assign social competencies, and develop a long-lasting attachment to rob...

Automated detection of mouse scratching behaviour using convolutional recurrent neural network.

Scientific reports
Scratching is one of the most important behaviours in experimental animals because it can reflect itching and/or psychological stress. Here, we aimed to establish a novel method to detect scratching using deep neural network. Scratching was elicited ...

Machine Learning Algorithms to Classify and Quantify Multiple Behaviours in Dairy Calves Using a Sensor: Moving beyond Classification in Precision Livestock.

Sensors (Basel, Switzerland)
Previous research has shown that sensors monitoring lying behaviours and feeding can detect early signs of ill health in calves. There is evidence to suggest that monitoring change in a single behaviour might not be enough for disease prediction. In ...

Directed Evolution of a Selective and Sensitive Serotonin Sensor via Machine Learning.

Cell
Serotonin plays a central role in cognition and is the target of most pharmaceuticals for psychiatric disorders. Existing drugs have limited efficacy; creation of improved versions will require better understanding of serotonergic circuitry, which ha...

Assessment of Machine Learning Models to Identify Port Jackson Shark Behaviours Using Tri-Axial Accelerometers.

Sensors (Basel, Switzerland)
Movement ecology has traditionally focused on the movements of animals over large time scales, but, with advancements in sensor technology, the focus can become increasingly fine scale. Accelerometers are commonly applied to quantify animal behaviour...

Machine learning enables improved runtime and precision for bio-loggers on seabirds.

Communications biology
Unravelling the secrets of wild animals is one of the biggest challenges in ecology, with bio-logging (i.e., the use of animal-borne loggers or bio-loggers) playing a pivotal role in tackling this challenge. Bio-logging allows us to observe many aspe...