AIMC Topic: Behavior, Animal

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An Amazon stingless bee foraging activity predicted using recurrent artificial neural networks and attribute selection.

Scientific reports
Bees play a key role in pollination of crops and in diverse ecosystems. There have been multiple reports in recent years illustrating bee population declines worldwide. The search for more accurate forecast models can aid both in the understanding of...

Avoidance of non-localizable obstacles in echolocating bats: A robotic model.

PLoS computational biology
Most objects and vegetation making up the habitats of echolocating bats return a multitude of overlapping echoes. Recent evidence suggests that the limited temporal and spatial resolution of bio-sonar prevents bats from separately perceiving the obje...

Behavioural analysis of single-cell aneural ciliate, using machine learning approaches.

Journal of the Royal Society, Interface
There is still a significant gap between our understanding of neural circuits and the behaviours they compute-i.e. the computations performed by these neural networks (Carandini 2012 , 507-509. (doi:10.1038/nn.3043)). Cellular decision-making process...

Deep learning tools for the measurement of animal behavior in neuroscience.

Current opinion in neurobiology
Recent advances in computer vision have made accurate, fast and robust measurement of animal behavior a reality. In the past years powerful tools specifically designed to aid the measurement of behavior have come to fruition. Here we discuss how capt...

The Effects of Population Tuning and Trial-by-Trial Variability on Information Encoding and Behavior.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Identifying the features of population responses that are relevant to the amount of information encoded by neuronal populations is a crucial step toward understanding population coding. Statistical features, such as tuning properties, individual and ...

Considerations in using recurrent neural networks to probe neural dynamics.

Journal of neurophysiology
Recurrent neural networks (RNNs) are increasingly being used to model complex cognitive and motor tasks performed by behaving animals. RNNs are trained to reproduce animal behavior while also capturing key statistics of empirically recorded neural ac...

Design and development of a robotic predator as a stimulus in conditioned place aversion for the study of the effect of ethanol and citalopram in zebrafish.

Behavioural brain research
Zebrafish are becoming a species of choice in psychopharmacology, laying a promising path to refined pharmacological manipulations and high-throughput behavioral phenotyping. The field of robotics has the potential to accelerate progress along this p...

DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning.

eLife
Quantitative behavioral measurements are important for answering questions across scientific disciplines-from neuroscience to ecology. State-of-the-art deep-learning methods offer major advances in data quality and detail by allowing researchers to a...

Deep attention networks reveal the rules of collective motion in zebrafish.

PLoS computational biology
A variety of simple models has been proposed to understand the collective motion of animals. These models can be insightful but may lack important elements necessary to predict the motion of each individual in the collective. Adding more detail incre...

Sensory processing and categorization in cortical and deep neural networks.

NeuroImage
Many recent advances in artificial intelligence (AI) are rooted in visual neuroscience. However, ideas from more complicated paradigms like decision-making are less used. Although automated decision-making systems are ubiquitous (driverless cars, pil...