Recurrent 3D Convolutional Network for Rodent Behavior Recognition

Le, Van Anh; Murari, Kartikeya

doi:10.1109/icassp.2019.8683238

Cited by 7 publications

(3 citation statements)

References 20 publications

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“…Results in recent studies have been mixed. In one report [52] the algorithm slightly underperformed compared to the original study using the same data, which had used manually selected features [27]. Another study [53] outperformed an older study using the same data [40].…”

Section: Supervised Machine Learningmentioning

confidence: 97%

“…In most cases the ground truth is not perfectly defined and contains a lot of variability. Additionally, there is currently a lack of extensive, well-annotated data-sets, and many studies use older labeled data from previous studies [52][53][54]. A potential approach could be to create extensive, well-annotated labeling sets, which include only examples where all raters agree with one another, however these would omit difficult cases from the training set and thus limit the sensitivity of the classifier.…”

Section: Big Data Big Problems Small Solutionsmentioning

confidence: 99%

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Big behavior: challenges and opportunities in a new era of deep behavior profiling

Ziegler

Sturman

Bohacek

2020

Neuropsychopharmacol.

100

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The assessment of rodent behavior forms a cornerstone of preclinical assessment in neuroscience research. Nonetheless, the true and almost limitless potential of behavioral analysis has been inaccessible to scientists until very recently. Now, in the age of machine vision and deep learning, it is possible to extract and quantify almost infinite numbers of behavioral variables, to break behaviors down into subcategories and even into small behavioral units, syllables or motifs. However, the rapidly growing field of behavioral neuroethology is experiencing birthing pains. The community has not yet consolidated its methods, and new algorithms transfer poorly between labs. Benchmarking experiments as well as the large, well-annotated behavior datasets required are missing. Meanwhile, big data problems have started arising and we currently lack platforms for sharing large datasets—akin to sequencing repositories in genomics. Additionally, the average behavioral research lab does not have access to the latest tools to extract and analyze behavior, as their implementation requires advanced computational skills. Even so, the field is brimming with excitement and boundless opportunity. This review aims to highlight the potential of recent developments in the field of behavioral analysis, whilst trying to guide a consensus on practical issues concerning data collection and data sharing.

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Section: Supervised Machine Learningmentioning

confidence: 97%

Section: Big Data Big Problems Small Solutionsmentioning

confidence: 99%

Big behavior: challenges and opportunities in a new era of deep behavior profiling

Ziegler

Sturman

Bohacek

2020

Neuropsychopharmacol.

100

View full text Add to dashboard Cite

show abstract

“…Le et al [125] propose a framework that uses a 3D Convolutional network (ConvNet) to extract short-term spatio-temporal features from overlapped short clips. Then those local features are fed to a Long Short Term Memory network to learn long-term features which are used for classification.…”

Section: Action Recognitionmentioning

confidence: 99%

Can MiceGPT be True? A Survey on AI-empowered Mice Behavior Analysis Applications and Solutions

Guo¹,

Chen²,

Ma³

et al. 2023

Preprint

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Mice are one of the frequently used animal models in science research whose behavioral characteristics can provide much valuable information in biology, neuroscience, and pharmacology. Nowadays, artificial intelligence is widely used in mice behavior analysis. Integrated AI systems such as ChatGPT and VisualGPT are already available, and we discuss the feasibility of MiceGPT to help researchers identify and classify mouse behavior more easily. We review the applications of mice behavior analysis, analyze the tasks of deep learning on these applications based on an AI pyramid, and finally summarize the AI approaches to solve these tasks. Based on these summaries, we propose three MiceGPT architectures to demonstrate the theoretical feasibility of MiceGPT.

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Of Mice and Mates: Automated Classification and Modelling of Mouse Behaviour in Groups Using a Single Model Across Cages

Camilleri,

Bains,

Williams

2024

Int J Comput Vis

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Behavioural experiments often happen in specialised arenas, but this may confound the analysis. To address this issue, we provide tools to study mice in the home-cage environment, equipping biologists with the possibility to capture the temporal aspect of the individual’s behaviour and model the interaction and interdependence between cage-mates with minimal human intervention. Our main contribution is the novel Global Behaviour Model (GBM) which summarises the joint behaviour of groups of mice across cages, using a permutation matrix to match the mouse identities in each cage to the model. In support of the above, we also (a) developed the Activity Labelling Module (ALM) to automatically classify mouse behaviour from video, and (b) released two datasets, ABODe for training behaviour classifiers and IMADGE for modelling behaviour.

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Recurrent 3D Convolutional Network for Rodent Behavior Recognition

Cited by 7 publications

References 20 publications

Big behavior: challenges and opportunities in a new era of deep behavior profiling

Big behavior: challenges and opportunities in a new era of deep behavior profiling

Can MiceGPT be True? A Survey on AI-empowered Mice Behavior Analysis Applications and Solutions

Of Mice and Mates: Automated Classification and Modelling of Mouse Behaviour in Groups Using a Single Model Across Cages

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