A-SOiD, an active learning platform for expert-guided, data efficient discovery of behavior. DOI Creative Commons
Eric A. Yttri, Alexander Hsu,

Jens Schweihoff

et al.

Research Square (Research Square), Journal Year: 2023, Volume and Issue: unknown

Published: March 23, 2023

Abstract Behavior identification and quantification techniques have undergone rapid development. To this end, supervised or unsupervised methods are chosen based upon their intrinsic strengths weaknesses (e.g. user bias, training cost, complexity, action discovery). Here, a new active learning platform, A-SOiD, blends these in doing so, overcomes several of inherent drawbacks. A-SOiD iteratively learns user-defined groups with fraction the usual data while attaining expansive classification through directed classification. In socially-interacting mice, outperformed standard despite requiring 85$\%$ less data. Additionally, it isolated two additional ethologically-distinct mouse interactions via Similar performance efficiency was observed using non-human primate 3D pose both cases, transparency A-SOiD’s cluster definitions revealed defining features game-theoretic approach. facilitate use, comes as an intuitive, open-source interface for efficient segmentation behaviors discovered subactions.

Language: Английский

Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics DOI Creative Commons
Caleb Weinreb, Jonah E Pearl, Sherry Lin

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: March 17, 2023

Abstract Keypoint tracking algorithms have revolutionized the analysis of animal behavior, enabling investigators to flexibly quantify behavioral dynamics from conventional video recordings obtained in a wide variety settings. However, it remains unclear how parse continuous keypoint data into modules out which behavior is organized. This challenge particularly acute because susceptible high frequency jitter that clustering can mistake for transitions between modules. Here we present keypoint-MoSeq, machine learning-based platform identifying (“syllables”) without human supervision. Keypoint-MoSeq uses generative model distinguish noise effectively identify syllables whose boundaries correspond natural sub-second discontinuities inherent mouse behavior. outperforms commonly used alternative methods at these transitions, capturing correlations neural activity and classifying either solitary or social behaviors accordance with annotations. therefore renders grammar accessible many researchers who use standard capture

Language: Английский

Citations

61
Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics DOI Creative Commons
Caleb Weinreb, Jonah E Pearl, Sherry Lin

et al.

Nature Methods, Journal Year: 2024, Volume and Issue: 21(7), P. 1329 - 1339

Published: July 1, 2024

Abstract Keypoint tracking algorithms can flexibly quantify animal movement from videos obtained in a wide variety of settings. However, it remains unclear how to parse continuous keypoint data into discrete actions. This challenge is particularly acute because are susceptible high-frequency jitter that clustering mistake for transitions between Here we present keypoint-MoSeq, machine learning-based platform identifying behavioral modules (‘syllables’) without human supervision. Keypoint-MoSeq uses generative model distinguish noise behavior, enabling identify syllables whose boundaries correspond natural sub-second discontinuities pose dynamics. outperforms commonly used alternative methods at these transitions, capturing correlations neural activity and behavior classifying either solitary or social behaviors accordance with annotations. also works multiple species generalizes beyond the syllable timescale, fast sniff-aligned movements mice spectrum oscillatory fruit flies. Keypoint-MoSeq, therefore, renders accessible modular structure through standard video recordings.

Language: Английский

Citations

44
A virtual rodent predicts the structure of neural activity across behaviors DOI
Diego Aldarondo,

Josh Merel,

Jesse D. Marshall

et al.

Nature, Journal Year: 2024, Volume and Issue: 632(8025), P. 594 - 602

Published: June 11, 2024

Language: Английский

Citations

19
Mapping the landscape of social behavior DOI Creative Commons
Ugne Klibaite,

Tianqing Li,

Diego Aldarondo

et al.

Cell, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

Citations

3
A-SOiD, an active-learning platform for expert-guided, data-efficient discovery of behavior DOI
Jens F. Tillmann, Alexander Hsu, Martin K. Schwarz

et al.

Nature Methods, Journal Year: 2024, Volume and Issue: 21(4), P. 703 - 711

Published: Feb. 21, 2024

Language: Английский

Citations

16
The what, how, and why of naturalistic behavior DOI Creative Commons
Ann Kennedy

Current Opinion in Neurobiology, Journal Year: 2022, Volume and Issue: 74, P. 102549 - 102549

Published: May 7, 2022

Language: Английский

Citations

36
Neural mechanisms underlying the temporal organization of naturalistic animal behavior DOI Creative Commons
Luca Mazzucato

eLife, Journal Year: 2022, Volume and Issue: 11

Published: July 6, 2022

Naturalistic animal behavior exhibits a strikingly complex organization in the temporal domain, with variability arising from at least three sources: hierarchical, contextual, and stochastic. What neural mechanisms computational principles underlie such intricate features? In this review, we provide critical assessment of existing behavioral neurophysiological evidence for these sources naturalistic behavior. Recent research converges on an emergent mechanistic theory based attractor networks metastable dynamics, via coordinated interactions between mesoscopic circuits. We highlight crucial role played by structural heterogeneities as well noise feedback loops regulating flexible assess shortcomings missing links current theoretical experimental literature propose new directions investigation to fill gaps.

Language: Английский

Citations

30
Cerebellar contributions to a brainwide network for flexible behavior in mice DOI Creative Commons
Jessica L. Verpeut, Silke Bergeler, Mikhail Kislin

et al.

Communications Biology, Journal Year: 2023, Volume and Issue: 6(1)

Published: June 5, 2023

The cerebellum regulates nonmotor behavior, but the routes of influence are not well characterized. Here we report a necessary role for posterior in guiding reversal learning task through network diencephalic and neocortical structures, flexibility free behavior. After chemogenetic inhibition lobule VI vermis or hemispheric crus I Purkinje cells, mice could learn water Y-maze were impaired ability to reverse their initial choice. To map targets perturbation, imaged c-Fos activation cleared whole brains using light-sheet microscopy. Reversal activated associative regions. Distinctive subsets structures altered by perturbation (including thalamus habenula) hypothalamus prelimbic/orbital cortex), both perturbations influenced anterior cingulate infralimbic cortex. identify functional networks, used correlated variation within each group. Lobule inactivation weakened within-thalamus correlations, while divided activity into sensorimotor subnetworks. In groups, high-throughput automated analysis whole-body movement revealed deficiencies across-day behavioral habituation an open-field environment. Taken together, these experiments reveal brainwide systems cerebellar that affect multiple flexible responses.

Language: Английский

Citations

21
Elephants and algorithms: a review of the current and future role of AI in elephant monitoring DOI Creative Commons
Leandra Brickson,

Libby Zhang,

Fritz Vollrath

et al.

Journal of The Royal Society Interface, Journal Year: 2023, Volume and Issue: 20(208)

Published: Nov. 1, 2023

Artificial intelligence (AI) and machine learning (ML) present revolutionary opportunities to enhance our understanding of animal behaviour conservation strategies. Using elephants, a crucial species in Africa Asia’s protected areas, as focal point, we delve into the role AI ML their conservation. Given increasing amounts data gathered from variety sensors like cameras, microphones, geophones, drones satellites, challenge lies managing interpreting this vast data. New techniques offer solutions streamline process, helping us extract vital information that might otherwise be overlooked. This paper focuses on different AI-driven monitoring methods potential for improving elephant Collaborative efforts between experts ecological researchers are essential leveraging these innovative technologies enhanced wildlife conservation, setting precedent numerous other species.

Language: Английский

Citations

19
Experiment protocols for brain-body imaging of locomotion: A systematic review DOI Creative Commons
Soroush Korivand, Nader Jalili, Jiaqi Gong

et al.

Frontiers in Neuroscience, Journal Year: 2023, Volume and Issue: 17

Published: March 1, 2023

Human locomotion is affected by several factors, such as growth and aging, health conditions, physical activity levels for maintaining overall well-being. Notably, impaired a prevalent cause of disability, significantly impacting the quality life individuals. The uniqueness high prevalence human have led to surge research develop experimental protocols studying brain substrates, muscle responses, motion signatures associated with locomotion. However, from technical perspective, reproducing experiments has been challenging due lack standardized benchmarking tools, which impairs evaluation validation previous findings.This paper addresses challenges conducting systematic review existing neuroimaging studies on locomotion, focusing settings protocols, intensity, duration, distance, adopted imaging technologies, corresponding activation patterns. Also, this study provides practical recommendations future experiment protocols.The findings indicate that EEG preferred sensor detecting patterns, compared fMRI, fNIRS, PET. Walking most studied task, likely its fundamental nature status reference task. In contrast, running received little attention in research. Additionally, cycling an ergometer at speed 60 rpm using fNIRS provided some basis. Dual-task walking tasks are typically used observe changes cognitive function. Moreover, primarily focused healthy individuals, scenario closely resembling free-living real-world environments.Finally, outlines standards setting up based findings. It discusses impact neurological musculoskeletal well locomotive demands, design. also considers limitations imposed sensing techniques used, including acceptable level artifacts brain-body effects spatial temporal resolutions performance. various protocol constraints need be addressed analyzed explained.

Language: Английский

Citations

10