Quantifying behavior to understand the brain

Nature Neuroscience, Journal Year: 2020, Volume and Issue: 23(12), P. 1537 - 1549

Published: Nov. 9, 2020

Language: Английский

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Finding, visualizing, and quantifying latent structure across diverse animal vocal repertoires

PLoS Computational Biology, Journal Year: 2020, Volume and Issue: 16(10), P. e1008228 - e1008228

Published: Oct. 15, 2020

Animals produce vocalizations that range in complexity from a single repeated call to hundreds of unique vocal elements patterned sequences unfolding over hours. Characterizing complex can require considerable effort and deep intuition about each species' behavior. Even with great deal experience, human characterizations animal communication be affected by perceptual biases. We present set computational methods for projecting into low dimensional latent representational spaces are directly learned the spectrograms signals. apply these diverse datasets 20 species, including humans, bats, songbirds, mice, cetaceans, nonhuman primates. Latent projections uncover features data visually intuitive quantifiable ways, enabling high-powered comparative analyses acoustics. introduce analyzing as both discrete continuous variables. Each method used disentangle spectro-temporal structure observe long-timescale organization communication.

Language: Английский

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Internal state dynamics shape brainwide activity and foraging behaviour

Nature, Journal Year: 2019, Volume and Issue: 577(7789), P. 239 - 243

Published: Dec. 18, 2019

Language: Английский

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Ethology as a physical science

Nature Physics, Journal Year: 2018, Volume and Issue: 14(7), P. 653 - 657

Published: April 9, 2018

Language: Английский

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The tectum/superior colliculus as the vertebrate solution for spatial sensory integration and action

Current Biology, Journal Year: 2021, Volume and Issue: 31(11), P. R741 - R762

Published: June 1, 2021

The superior colliculus, or tectum in the case of non-mammalian vertebrates, is a part brain that registers events surrounding space, often through vision and hearing, but also electrosensation, infrared detection, other sensory modalities diverse vertebrate lineages. This information used to form maps space positions different salient stimuli relation individual. are arranged layers with visual input uppermost layer, senses deeper positions, spatially aligned motor map deepest layer. Here, we will review organization intrinsic function tectum/superior colliculus processed within tectal circuits. We discuss tectal/superior outputs conveyed directly downstream circuits via thalamus cortical areas control various aspects behavior. evolutionarily conserved among all tailored specialties each lineage, its roles have shifted emergence cerebral cortex mammals. illustrate both divergent properties evolution by comparing processing lampreys belonging oldest group extant larval zebrafish, rodents, vertebrates including primates.

Language: Английский

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Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics

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

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Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics

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

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Fish Tracking, Counting, and Behaviour Analysis in Digital Aquaculture: A Comprehensive Survey

Reviews in Aquaculture, Journal Year: 2025, Volume and Issue: 17(1)

Published: Jan. 1, 2025

ABSTRACT Digital aquaculture leverages advanced technologies and data‐driven methods, providing substantial benefits over traditional practices. This article presents a comprehensive review of three interconnected digital tasks, namely, fish tracking, counting, behaviour analysis, using novel unified approach. Unlike previous reviews which focused on single modalities or individual we analyse vision‐based (i.e., image‐ video‐based), acoustic‐based, biosensor‐based methods across all tasks. We examine their advantages, limitations, applications, highlighting recent advancements identifying critical cross‐cutting research gaps. The also includes emerging ideas such as applying multitask learning large language models to address various aspects monitoring, an approach not previously explored in literature. identify the major obstacles hindering progress this field, including scarcity datasets lack evaluation standards. To overcome current explore potential multimodal data fusion deep improve accuracy, robustness, efficiency integrated monitoring systems. In addition, provide summary existing available for analysis. holistic perspective offers roadmap future research, emphasizing need standards facilitate meaningful comparisons between promote practical implementations real‐world settings.

Language: Английский

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Nested Neuronal Dynamics Orchestrate a Behavioral Hierarchy across Timescales

Neuron, Journal Year: 2019, Volume and Issue: 105(3), P. 562 - 576.e9

Published: Nov. 27, 2019

Classical and modern ethological studies suggest that animal behavior is organized hierarchically across timescales, such longer-timescale behaviors are composed of specific shorter-timescale actions. Despite progress relating neuronal dynamics to single-timescale behavior, it remains unclear how different timescale interact give rise higher-order behavioral organization. Here, we show, in the nematode Caenorhabditis elegans, a hierarchy spanning three timescales implemented by nested dynamics. At uppermost hierarchical level, slow population brain motor periphery control two faster neuron oscillations, toggling them between activity states functional roles. lower levels, these oscillations further manner enables flexible an otherwise rigid framework. Our findings establish patterns as repeated dynamical motif C. elegans nervous system, which together implement controllable organization behavior.

Language: Английский

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Deconstructing Hunting Behavior Reveals a Tightly Coupled Stimulus-Response Loop

Current Biology, Journal Year: 2019, Volume and Issue: 30(1), P. 54 - 69.e9

Published: Dec. 19, 2019

Language: Английский

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