The Importance of Accounting for Movement When Relating Neuronal Activity to Sensory and Cognitive Processes

Journal of Neuroscience, Год журнала: 2022, Номер 42(8), С. 1375 - 1382

Опубликована: Янв. 13, 2022

A surprising finding of recent studies in mouse is the dominance widespread movement-related activity throughout brain, including early sensory areas. In awake subjects, failing to account for movement risks misattributing other (e.g., or cognitive) processes. this article, we (1) review task designs separating task-related and activity, (2) three “case studies” which not considering would have resulted critically different interpretations neuronal function, (3) discuss functional couplings that may prevent us from ever fully isolating sensory, motor, cognitive-related activity. Our main thesis neural signals related are ubiquitous, therefore ought be considered first foremost when attempting correlate with

Язык: Английский

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Optogenetic stimulation of glutamatergic neurons in the cuneiform nucleus controls locomotion in a mouse model of Parkinson’s disease

Proceedings of the National Academy of Sciences, Год журнала: 2021, Номер 118(43)

Опубликована: Окт. 20, 2021

In Parkinson's disease (PD), the loss of midbrain dopaminergic cells results in severe locomotor deficits, such as gait freezing and akinesia. Growing evidence indicates that these deficits can be attributed to decreased activity mesencephalic region (MLR), a brainstem controlling locomotion. Clinicians are exploring deep brain stimulation MLR treatment option improve function. The variable, from modest promising. However, within MLR, clinicians have targeted pedunculopontine nucleus exclusively, while leaving cuneiform unexplored. To our knowledge, effects never been determined parkinsonian conditions any animal model. Here, we addressed this issue mouse model PD, based on bilateral striatal injection 6-hydroxydopamine, which damaged nigrostriatal pathway activity. We show selective optogenetic glutamatergic neurons mice expressing channelrhodopsin Cre-dependent manner Vglut2-positive (Vglut2-ChR2-EYFP mice) increased number initiations, time spent locomotion, controlled speed. Using learning-based movement analysis, found limb kinematics optogenetic-evoked locomotion pathological were largely similar those recorded intact animals. Our work identifies potentially clinically relevant target conditions. study should open avenues develop using stimulation, pharmacotherapy, or optogenetics.

Язык: Английский

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Not so spontaneous: Multi-dimensional representations of behaviors and context in sensory areas

Neuron, Год журнала: 2022, Номер 110(19), С. 3064 - 3075

Опубликована: Июль 20, 2022

Sensory areas are spontaneously active in the absence of sensory stimuli. This spontaneous activity has long been studied; however, its functional role remains largely unknown. Recent advances technology, allowing large-scale neural recordings awake and behaving animal, have transformed our understanding activity. Studies using these discovered high-dimensional patterns, correlation between behavior, dissimilarity sensory-driven patterns. These findings supported by evidence from developing animals, where a transition toward characteristics is observed as circuit matures, well mature animals across species. newly revealed call for formulation new computation.

Язык: Английский

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Emerging technologies for behavioral research in changing environments

Trends in Ecology & Evolution, Год журнала: 2022, Номер 38(4), С. 346 - 354

Опубликована: Дек. 9, 2022

The first response exhibited by animals to changing environments is typically behavioral. Behavior thus central predicting, and mitigating, the impacts that natural anthropogenic environmental changes will have on populations and, consequently, ecosystems. Yet inherently multiscale nature of behavior, as well complexities associated with inferring how perceive their world, make decisions, has constrained scope behavioral research. Major technological advances in electronics machine learning, however, provide increasingly powerful means see, analyze, interpret behavior its complexity. We argue these disruptive technologies foster new approaches allow us move beyond quantitative descriptions reveal underlying generative processes give rise behavior.

Язык: Английский

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Task-driven neural network models predict neural dynamics of proprioception

Cell, Год журнала: 2024, Номер 187(7), С. 1745 - 1761.e19

Опубликована: Март 1, 2024

Proprioception tells the brain state of body based on distributed sensory neurons. Yet, principles that govern proprioceptive processing are poorly understood. Here, we employ a task-driven modeling approach to investigate neural code neurons in cuneate nucleus (CN) and somatosensory cortex area 2 (S1). We simulated muscle spindle signals through musculoskeletal generated large-scale movement repertoire train networks 16 hypotheses, each representing different computational goals. found emerging, task-optimized internal representations generalize from synthetic data predict dynamics CN S1 primates. Computational tasks aim limb position velocity were best at predicting activity both areas. Since task optimization develops better during active than passive movements, postulate is top-down modulated goal-directed movements.

Язык: Английский

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SuperAnimal pretrained pose estimation models for behavioral analysis

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Июнь 21, 2024

Quantification of behavior is critical in diverse applications from neuroscience, veterinary medicine to animal conservation. A common key step for behavioral analysis first extracting relevant keypoints on animals, known as pose estimation. However, reliable inference poses currently requires domain knowledge and manual labeling effort build supervised models. We present SuperAnimal, a method develop unified foundation models that can be used over 45 species, without additional labels. These show excellent performance across six estimation benchmarks. demonstrate how fine-tune the (if needed) differently labeled data provide tooling unsupervised video adaptation boost decrease jitter frames. If fine-tuned, SuperAnimal are 10-100× more efficient than prior transfer-learning-based approaches. illustrate utility our classification kinematic analysis. Collectively, we data-efficient solution

Язык: Английский

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AcinoSet: A 3D Pose Estimation Dataset and Baseline Models for Cheetahs in the Wild

Опубликована: Май 30, 2021

Animals are capable of extreme agility, yet understanding their complex dynamics, which have ecological, biomechanical and evolutionary implications, remains challenging. Being able to study this incredible agility will be critical for the development next-generation autonomous legged robots. In particular, cheetah (acinonyx jubatus) is supremely fast maneuverable, quantifying its wholebody 3D kinematic data during locomotion in wild a challenge, even with new deep learning-based methods. work we present an extensive dataset free-running cheetahs wild, called AcinoSet, that contains 119, 490 frames multi-view synchronized high-speed video footage, camera calibration files 7, 588 human-annotated frames. We utilize markerless animal pose estimation provide 2D keypoints. Then, use three methods serve as strong baselines tool development: traditional sparse bundle adjustment, Extended Kalman Filter, trajectory optimization-based method call Full Trajectory Estimation. The resulting trajectories, human-checked ground truth, interactive inspect also provided. believe useful diverse range fields such ecology, neuroscience, robotics, biomechanics well computer vision. Code can found at: https://github.com/African-Robotics-Unit/AcinoSet.

Язык: Английский

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Precise Quantification of Behavioral Individuality From 80 Million Decisions Across 183,000 Flies

Frontiers in Behavioral Neuroscience, Год журнала: 2022, Номер 16

Опубликована: Май 26, 2022

Individual animals behave differently from each other. This variability is a component of personality and arises even when genetics environment are held constant. Discovering the biological mechanisms underlying behavioral depends on efficiently measuring individual bias, requirement that facilitated by automated, high-throughput experiments. We compiled large data set locomotor behavior measures, acquired over 183,000 fruit flies walking in Y-shaped mazes. With this we first conducted "computational ethology natural history" study to quantify distribution biases with unprecedented precision examine correlations between measures high power. discovered slight, but highly significant, left-bias spontaneous decision-making. then used evaluate standing hypotheses about affecting variability, specifically: neuromodulator serotonin its precursor transporter, heterogametic sex, temperature. found variety significant effects associated these were behavior-dependent. indicates relationship may be context dependent. Going forward, automation experiments will likely essential teasing out complex causality individuality.

Язык: Английский

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Not everything, not everywhere, not all at once: a study of brain-wide encoding of movement

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Июнь 9, 2023

Abstract Activity related to movement is found throughout sensory and motor regions of the brain. However, it remains unclear how movement-related activity distributed across brain whether systematic differences exist between areas. Here, we analyzed in brain-wide recordings containing more than 50,000 neurons mice performing a decision-making task. Using multiple techniques, from markers deep neural networks, find that signals were pervasive brain, but systematically differed Movement-related was stronger areas closer or periphery. Delineating terms sensory- motor-related components revealed finer scale structures their encodings within We further identified modulation correlates with uninstructed movement. Our work charts out largescale map encoding provides roadmap for dissecting different forms multi-regional circuits.

Язык: Английский

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Contrasting action and posture coding with hierarchical deep neural network models of proprioception

eLife, Год журнала: 2023, Номер 12

Опубликована: Май 31, 2023

Biological motor control is versatile, efficient, and depends on proprioceptive feedback. Muscles are flexible undergo continuous changes, requiring distributed adaptive mechanisms that continuously account for the body's state. The canonical role of proprioception representing body We hypothesize system could also be critical high-level tasks such as action recognition. To test this theory, we pursued a task-driven modeling approach, which allowed us to isolate study proprioception. generated large synthetic dataset human arm trajectories tracing characters Latin alphabet in 3D space, together with muscle activities obtained from musculoskeletal model model-based spindle activity. Next, compared two classes tasks: trajectory decoding recognition, train hierarchical models decode either position velocity end-effector one's posture or character (action) identity firing patterns. found artificial neural networks robustly solve both tasks, networks' units show tuning properties similar neurons primate somatosensory cortex brainstem. Remarkably, uniformly directional selective only action-recognition-trained not trajectory-decoding-trained models. This suggests encoding additionally associated higher-level functions recognition therefore provides new, experimentally testable hypotheses how aids control.

Язык: Английский

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