Computational Neuroethology: A Call to Action

Vision: A Computational Investigation into the Human Representation and Processing of Visual Information

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Vision: A computational investigation into the human representation and processing of visual information

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DeepLabCut: markerless pose estimation of user-defined body parts with deep learning

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On aims and methods of ethology

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Sensory Communication

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The Study of Instinct

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Growing points in ethology

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The Variable Discharge of Cortical Neurons: Implications for Connectivity, Computation, and Information Coding

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Neural population dynamics during reaching

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Vehicles: Experiments in Synthetic Psychology.

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Modelling behaviors relevant to brain disorders in the nonhuman primate: Are we there yet?

Scott, J. T., & Bourne, J. A. (2021) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2021.102183 · Cited by 31

From Photons to Behaviors: Neural Implementations of Visual Behaviors in Drosophila

Ryu, L., Kim, S. Y., & Kim, A. J. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.883640 · Cited by 31

Widespread coding of navigational variables in prefrontal cortex

Maisson, D. J.-N., Cervera, R. L., Voloh, B., Conover, I., Zambre, M., Zimmermann, J., & Hayden, B. Y. (2023) · Current Biology · doi:10.1016/j.cub.2023.07.024 · Cited by 33

AUTOPILOT: Automating experiments with lots of Raspberry PisPreprint

Saunders, J. L., Ott, L. A., & Wehr, M. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/807693 · Cited by 29

Kinematic coding: Measuring information in naturalistic behaviour

Becchio, C., Pullar, K. F., Scaliti, E., & Panzeri, S. (2024) · Physics of Life Reviews · doi:10.1016/j.plrev.2024.11.009 · Cited by 33

Internal State: Dynamic, Interconnected Communication Loops Distributed Across Body, Brain, and Time

Kanwal, J. K., Coddington, E. J., Frazer, R., Limbania, D., Turner, G., Davila, K. J., Givens, M. A., Williams, V. M., Datta, S. R., & Wasserman, S. M. (2021) · Integrative and Comparative Biology · doi:10.1093/icb/icab101 · Cited by 30

Separating cognitive and motor processes in the behaving mouse

Hasnain, M. A., Birnbaum, J. E., Nunez, J. L. U., Hartman, E. K., Chandrasekaran, C., & Economo, M. N. (2025) · Nature Neuroscience · doi:10.1038/s41593-024-01859-1 · Cited by 35

Dissociable contributions of phasic dopamine activity to reward and prediction

Pan, W. X., Coddington, L. T., & Dudman, J. T. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109684 · Cited by 29

MouseVenue3D: A Markerless Three-Dimension Behavioral Tracking System for Matching Two-Photon Brain Imaging in Free-Moving Mice

Han, Y., Huang, K., Chen, K., Pan, H., Ju, F., Long, Y., Gao, G., Wu, R., Wang, A., Wang, L., et al. (2021) · Neuroscience Bulletin · doi:10.1007/s12264-021-00778-6 · Cited by 27

A lexical approach for identifying behavioural action sequences

Reddy, G., Desban, L., Tanaka, H., Roussel, J., Mirat, O., & Wyart, C. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009672 · Cited by 29

A dynamical systems view of neuroethology: Uncovering stateful computation in natural behaviors

Robson, D. N., & Li, J. (2022) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2022.01.002 · Cited by 25

Ethical considerations for integrating multimodal computer perception and neurotechnology

Hurley, M. E., Sonig, A., Herrington, J. D., Storch, E. A., Lázaro‐Muñoz, G., Blumenthal‐Barby, J., & Kostick, K. M. (2024) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2024.1332451 · Cited by 28

Vascular and glymphatic dysfunction as drivers of cognitive impairment in Alzheimer's disease: Insights from computational approaches

Fatima, G., Ashiquzzaman, A., Kim, S. S., Kim, Y. R., Kwon, H.-S., & Chung, E. (2025) · Neurobiology of Disease · doi:10.1016/j.nbd.2025.106877 · Cited by 26

From Neurons to Cognition: Technologies for Precise Recording of Neural Activity Underlying Behavior

Roth, R. H., & Ding, J. (2020) · BME Frontiers · doi:10.34133/2020/7190517 · Cited by 28

Personality beyond taxonomy

Dubois, J., Eberhardt, F., Paul, L. K., & Adolphs, R. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-00989-3 · Cited by 23

Long Timescales, Individual Differences, and Scale Invariance in Animal Behavior

Bialek, W., & Shaevitz, J. W. (2024) · Physical Review Letters · doi:10.1103/physrevlett.132.048401 · Cited by 23

A cortical circuit for orchestrating oromanual food manipulationPreprint

An, X., Li, Y., Matho, K. S., Mohan, H., Xu, X. H., Whishaw, I. Q., Kepecs, Á., & Huang, Z. J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.03.518964 · Cited by 24

Lifelong behavioral screen reveals an architecture of vertebrate aging

Bedbrook, C. N., Nath, R. D., Zhang, L., Linderman, S. W., Brunet, A., & Deisseroth, K. A. (2026) · Science · doi:10.1126/science.aea9795 · Cited by 22

Correcting motion induced fluorescence artifacts in two-channel neural imaging

Creamer, M. S., Chen, K. S., Leifer, A. M., & Pillow, J. W. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010421 · Cited by 22

Male and female mice display consistent lifelong ability to address potential life-threatening cues using different post-threat coping strategies

Liu, X., Feng, X., Huang, H., Huang, K., Xu, Y., Ye, S., Tseng, Y.-T., Wei, P., Wang, L., & Wang, F. (2022) · BMC Biology · doi:10.1186/s12915-022-01486-x · Cited by 24

Uncovering 2-D toroidal representations in grid cell ensemble activity during 1-D behavior

Hermansen, E., Klindt, D. A., & Dunn, B. A. (2024) · Nature Communications · doi:10.1038/s41467-024-49703-1 · Cited by 22

Neurorobots as a Means Toward Neuroethology and Explainable AI

Chen, K., Hwu, T. J., Kashyap, H. J., Krichmar, J. L., Stewart, K. M., Xing, J., & Zou, X. (2020) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2020.570308 · Cited by 22

Quantifying influence of human choice on the automated detection of Drosophila behavior by a supervised machine learning algorithm

Leng, X., Wohl, M. P., Ishii, K., Nayak, P. K., & Asahina, K. (2020) · PLoS ONE · doi:10.1371/journal.pone.0241696 · Cited by 24

Comprehensive analysis of locomotion dynamics in the protochordate Ciona intestinalis reveals how neuromodulators flexibly shape its behavioral repertoire

Athira, A., Dondorp, D., Rudolf, J., Peytral, O., & Chatzigeorgiou, M. (2022) · PLoS Biology · doi:10.1371/journal.pbio.3001744 · Cited by 22

Ethological computational psychiatry: Challenges and opportunities

Monosov, I. E., Zimmermann, J., Frank, M. J., Mathis, M. W., & Baker, J. T. (2024) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2024.102881 · Cited by 21

Behavioral neuroscience’s inevitable SABV growing pains

Shansky, R. M. (2024) · Trends in Neurosciences · doi:10.1016/j.tins.2024.06.007 · Cited by 24

Quantitative standardization of resident mouse behavior for studies of aggression and social defeat

Kwiatkowski, C. C., Akaeze, H., Ndlebe, I., Goodwin, N. L., Eagle, A. L., Moon, K., Bender, A. R., Golden, S. A., & Robison, A. J. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-021-01018-1 · Cited by 21

Using an unbiased symbolic movement representation to characterize Parkinson’s disease states

Abrami, A., Heisig, S., Ramos, V. F. M. L., Thomas, K. C., Ho, B. K., & Caggiano, V. (2020) · Scientific Reports · doi:10.1038/s41598-020-64181-3 · Cited by 20

Objective and comprehensive re-evaluation of anxiety-like behaviors in mice using the Behavior Atlas

Liu, N., Han, Y., Ding, H., Huang, K., Wei, P., & Wang, L. (2021) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2021.03.125 · Cited by 21

Behavioral and Neural Variability of Naturalistic Arm Movements

Peterson, S. M., Singh, S. H., Wang, N. X. R., Rao, R. P. N., & Brunton, B. W. (2021) · eNeuro · doi:10.1523/eneuro.0007-21.2021 · Cited by 23

Refinements to rodent head fixation and fluid/food control for neuroscience

Barkus, C., Bergmann, C., Branco, T., Carandini, M., Chadderton, P. T., Galiñanes, G. L., Gilmour, G., Huber, D., Huxter, J. R., Khan, A. G., et al. (2022) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2022.109705 · Cited by 23

Developing a Data-Fused Water Quality Index Based on Artificial Intelligence Models to Mitigate Conflicts between GQI and GWQI

Nadiri, A. A., Barzegar, R., Sadeghfam, S., & Rostami, A. (2022) · Water · doi:10.3390/w14193185 · Cited by 20

Maximally predictive states: From partial observations to long timescales

Costa, A. C., Ahamed, T., Jordan, D., & Stephens, G. J. (2023) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0129398 · Cited by 25

A Head-Mounted Multi-Camera System for Electrophysiology and Behavior in Freely-Moving Mice

Sattler, N. J., & Wehr, M. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.592417 · Cited by 21

Functional ultrasound imaging: A useful tool for functional connectomics?

de Paz, J. M. M., & Macé, É. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118722 · Cited by 19

A high-throughput method to deliver targeted optogenetic stimulation to moving C. elegans populations

Liu, M., Kumar, S., Sharma, A. K., & Leifer, A. M. (2022) · PLoS Biology · doi:10.1371/journal.pbio.3001524 · Cited by 19

Behavioral algorithms and neural mechanisms underlying odor-modulated locomotion in insects

Wechsler, S. P., & Bhandawat, V. (2023) · Journal of Experimental Biology · doi:10.1242/jeb.200261 · Cited by 21

Thermal modulation of Zebrafish exploratory statistics reveals constraints on individual behavioral variability

Goc, G. L., Lafaye, J., Karpenko, S., Bormuth, V., Candelier, R., & Debrégeas, G. (2021) · BMC Biology · doi:10.1186/s12915-021-01126-w · Cited by 18

Noncanonical genomic imprinting in the monoamine system determines naturalistic foraging and brain-adrenal axis functions

Bonthuis, P. J., Steinwand, S., Hörndli, C. S., Emery, J., Huang, W.-C., Kravitz, S. N., Ferris, E., & Gregg, C. J. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110500 · Cited by 18

Analysis of Mouse Vocal Communication (AMVOC): a deep, unsupervised method for rapid detection, analysis and classification of ultrasonic vocalisations

Stoumpou, V., Vargas, C. D. M., Schade, P. F., Boyd, J. L., Γιαννακόπουλος, Θ., & Jarvis, E. D. (2022) · Bioacoustics · doi:10.1080/09524622.2022.2099973 · Cited by 19

Effects of ketamine on rat social behavior as analyzed by DeepLabCut and SimBA deep learning algorithms

Popik, P., Cyrano, E., Piotrowska, D., Hołuj, M., Gołębiowska, J., Malikowska‐Racia, N., Potasiewicz, A., & Nikiforuk, A. (2024) · Frontiers in Pharmacology · doi:10.3389/fphar.2023.1329424 · Cited by 18

Representing the dynamics of natural marmoset vocal behaviors in frontal cortex

Li, J., Aoi, M., & Miller, C. T. (2024) · Neuron · doi:10.1016/j.neuron.2024.08.020 · Cited by 18

SAMPL is a high-throughput solution to study unconstrained vertical behavior in small animals

Zhu, Y., Auer, F., Gelnaw, H., Davis, S. N., Hamling, K. R., May, C. E., Ahamed, H., Ringstad, N., Nagel, K. I., & Schoppik, D. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112573 · Cited by 17

Unsupervised discovery of family specific vocal usage in the Mongolian gerbilPreprint

Peterson, R. E., Choudhri, A., Mitelut, C., Tanelus, A., Capo-Battaglia, A., Williams, A. H., Schneider, D. M., & Sanes, D. H. (2023) · eLife · doi:10.7554/elife.89892.3 · Cited by 17

A neurogenetic mechanism of experience-dependent suppression of aggression

Ishii, K., Cortese, M., Leng, X., Shokhirev, M. N., & Asahina, K. (2022) · Science Advances · doi:10.1126/sciadv.abg3203 · Cited by 16

Validation of deep learning-based markerless 3D pose estimation

Kosourikhina, V., Kavanagh, D., Richardson, M. J., & Kaplan, D. M. (2022) · PLoS ONE · doi:10.1371/journal.pone.0276258 · Cited by 16

Leveraging big data to uncover the eco-evolutionary factors shaping behavioural development

Ehlman, S. M., Scherer, U., Bierbach, D., Francisco, F. A., Laskowski, K. L., Krause, J., & Wolf, M. (2023) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2022.2115 · Cited by 16

Investigating visual navigation using spiking neural network models of the insect mushroom bodies

Jesusanmi, O. O., Amin, A. A., Domcsek, N., Knight, J. C., Philippides, A. O., Nowotny, T., & Graham, P. (2024) · Frontiers in Physiology · doi:10.3389/fphys.2024.1379977 · Cited by 20

Activated PI3Kδ syndrome, an immunodeficiency disorder, leads to sensorimotor deficits recapitulated in a murine model

Serra, I., Manusama, O. R., Kaiser, F. M. P., Floriano, I. I., Wahl, L. F., van der Zalm, C., IJspeert, H., van Hagen, P. M., van Beveren, N. J. M., Arend, S. M., et al. (2021) · Brain Behavior & Immunity - Health · doi:10.1016/j.bbih.2021.100377 · Cited by 16

Structure uncovered: understanding temporal variability in perceptual decision-making

Urai, A. E. (2025) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2025.06.003 · Cited by 15

From Adaptive Locomotion to Predictive Action Selection – Cognitive Control for a Six-Legged Walker

Schilling, M., Paskarbeit, J., Ritter, H. J., Schneider, A., & Cruse, H. (2021) · IEEE Transactions on Robotics · doi:10.1109/tro.2021.3106832 · Cited by 15

Variation and Variability in Drosophila Grooming Behavior

Mueller, J. M., Zhang, N., Carlson, J. M., & Simpson, J. H. (2022) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2021.769372 · Cited by 15

Recording and Quantifying C. elegans Behavior

Pokala, N., & Flavell, S. W. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2181-3_20 · Cited by 14

EXPLORE: a novel deep learning-based analysis method for exploration behaviour in object recognition tests

Ibañez, V., Bohlen, L., Manuella, F., Mansuy, I. M., Helmchen, F., & Wahl, A.-S. (2023) · Scientific Reports · doi:10.1038/s41598-023-31094-w · Cited by 14

Three-dimensional markerless motion capture of multiple freely behaving monkeys toward automated characterization of social behavior

Matsumoto, J., Kaneko, T., Kimura, K., Negrete, S. B., Guo, J. Y., Suda‐Hashimoto, N., Kaneko, A., Morimoto, M., Nishimaru, H., Setogawa, T., et al. (2025) · Science Advances · doi:10.1126/sciadv.adn1355 · Cited by 14

The Promise of Behavioral Tracking Systems for Advancing Primate Animal Welfare

Knaebe, B., Weiss, C. C., Zimmermann, J., & Hayden, B. Y. (2022) · Animals · doi:10.3390/ani12131648 · Cited by 14

Leptin deficiency-caused behavioral change – A comparative analysis using EthoVision and DeepLabCut

Bühler, D., Guerra, N. P., Müller, L., Wolkenhauer, O., Düffer, M., Vollmar, B., Kuhla, A., & Wolfien, M. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1052079 · Cited by 15

Uncovering multiscale structure in the variability of larval zebrafish navigation

Sridhar, G., Vergassola, M., Marques, J. C., Orger, M. B., Costa, A. C., & Wyart, C. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2410254121 · Cited by 17

Marker-Less Motion Capture of Insect Locomotion With Deep Neural Networks Pre-trained on Synthetic Videos

Arent, I., Schmidt, F. P., Botsch, M., & Dürr, V. (2021) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2021.637806 · Cited by 13

Pupal behavior emerges from unstructured muscle activity in response to neuromodulation in Drosophila

Elliott, A. D., Berndt, A. J., Houpert, M., Roy, S., Scott, R. L., Chow, C. C., Shroff, H., & White, B. H. (2021) · eLife · doi:10.7554/elife.68656 · Cited by 13

Historical and Modern Evidence for the Role of Reward Circuitry in Emergence

Heshmati, M., & Bruchas, M. R. (2022) · Anesthesiology · doi:10.1097/aln.0000000000004148 · Cited by 15

Behavioral Activity Recognition Based on Gaze Ethograms

de Lope, J., & Graña, M. (2020) · International Journal of Neural Systems · doi:10.1142/s0129065720500252 · Cited by 13

Flexible analysis of animal behavior via time-resolved manifold embeddingPreprint

York, R. A., Carreira-Rosario, A., Giocomo, L. M., & Clandinin, T. R. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.30.321406 · Cited by 13

Foraging as an ethological framework for neuroscience

Grima, L. L., Haberkern, H., Mohanta, R., Morimoto, M. M., Rajagopalan, A. E., & Scholey, E. V. (2025) · Trends in Neurosciences · doi:10.1016/j.tins.2025.08.006 · Cited by 15

Shortwave infrared fluorescence imaging of peripheral organs in awake and freely moving mice

Arús, B. A., Cosco, E. D., Yiu, J., Balba, I., Bischof, T. S., Sletten, E. M., & Bruns†, O. T. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1135494 · Cited by 12

Methods and Measures for Investigating Microscale Motility

Bondoc, K. G. V., Laeverenz-Schlogelhofer, H., Poon, R. N., Boggon, A. K., Bentley, S. A., Cortese, D., & Wan, K. Y. (2023) · Integrative and Comparative Biology · doi:10.1093/icb/icad075 · Cited by 12

STPoseNet: A real-time spatiotemporal network model for robust mouse pose estimation

Lv, S., Wang, J., Chen, X., & Liao, X. (2024) · iScience · doi:10.1016/j.isci.2024.109772 · Cited by 12

Studying dominance and aggression requires ethologically relevant paradigms.

Shemesh, Y., Benjamin, A., Shoshani-Haye, K., Yizhar, O., & Chen, A. (2024) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2024.102879 · Cited by 10

Comprehensive ethological analysis of fear expression in rats using DeepLabCut and SimBA machine learning model

Chanthongdee, K., Fuentealba, Y., Wahlestedt, T., Foulhac, L., Kardash, T., Coppola, A., Heilig, M. A., & Barbier, E. (2024) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2024.1440601 · Cited by 12

AI-powered home cage system for real-time tracking and analysis of rodent behavior

Hsieh, C.-M., Hsu, C., Chen, J.-K., & Liao, L. (2024) · iScience · doi:10.1016/j.isci.2024.111223 · Cited by 12

Hierarchical behavioral analysis framework as a platform for standardized quantitative identification of behaviors

Ye, J., Xu, Y., Huang, K., Wang, X., Wang, L., & Wang, F. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.115239 · Cited by 12

Beyond Single Discrete Responses: An Integrative and Multidimensional Analysis of Behavioral Dynamics Assisted by Machine Learning

León, A., Hernández, V., Lopez, J., Guzmán, I., Quintero, V., Toledo, P., Avendaño‐Garrido, M. L., Hernández-Linares, C. A., & Escamilla, E. (2021) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2021.681771 · Cited by 12

Machine learning and deep learning frameworks for the automated analysis of pain and opioid withdrawal behaviors

Bumgarner, J. R., Becker‐Krail, D. D., White, R. C., & Nelson, R. J. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.953182 · Cited by 12

AVATAR: AI Vision Analysis for Three-dimensional Action in Real-timePreprint

Kim, D., Shin, A., Jeong, Y.-C., Park, S., & Kim, D. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.31.474634 · Cited by 12

From innate to instructed: A new look at perceptual decision-making

Oesch, L. T., Ryan, M. B., & Churchland, A. K. (2024) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2024.102871 · Cited by 11

Deep learning-based scoring method of the three-chamber social behaviour test in a mouse model of alcohol intoxication. A comparative analysis of DeepLabCut, commercial automatic tracking and manual scoring

Zahran, M. A., Mañas-Ojeda, A., Navarro-Sánchez, M., Castillo-Gómez, E., & Olucha-Bordonau, F. E. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e36352 · Cited by 11

How can ethology inform the neuroscience of fear, aggression and dominance?

Battivelli, D., Fan, Z., Hu, H., & Gross, C. T. (2024) · Nature reviews. Neuroscience · doi:10.1038/s41583-024-00858-2 · Cited by 11

Inferring internal states across mice and monkeys using facial features

Tlaie, A., Hay, M. Y. A. E., Mert, B., Taylor, R., Ferracci, P.-A., Shapcott, K. A., Glukhova, M., Pillow, J. W., Havenith, M. N., & Schölvinck, M. L. (2025) · Nature Communications · doi:10.1038/s41467-025-60296-1 · Cited by 11

Dimensionality of locomotor behaviors in developing C. elegans

Hassinan, C. W., Sterrett, S. C., Summy, B., Khera, A., Wang, A., & Bai, J. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011906 · Cited by 11

From Karl von Frisch to Neuroethology: A Methodological Perspective on the Frischean Tradition's Expansion into Neuroethology**

Dhein, K. (2022) · Berichte zur Wissenschaftsgeschichte · doi:10.1002/bewi.202200003 · Cited by 11

OpenLabCluster: active learning based clustering and classification of animal behaviors based on kinematic body keypoints

Li, J., Keselman, M., & Shlizerman, E. (2025) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2025.1630654 · Cited by 10

Neural signaling mechanisms in depression: bridging classical monoamine hypotheses, animal models, and emerging antidepressant strategies

Yamamoto, M., Hirakata, H., & Toda, K. (2026) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2026.1777975 · Cited by 11

The nervous system as a solution for implementing closed negative feedback control loops

Sosa, R., & Alcalá, E. (2022) · Journal of the Experimental Analysis of Behavior · doi:10.1002/jeab.736 · Cited by 10

Home-cage behavior in the Stargazer mutant mouse

Schirmer, C., Abboud, M. A., Lee, S., Bass, J. S., Mazumder, A. G., Kamen, J. L., & Krishnan, V. (2022) · Scientific Reports · doi:10.1038/s41598-022-17015-3 · Cited by 10

Habit formation viewed as structural change in the behavioral network

Yamada, K., & Toda, K. (2023) · Communications Biology · doi:10.1038/s42003-023-04500-2 · Cited by 10

Distinguishing Discrete and Continuous Behavioral Variability Using Warped Autoregressive HMMs

Costacurta, J. C., Duncker, L., Sheffer, B., Gillis, W. F., Weinreb, C., Markowitz, J. E., Datta, S. R., Williams, A., & Linderman, S. W. (2022) · neural information processing systems · doi:10.52202/068431-1731 · Cited by 11

Transcriptome Analysis of NPFR Neurons Reveals a Connection Between Proteome Diversity and Social Behavior

Ryvkin, J., Bentzur, A., Shmueli, A., Tannenbaum, M., Shallom, O., Dokarker, S., Benichou, J. I. C., Levi, M., & Shohat-Ophir, G. (2021) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2021.628662 · Cited by 10

Exploring a strongly non-Markovian animal behaviorPreprint

Alba, V. A., Berman, G. J., Bialek, W., & Shaevitz, J. W. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.15681 · Cited by 10

MouseGPT: A Large-scale Vision-Language Model for Mouse Behavior AnalysisPreprint

Xu, T. G., Zhou, T., Wang, Y., Yang, P., Tang, S., Shao, K., Tang, Z., Liu, Y., Chen, X., Liu, Y., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.27.645630 · Cited by 13

Identification of interacting neural populations: methods and statistical considerations

Kass, R. E., Bong, H., Olarinre, M., Qi, X., & Urban, K. N. (2023) · Journal of Neurophysiology · doi:10.1152/jn.00131.2023 · Cited by 9

The utility of animal models to inform the next generation of human space exploration

Duporge, I., Pereira, T., Castiello, S., Ross, J. G. B., Lee, S. J., & Hindle, A. G. (2025) · npj Microgravity · doi:10.1038/s41526-025-00460-5 · Cited by 10

Neuromodulation and the toolkit for behavioural evolution: can ecdysis shed light on an old problem?

Sullivan, L. F., Barker, M. S., Felix, P. C., Vuong, R. Q., & White, B. H. (2022) · FEBS Journal · doi:10.1111/febs.16650 · Cited by 9

Searching for a paradigm shift in the research on the epilepsies and associated neuropsychiatric comorbidities. From ancient historical knowledge to the challenge of contemporary systems complexity and emergent functions

Garcia‐Cairasco, N., Gondim, G. G. P., & Tejada, J. (2021) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2021.107930 · Cited by 9

Next generation behavioral sequencing for advancing pain quantification

Jhumka, A., & Abdus-Saboor, I. (2022) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2022.102598 · Cited by 9

A reductionist paradigm for high-throughput behavioural fingerprinting in Drosophila melanogaster

Jones, H., Willis, J. A., Firth, L. C., Giachello, C. N., & Gilestro, G. F. (2023) · eLife · doi:10.7554/elife.86695 · Cited by 8

Rapid automated 3-D pose estimation of larval zebrafish using a physical model-trained neural network

Ravan, A., Feng, R., Gruebele, M., & Chemla, Y. R. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011566 · Cited by 8

An ethologically motivated neurobiology of primate visually-guided reach-to-grasp behavior

Mitchell, J. F., Wang, K. H., Batista, A. P., & Miller, C. T. (2024) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2024.102872 · Cited by 8

Assessing the effects of 5-HT2A and 5-HT5A receptor antagonists on DOI-induced head-twitch response in male rats using marker-less deep learning algorithms

Cyrano, E., & Popik, P. (2024) · Pharmacological Reports · doi:10.1007/s43440-024-00679-1 · Cited by 8

Thermal modulation of Zebrafish exploratory statistics reveals constraints on individual behavioral variabilityPreprint

Goc, G. L., Lafaye, J., Karpenko, S., Bormuth, V., Candelier, R., & Debrégeas, G. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.17.435787 · Cited by 8

An integrated and scalable rodent cage system enabling continuous computer vision-based behavioral analysis and AI-enhanced digital biomarker developmentPreprint

Robertson, T. L., Ellis, M., Bratcher-Petersen, N., Ruidíaz, M. E., Harada, K., Toburen, D., Oberhauser, J. P., Grzenda, D., Peltier, N. E., Raza, M., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.18.629281 · Cited by 8

Promises and challenges of human computational ethology

Mobbs, D., Wise, T., Suthana, N., Guzman, N., Kriegeskorte, N., & Leibo, J. Z. (2021) · Neuron · doi:10.1016/j.neuron.2021.05.021 · Cited by 86

Natural behavior is the language of the brain

Miller, C. T., Gire, D. H., Hoke, K. L., Huk, A. C., Kelley, D. B., Leopold, D. A., Smear, M. C., Theunissen, F. E., Yartsev, M. M., & Niell, C. M. (2022) · Current Biology · doi:10.1016/j.cub.2022.03.031 · Cited by 179

Neuroethology: Methods

Burmeister, S. S. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-809633-8.20817-0

Quantifying Behavior Using Deep Learning

Maree, L., Leonardis, E. J., Gepshtein, S., Albright, T. D., Hitchcock, K., Andrews, N. J., Azim, E., Pfaff, S. L., Metallo, C. M., & Pereira, T. (2023) · Biological Psychiatry · doi:10.1016/j.biopsych.2023.02.038 · Cited by 1

Toward a neuroscience of natural behavior

Cisek, P., & Green, A. M. (2024) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2024.102859 · Cited by 97