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Measuring behavior across scales
Gordon J. Berman
BMC Biology · 2018 · Review · CC BY
Abstract
The need for high-throughput, precise, and meaningful methods for measuring behavior has been amplified by our recent successes in measuring and manipulating neural circuitry. The largest challenges associated with moving in this direction, however, are not technical but are instead conceptual: what numbers should one put on the movements an animal is performing (or not performing)? In this review, I will describe how theoretical and data analytical ideas are interfacing with recently-developed computational and experimental methodologies to answer these questions across a variety of contexts, length scales, and time scales. I will attempt to highlight commonalities between approaches and areas where further advances are necessary to place behavior on the same quantitative footing as other scientific fields.
References 111
- 1.
Anderson DJ, Perona P. Toward a Science of Computational Ethology. Neuron. 2014;84(1):18-31.
Not in our library10.1016/j.neuron.2014.09.005
- 2.
Krakauer JW, Ghazanfar AA, Gomez-Marin A, Maciver MA, Poeppel D. Neuroscience Needs Behavior: Correcting a Reductionist Bias. Neuron. 2017;93(3):480-90.
Not in our library10.1016/j.neuron.2016.12.041
- 3.
Wang XJ, Krystal JH. Computational psychiatry. Neuron. 2014;84(3):638-54.
Not in our library10.1016/j.neuron.2014.10.018
- 4.
Lebedev MA, Nicolelis MAL. Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. Physiol Rev. 2017;97(2):767-837.
Not in our library10.1152/physrev.00027.2016
- 5.
Anderson PW. More Is Different. Science. 1972;177(4047):393-6.
Not in our library10.1126/science.177.4047.393
- 6.
Goldenfeld N, Kadanoff LP. Simple Lessons from Complexity. Science. 1999;284(5411):87-9.
Not in our library10.1126/science.284.5411.87
- 7.
Dell AI, Bender JA, Branson K, Couzin ID, De Polavieja GG, Noldus LPJJ, et al. Automated image-based tracking and its application in ecology. Trends Ecol Evol. 2014;29(7):417-28.
Not in our library10.1016/j.tree.2014.05.004
- 8.
Egnor SER, Branson K. Computational Analysis of Behavior. Ann Rev Neuro. 2016;39:217-36.
Not in our library10.1146/annurev-neuro-070815-013845
- 9.
Robie AA, Seagraves KM, Egnor SER, Branson K. Machine vision methods for analyzing social interactions. J Exp Bio. 2017;220(1):25-34.
Not in our library10.1242/jeb.142281
- 10.
Calhoun AJ, Murthy M. Quantifying behavior to solve sensorimotor transformations: advances from worms and flies. Curr Opin Neurobiol. 2017;46:90-8.
Not in our library10.1016/j.conb.2017.08.006
- 11.
Stephens GJ, Osborne LC, Bialek W. Searching for simplicity in the analysis of neurons and behavior. Proc Nat Acad Sci. 2011;108(Supp 3):15565-71.
Not in our library10.1073/pnas.1010868108
- 12.
Gomez-Marin A, Paton JJ, Kampff AR, Costa RM, Mainen ZF. Big behavioral data: psychology, ethology and the foundations of neuroscience. Nat Neurosci. 2014;17(11):1455-62.
Not in our library10.1038/nn.3812
- 13.
Montague PR, Dolan RJ, Friston KJ, Dayan P. Computational psychiatry. Trends Cogn Sci. 2012;16(1):72-80.
Not in our library10.1016/j.tics.2011.11.018
- 14.
Anderson DJ, Adolphs R. A Framework for Studying Emotions across Species. Cell. 2014;157(1):187-200.
Not in our library10.1016/j.cell.2014.03.003
- 15.
Anderson DJ. Circuit modules linking internal states and social behaviour in flies and mice. Nature Rev Neuro. 2016;17(11):692-704.
Not in our library10.1038/nrn.2016.125
- 16.
Huys QJM, Maia TV, Frank MJ. Computational psychiatry as a bridge from neuroscience to clinical applications. Nat Neurosci. 2016;19(3):404-13.
Not in our library10.1038/nn.4238
- 17.
Stephan KE, Mathys C. Computational approaches to psychiatry. Curr Opin Neurobiol. 2014;25:85-92.
Not in our library10.1016/j.conb.2013.12.007
- 18.
Leshner A, Pfaff DW. Quantification of behavior. Proc Nat Acad Sci. 2011;108(Supp 3):15537-41.
Not in our library10.1073/pnas.1010653108
- 19.
Fonio E, Golani I, Benjamini Y. Measuring behavior of animal models: faults and remedies. Nat Methods. 2012;9(12):1167-70.
Not in our library10.1038/nmeth.2252
- 20.
Altmann J. Observational study of behavior: sampling methods. Behaviour. 1974;49(3):227-67.
Not in our library10.1163/156853974x00534
- 21.
Martin P, Bateson P. Measuring behaviour: an introductory guide. Cambridge. 2007.
Not in our library
- 22.
Gao P, Ganguli S. On simplicity and complexity in the brave new world of large-scale neuroscience. Curr Opin Neurobiol. 2015;32:148-55.
Not in our library10.1016/j.conb.2015.04.003
- 23.
Panda S, Hogenesch JB, Kay SA. Circadian rhythms from flies to human. Nature. 2002;417(6886):329-35.
Not in our library10.1038/417329a
- 24.
Yemini E, Jucikas T, Grundy LJ, Brown AEX, Schafer WR. A database of Caenorhabditis elegans behavioral phenotypes. Nat Methods. 2013;10(9):877-9.
Not in our library10.1038/nmeth.2560
- 25.
- 26.
Ayroles JF, Buchanan SM, O’Leary C, Skutt-Kakaria K, Grenier JK, Clark AG, et al. Behavioral idiosyncrasy reveals genetic control of phenotypic variability. Proc Nat Acad Sci. 2015;112(21):6706-11.
Not in our library10.1073/pnas.1503830112
- 27.
Branson K, Robie AA, Bender JA, Perona P, Dickinson MH. High-throughput ethomics in large groups of Drosophila. Nat Methods. 2009;6(6):451-7.
Not in our library10.1038/nmeth.1328
- 28.
Pérez-Escudero A, Vicente-Page J, Hinz RC, Arganda S, De Polavieja GG. idTracker: tracking individuals in a group by automatic identification of unmarked animals. Nat Methods. 2014;11(7):743-8.
Not in our library10.1038/nmeth.2994
- 29.
Naumann EA, Fitzgerald JE, Dunn TW, Rihel J, Sompolinsky H, Engert F. From Whole-Brain Data to Functional Circuit Models: The Zebrafish Optomotor Response. Cell. 2016;167(4):947-960.e20.
Not in our library10.1016/j.cell.2016.10.019
- 30.
Orger MB, De Polavieja GG. Zebrafish Behavior: Opportunities and Challenges. Ann Rev Neuro. 2017;40:125-147.
Not in our library10.1146/annurev-neuro-071714-033857
- 31.
Dankert H, Wang L, Hoopfer ED, Anderson DJ, Perona P. Automated monitoring and analysis of social behavior in Drosophila. Nat Methods. 2009;6(4):297-303.
Not in our library10.1038/nmeth.1310
- 32.
Kabra M, Robie AA, Rivera-Alba M, Branson S, Branson K. JAABA: interactive machine learning for automatic annotation of animal behavior. Nat Methods. 2013;10(1):64-7.
Not in our library10.1038/nmeth.2281
- 33.
de Chaumont F, Coura RDS, Serreau P, Cressant A, Chabout J, Granon S, et al. Computerized video analysis of social interactions in mice. Nat Methods. 2012;9(4):410-7.
Not in our library10.1038/nmeth.1924
- 34.
Kain J, Stokes C, Gaudry Q, Song X, Foley J, Wilson R, et al. Leg-tracking and automated behavioural classification in Drosophila. Nat Communications. 2013;4:1910.
Not in our library10.1038/ncomms2908
- 35.
Hong W, Kennedy A, Burgos-Artizzu XP, Zelikowsky M, Navonne SG, Perona P, et al. Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning. Proc Nat Acad Sci. 2015;112(38):E5351-E5360.
Not in our library10.1073/pnas.1515982112
- 36.
Dickinson MH, Farley CT, Full RJ, Koehl MAR, Kram R, Lehman S. How Animals Move: An Integrative View. Science. 2000;288(5):100-6.
Not in our library10.1126/science.288.5463.100
- 37.
Childress S, Hosoi A, Schultz WW, Wang ZJ. Natural locomotion in fluids and on surfaces: swimming, flying, and sliding. 2012.
Not in our library
- 38.
Holmes P, Full RJ, Koditschek D, Guckenheimer J. The Dynamics of Legged Locomotion: Models, Analyses, and Challenges. SIAM Rev. 2006;48:207-304.
Not in our library
- 39.
Miller LA, Goldman DI, Hedrick TL, Tytell ED, Wang ZJ, Yen J, et al. Using computational and mechanical models to study animal locomotion. Int Comp Bio. 2012;52(5):553-75.
Not in our library10.1093/icb/ics115
- 40.
McInroe B, Astley HC, Gong C, Kawano SM, Schiebel PE, Rieser JM, et al. Tail use improves soft substrate performance in models of early vertebrate land locomotors. Science. 2016;353(6295):154-8.
Not in our library10.1126/science.aaf0984
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