Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

A Short Review of Some Aspects of Computational Neuroethology

  • Conference paper
  • First Online:
Understanding the Brain Function and Emotions (IWINAC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11486))

Abstract

Computational Neuroethology comprises a wide variety of devices, computational tools and techniques used in studies aiming to understand the neural substrate of the observable behavior. In this short review we focus on the description of available computational tools in a landscape of resources that is steadily growing as the scientific community recognizes this Computational Neuroethology as one of the frontiers of scientific endeavor. We comment on the biological basis and some examples of studies reported in the literature before providing a description and taxonomy of resources and tools.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    http://www.ansc.purdue.edu/USDA-LBRU/vdb/video3.htm.

  2. 2.

    http://www.vision.caltech.edu/Video_Datasets/CRIM13/CRIM13/Main.html.

  3. 3.

    http://cbcl.mit.edu/software-datasets/mouse/.

  4. 4.

    https://www.harvardapparatus.com/smart-video-tracking-system.html.

  5. 5.

    https://www.noldus.com/animal-behavior-research/products/ethovision-xt.

References

  1. Aguzzi, J., Costa, C., Fujiwara, Y., Iwase, R., Ramirez-Llorda, E., Menesatti, P.: A novel morphometry-based protocol of automated video-image analysis for species recognition and activity rhythms monitoring in deep-sea fauna. Sensors 9(11), 8438–8455 (2009)

    Article  Google Scholar 

  2. Akkaya, B., Tabar, Y.R., Gharbalchi, F., Ulusoy, I., Halici, U.: Tracking mice face in video. In: 20th National Biomedical Engineering Meeting (BIYOMUT), pp. 1–4, November 2016

    Google Scholar 

  3. Akkaya, İ.B., Halici, U.: Mouse face tracking using convolutional neural networks. IET Comput. Vis. 12(2), 153–161 (2018)

    Article  Google Scholar 

  4. Anderson, D.J., Adolphs, R.: A framework for studying emotions across species. Cell 157(1), 187–200 (2014)

    Article  Google Scholar 

  5. Andrienko, G., et al.: Visual analysis of pressure in football. Data Min. Knowl. Discov. 31(6), 1793–1839 (2017)

    Article  MathSciNet  Google Scholar 

  6. Arbib, M.A.: Rana computatrix to human language: towards a computational neuroethology of language evolution. Philos. Trans. R. Soc. Lond. A: Math. Phys. Eng. Sci. 361(1811), 2345–2379 (2003)

    Article  MathSciNet  Google Scholar 

  7. Bains, R.S., et al.: Assessing mouse behaviour throughout the light/dark cycle using automated in-cage analysis tools. J. Neurosci. Methods 300, 37–47 (2018). Measuring Behaviour 2016

    Article  Google Scholar 

  8. Benice, T.S., Raber, J.: Object recognition analysis in mice using nose-point digital video tracking. J. Neurosci. Methods 168(2), 422–430 (2008)

    Article  Google Scholar 

  9. Bolles, R.C., Fanselow, M.S.: A perceptual-defensive-recuperative model of fear and pain. Behav. Brain Sci. 3(2), 291–301 (1980)

    Article  Google Scholar 

  10. Brown, A.E.X., Yemini, E.I., Grundy, L.J., Jucikas, T., Schafer, W.R.: A dictionary of behavioral motifs reveals clusters of genes affecting caenorhabditis elegans locomotion. Proc. Natl. Acad. Sci. 110(2), 791–796 (2013)

    Article  Google Scholar 

  11. Burgos-Artizzu, X.P., Dollár, P., Lin, D., Anderson, D.J., Perona, P.: Social behavior recognition in continuous video. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1322–1329, June 2012

    Google Scholar 

  12. Carreno, M.I., et al.: First approach to the analysis of spontaneous activity of mice based on permutation entropy. In: 2015 4th International Work Conference on Bioinspired Intelligence (IWOBI), pp. 197–204, June 2015

    Google Scholar 

  13. Cha, B.J., Bae, B.S., Cho, S.K., Oh, J.K.: A simple method to quantify fish behavior by forming time-lapse images. Aquac. Eng. 51, 15–20 (2012)

    Article  Google Scholar 

  14. Cho, H.-J., et al.: Newly developed method for mouse olfactory behavior tests using an automatic video tracking system. Auris Nasus Larynx 45(1), 103–110 (2018)

    Article  Google Scholar 

  15. Conklin, E.E., Lee, K.L., Schlabach, S.A., Woods, I.G.: Videohacking: automated tracking and quantification of locomotor behavior with open source software and off-the-shelf video equipment. J. Undergrad. Neurosci. Educ. 13(3), A120–A125 (2015). PMID: 26240518

    Google Scholar 

  16. Cronin, C.J., Feng, Z., Schafer, W.R.: Automated Imaging of C. elegans Behavior, pp. 241–251. Humana Press, Totowa (2006)

    Google Scholar 

  17. Dankert, H., Wang, L., Hoopfer, E.D., Anderson, D.J., Perona, P.: Automated monitoring and analysis of social behavior in drosophila. Nat. Methods 6, 297 (2009)

    Article  Google Scholar 

  18. Dell, A.I., et al.: Automated image-based tracking and its application in ecology. Trends Ecol. Evol. 29(7), 417–428 (2014)

    Article  Google Scholar 

  19. Desland, F.A., Afzal, A., Warraich, Z., Mocco, J.: Manual versus automated rodent behavioral assessment: comparing efficacy and ease of Bederson and Garcia neurological deficit scores to an open field video-tracking system. J. Cent. Nerv. Syst. Dis. 6, 7–14 (2014). PMID: 24526841

    Article  Google Scholar 

  20. Eyjolfsdottir, Eyrun, et al.: Detecting social actions of fruit flies. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8690, pp. 772–787. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10605-2_50

    Chapter  Google Scholar 

  21. Fanselow, M.S., Lester, L.S.: A functional behavioristic approach to aversively motivated behavior: predatory imminence as a determinant of the topography of defensive behavior. In: Bolles, R.C., Beecher, M.D. (eds.) Evol. Learn., pp. 185–212. Lawrence Erlbaum Associates Inc., Hillsdale (1988)

    Google Scholar 

  22. Fontaine, E., et al.: Automated visual tracking for studying the ontogeny of zebrafish swimming. J. Exp. Biol. 211(8), 1305–1316 (2008)

    Article  Google Scholar 

  23. Manuel Graña for CybSPEED: On The Proposed Cybspeed Project Experimental Research Protocols. Zenodo (2018). https://doi.org/10.5281/zenodo.1405505. Accessed Aug 2018

  24. Fournely, M., Petit, Y., Wagnac, É., Laurin, J., Callot, V., Arnoux, P.-J.: High-speed video analysis improves the accuracy of spinal cord compression measurement in a mouse contusion model. J. Neurosci. Methods 293, 1–5 (2018)

    Article  Google Scholar 

  25. Fröhlich, H., Claes, K., De Wolf, C., Van Damme, X., Michel, A.: A machine learning approach to automated gait analysis for the Noldus catwalk system. IEEE Trans. Biomed. Eng. 65(5), 1133–1139 (2018)

    Google Scholar 

  26. Hong, W., Kim, D.-W., Anderson, D.J.: Antagonistic control of social versus repetitive self-grooming behaviors by separable amygdala neuronal subsets. Cell 158(6), 1348–1361 (2014)

    Article  Google Scholar 

  27. Idei, H., Murata, S., Chen, Y., Yamashita, Y., Tani, J., Ogata, T.: Reduced behavioral flexibility by aberrant sensory precision in autism spectrum disorder: a neurorobotics experiment. In: 2017 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), pp. 271–276, September 2017

    Google Scholar 

  28. Jhuang, H., et al.: Automated home-cage behavioural phenotyping of mice. Nat. Commun. 1, 68 (2010)

    Article  Google Scholar 

  29. Kabra, M., Robie, A.A., Rivera-Alba, M., Branson, S., Branson, K.: JAABA: interactive machine learning for automatic annotation of animal behavior. Nat. Methods 10, 64 (2012)

    Article  Google Scholar 

  30. Kearns, W.D., Fozard, J.L., Nams, V.O.: Movement path tortuosity in free ambulation: relationships to age and brain disease. IEEE J. Biomed. Health Inform. 21(2), 539–548 (2017)

    Article  Google Scholar 

  31. Kelso, J.A.S., Dumas, G., Tognoli, E.: Outline of a general theory of behavior and brain coordination. Neural Netw. 37, 120–131 (2013). Twenty-fifth Anniversay Commemorative Issue

    Article  Google Scholar 

  32. Cario, C.L., Farrell, T.C., Milanese, C., Burton, E.A.: Automated measurement of zebrash larval movement. J. Physiol. 589(15), 3703–3708 (2011)

    Article  Google Scholar 

  33. (Sam) Ma, Z.: Towards computational models of animal cognition, an introduction for computer scientists. Cognit. Syst. Res. 33, 42–69 (2015)

    Google Scholar 

  34. Menzel, R., Greggers, U.: The memory structure of navigation in honeybees. J. Comp. Physiol. A 201(6), 547–561 (2015)

    Article  Google Scholar 

  35. Mobbs, D.: Foraging under competition: the neural basis of input-matching in humans. J. Neurosci. 33(23), 9866–9872 (2013)

    Article  Google Scholar 

  36. Mobbs, D., Kim, J.J.: Neuroethological studies of fear, anxiety, and risky decision-making in rodents and humans. Curr. Opin. Behav. Sci. 5, 8–15 (2015). Neuroeconomics

    Article  Google Scholar 

  37. Morrow-Tesch, J., Dailey, J.W., Jiang, H.: A video data base system for studying animal behavior. J. Anim. Sci. 76(10), 2605–2608 (1998)

    Article  Google Scholar 

  38. Muto, A., Lal, P., Ailani, D., Abe, G., Itoh, M., Kawakami, K.: Activation of the hypothalamic feeding centre upon visual prey detection. Nat. Commun. 8, 15029 (2017)

    Article  Google Scholar 

  39. Obdrzálek, S.: Accuracy and robustness of kinect pose estimation in the context of coaching of elderly population. In: Conference Proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, pp. 1188–1193 (2012)

    Google Scholar 

  40. Ohayon, S., Avni, O., Taylor, A.L., Perona, P., Roian, S.E.: Automated multi-day tracking of marked mice for the analysis of social behaviour. J. Neurosci. Methods 219(1), 10–19 (2013)

    Article  Google Scholar 

  41. Papadakis, V.M., Papadakis, I.E., Lamprianidou, F., Glaropoulos, A., Kentouri, M.: A computer-vision system and methodology for the analysis of fish behavior. Aquac. Eng. 46, 53–59 (2012)

    Article  Google Scholar 

  42. Pérez-Escudero, A., Vicente-Page, J., Hinz, R.C., Arganda, S., de Polavieja, G.G.: idtracker: tracking individuals in a group by automatic identification of unmarked animals. Nat. Methods 11, 743 (2014)

    Article  Google Scholar 

  43. Pham, J., Cabrera, S.M., Sanchis-Segura, C., Wood, M.A.: Automated scoring of fear-related behavior using ethovision software. J. Neurosci. Methods 178(2), 323–326 (2009)

    Article  Google Scholar 

  44. Riley, J.R.: Tracking bees with harmonic radar. Nature 379, 29 (1996)

    Article  Google Scholar 

  45. Saberioon, M.M., Cisar, P.: Automated multiple fish tracking in three-dimension using a structured light sensor. Comput. Electron. Agric. 121, 215–221 (2016)

    Article  Google Scholar 

  46. Shotton, J., et al.: Real-time human pose recognition in parts from single depth images. In: Cipolla, R., Battiato, S., Farinella, G. (eds.) Machine Learning for Computer Vision. Studies in Computational Intelligence, vol. 411, pp. 119–135. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-28661-2_5

    Chapter  Google Scholar 

  47. Sminchisescu, C., Kanaujia, A., Li, Z., Metaxas, D.: Conditional models for contextual human motion recognition. In: Tenth IEEE International Conference on Computer Vision (ICCV 2005), vol. 1, vol. 2, pp. 1808–1815, October 2005

    Google Scholar 

  48. Squire, L.R.: Encyclopedia of Neuroscience. In: Encyclopedia of Neuroscience, vol. 3. Elsevier/Academic Press (2009)

    Google Scholar 

  49. Stafstrom, J.A., Michalik, P., Hebets, E.A.: Sensory system plasticity in a visually specialized, nocturnal spider. Sci. Rep. 7, 46627 (2017)

    Article  Google Scholar 

  50. Stewart, A.M.: A novel 3D method of locomotor analysis in adult zebrafish. J. Neurosci. Methods 255, 66–74 (2015)

    Article  Google Scholar 

  51. Stone, E.E., Skubic, M.: Unobtrusive, continuous, in-home gait measurement using the microsoft kinect. IEEE Trans. Biomed. Eng. 60(10), 2925–2932 (2013)

    Article  Google Scholar 

  52. Tang, B.: An in vivo study of hypoxia-inducible factor-1\(\alpha \) signaling in ginsenoside Rg1-mediated brain repair after hypoxia/ischemia brain injury. Pediatr. Res. 81, 120 (2016)

    Article  Google Scholar 

  53. Todd, P.A.C., McCue, H.V., Haynes, L.P., Barclay, J.W., Burgoyne, R.D.: Interaction of ARF-1.1 and neuronal calcium sensor-1 in the control of the temperature-dependency of locomotion in caenorhabditis elegans. Sci. Rep. 6, 30023 (2016)

    Article  Google Scholar 

  54. Tsai, H.-Y., Huang, Y.-W.: Image tracking study on courtship behavior of drosophila. PLoS One 7(4), 1–8 (2012)

    Google Scholar 

  55. Urgen, B., Plank, M., Ishiguro, H., Poizner, H., Saygin, A.: EEG theta and Mu oscillations during perception of human and robot actions. Front. Neurorobotics 7, 19 (2013)

    Article  Google Scholar 

  56. Wang, Y.-N.: Behavioural screening of zebrafish using neuroactive traditional Chinese medicine prescriptions and biological targets. Sci. Rep. 4, 5311 (2014)

    Article  Google Scholar 

  57. Wario, F., Wild, B., Couvillon, M., Rojas, R., Landgraf, T.: Automatic methods for long-term tracking and the detection and decoding of communication dances in honeybees. Front. Ecol. Evol. 3, 103 (2015)

    Article  Google Scholar 

  58. Zhao, J., et al.: Modified motion influence map and recurrent neural network-based monitoring of the local unusual behaviors for fish school in intensive aquaculture. Aquaculture 493, 165–175 (2018)

    Article  Google Scholar 

  59. Zhu, L., Weng, W.: Catadioptric stereo-vision system for the real-time monitoring of 3D behavior in aquatic animals. Physiol. Behav. 91(1), 106–119 (2007)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computational Intelligence Group, University of the Basque Country, (UPV/EHU), San Sebastián, Spain

    Manuel Graña & Javier de Lope Asiain

  2. Polytechnical University Madrid (UPM), Madrid, Spain

    Manuel Graña & Javier de Lope Asiain

Authors
  1. Manuel Graña
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Javier de Lope Asiain
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manuel Graña .

Editor information

Editors and Affiliations

  1. Universidad Politécnica de Cartagena, Cartagena, Spain

    José Manuel Ferrández Vicente

  2. Universidad Nacional de Educación a Distancia, Madrid, Spain

    José Ramón Álvarez-Sánchez

  3. Universidad Nacional de Educación a Distancia, Madrid, Madrid, Spain

    Félix de la Paz López

  4. Universidad Politécnica de Cartagena, Cartagena, Spain

    Javier Toledo Moreo

  5. The Ohio State University, Columbus, OH, USA

    Hojjat Adeli

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Graña, M., de Lope Asiain, J. (2019). A Short Review of Some Aspects of Computational Neuroethology. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Understanding the Brain Function and Emotions. IWINAC 2019. Lecture Notes in Computer Science(), vol 11486. Springer, Cham. https://doi.org/10.1007/978-3-030-19591-5_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-19591-5_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-19590-8

  • Online ISBN: 978-3-030-19591-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation