High-Throughput Automatic Training System for Spatial Working Memory in Free-Moving Mice

  • Shimin Zou
  • Chengyu Tony LiEmail author


Efficient behavioral assays are crucial for understanding the neural mechanisms of cognitive functions. Here, we designed a high-throughput automatic training system for spatial cognition (HASS) for free-moving mice. Mice were trained to return to the home arm and remain there during a delay period. Software was designed to enable automatic training in all its phases, including habituation, shaping, and learning. Using this system, we trained mice to successfully perform a spatially delayed nonmatch to sample task, which tested spatial cognition, working memory, and decision making. Performance depended on the delay duration, which is a hallmark of working memory tasks. The HASS enabled a human operator to train more than six mice simultaneously with minimal intervention, therefore greatly enhancing experimental efficiency and minimizing stress to the mice. Combined with the optogenetic method and neurophysiological techniques, the HASS will be useful in deciphering the neural circuitry underlying spatial cognition.


Cognitive functions Automatic training Free-moving mice Working memory Spatial cognition 



The work was supported by the Instrument Developing Project of the Chinese Academy of Sciences (YZ201540), the National Science Foundation for Distinguished Young Scholars of China (31525010), the General Program of the National Science Foundation of China (31471049), the Key Research Project of Frontier Science of the Chinese Academy of Sciences (QYZDB-SSW-SMC009), China – Netherlands CAS-NWO Programme: Joint Research Projects, The Future of Brain and Cognition (153D31KYSB20160106), the Key Project of Shanghai Science and Technology Commission (15JC1400102, 16JC1400101), and the State Key Laboratory of Neuroscience, China. We thank Xuehan Zhou and Dr. Ding Liu for assistance with designing the system and for training protocol optimization, and Dr. Xiaoxing Zhang for suggestions about programming.


  1. 1.
    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: 1455–1462.CrossRefGoogle Scholar
  2. 2.
    Baddeley A. Working memory: theories, models, and controversies. Annu Rev Psychol 2012, 63: 1–29.CrossRefGoogle Scholar
  3. 3.
    Bai W, Liu T, Yi H, Li S, Tian X. Anticipatory activity in rat medial prefrontal cortex during a working memory task. Neurosci Bull 2012, 28: 693–703.CrossRefGoogle Scholar
  4. 4.
    Gold JI, Shadlen MN. The neural basis of decision making. Annu Rev Neurosci 2007, 30: 535–574.CrossRefGoogle Scholar
  5. 5.
    Lee D, Seo H, Jung MW. Neural basis of reinforcement learning and decision making. Annu Rev Neurosci 2012, 35: 287–308.CrossRefGoogle Scholar
  6. 6.
    O’Keefe J, Dostrovsky J. The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat. Brain Res 1971, 34: 171–175.Google Scholar
  7. 7.
    Fyhn M, Molden S, Witter MP, Moser EI, Moser MB. Spatial representation in the entorhinal cortex. Science 2004, 305: 1258–1264.CrossRefGoogle Scholar
  8. 8.
    Wood RA, Bauza M, Krupic J, Burton S, Delekate A, Chan D, et al. The honeycomb maze provides a novel test to study hippocampal-dependent spatial navigation. Nature 2018, 554: 102–105.CrossRefGoogle Scholar
  9. 9.
    Fernando AB, Robbins TW. Animal models of neuropsychiatric disorders. Annu Rev Clin Psychol 2011, 7: 39–61.CrossRefGoogle Scholar
  10. 10.
    Gotz J, Ittner LM. Animal models of Alzheimer’s disease and frontotemporal dementia. Nat Rev Neurosci 2008, 9: 532–544.CrossRefGoogle Scholar
  11. 11.
    Nestler EJ, Hyman SE. Animal models of neuropsychiatric disorders. Nat Neurosci 2010, 13: 1161–1169.CrossRefGoogle Scholar
  12. 12.
    Schaefer AT, Claridge-Chang A. The surveillance state of behavioral automation. Curr Opin Neurobiol 2012, 22: 170–176.CrossRefGoogle Scholar
  13. 13.
    Fenno L, Yizhar O, Deisseroth K. The development and application of optogenetics. Annu Rev Neurosci 2011, 34: 389–412.CrossRefGoogle Scholar
  14. 14.
    Armbruster BN, Li X, Pausch MH, Herlitze S, Roth BL. Evolving the lock to fit the key to create a family of G protein-coupled receptors potently activated by an inert ligand. Proc Natl Acad Sci U S A 2007, 104: 5163–5168.CrossRefGoogle Scholar
  15. 15.
    Deisseroth K, Schnitzer MJ. Engineering approaches to illuminating brain structure and dynamics. Neuron 2013, 80: 568–577.CrossRefGoogle Scholar
  16. 16.
    Davidson AB, Davis DJ, Cook L. A rapid automatic technique for generating operant key-press behavior in rats. J Exp Anal Behav 1971, 15: 123–127.CrossRefGoogle Scholar
  17. 17.
    Benkner B, Mutter M, Ecke G, Munch TA. Characterizing visual performance in mice: an objective and automated system based on the optokinetic reflex. Behav Neurosci 2013, 127: 788–796.CrossRefGoogle Scholar
  18. 18.
    de Visser L, van den Bos R, Spruijt BM. Automated home cage observations as a tool to measure the effects of wheel running on cage floor locomotion. Behav Brain Res 2005, 160: 382–388.CrossRefGoogle Scholar
  19. 19.
    Kretschmer F, Kretschmer V, Kunze VP, Kretzberg J. OMR-arena: automated measurement and stimulation system to determine mouse visual thresholds based on optomotor responses. PLoS One 2013, 8: e78058.CrossRefGoogle Scholar
  20. 20.
    Han Z, Zhang X, Zhu J, Chen Y, Li CT. High–throughput automatic training system for odor-based learned behaviors in head-fixed mice. Front Neural Circuits 2018, 12.Google Scholar
  21. 21.
    Kazdoba TM, Del Vecchio RA, Hyde LA. Automated evaluation of sensitivity to foot shock in mice: inbred strain differences and pharmacological validation. Behav Pharmacol 2007, 18: 89–102.CrossRefGoogle Scholar
  22. 22.
    Roughan JV, Wright-Williams SL, Flecknell PA. Automated analysis of postoperative behaviour: assessment of HomeCageScan as a novel method to rapidly identify pain and analgesic effects in mice. Lab Anim 2009, 43: 17–26.CrossRefGoogle Scholar
  23. 23.
    Anagnostaras SG, Wood SC, Shuman T, Cai DJ, Leduc AD, Zurn KR, et al. Automated assessment of pavlovian conditioned freezing and shock reactivity in mice using the video freeze system. Front Behav Neurosci 2010, 4.Google Scholar
  24. 24.
    Kopec CD, Kessels HW, Bush DE, Cain CK, LeDoux JE, Malinow R. A robust automated method to analyze rodent motion during fear conditioning. Neuropharmacology 2007, 52: 228–233.CrossRefGoogle Scholar
  25. 25.
    Balci F, Oakeshott S, Shamy JL, El-Khodor BF, Filippov I, Mushlin R, et al. High-throughput automated phenotyping of two genetic mouse models of Huntington’s disease. PLoS Curr 2013, 5.Google Scholar
  26. 26.
    Jhuang H, Garrote E, Mutch J, Yu X, Khilnani V, Poggio T, et al. Automated home-cage behavioural phenotyping of mice. Nat Commun 2010, 1: 68.CrossRefGoogle Scholar
  27. 27.
    Hubener J, Casadei N, Teismann P, Seeliger MW, Bjorkqvist M, von Horsten S, et al. Automated behavioral phenotyping reveals presymptomatic alterations in a SCA3 genetrap mouse model. J Genet Genomics 2012, 39: 287–299.CrossRefGoogle Scholar
  28. 28.
    Aarts E, Maroteaux G, Loos M, Koopmans B, Kovacevic J, Smit AB, et al. The light spot test: Measuring anxiety in mice in an automated home-cage environment. Behav Brain Res 2015, 294: 123–130.CrossRefGoogle Scholar
  29. 29.
    Adamah-Biassi EB, Stepien I, Hudson RL, Dubocovich ML. Automated video analysis system reveals distinct diurnal behaviors in C57BL/6 and C3H/HeN mice. Behav Brain Res 2013, 243: 306–312.CrossRefGoogle Scholar
  30. 30.
    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 Natl Acad Sci U S A 2015, 112: E5351–5360.CrossRefGoogle Scholar
  31. 31.
    Weissbrod A, Shapiro A, Vasserman G, Edry L, Dayan M, Yitzhaky A, et al. Automated long-term tracking and social behavioural phenotyping of animal colonies within a semi-natural environment. Nat Commun 2013, 4: 2018.Google Scholar
  32. 32.
    Ohayon S, Avni O, Taylor AL, Perona P, Roian Egnor SE. Automated multi-day tracking of marked mice for the analysis of social behaviour. J Neurosci Methods 2013, 219: 10–19.CrossRefGoogle Scholar
  33. 33.
    Reiss D, Walter O, Bourgoin L, Kieffer BL, Ouagazzal AM. New automated procedure to assess context recognition memory in mice. Psychopharmacology (Berl) 2014, 231: 4337–4347.CrossRefGoogle Scholar
  34. 34.
    Remmelink E, Loos M, Koopmans B, Aarts E, van der Sluis S, Smit AB, et al. A 1-night operant learning task without food-restriction differentiates among mouse strains in an automated home-cage environment. Behav Brain Res 2015, 283: 53–60.CrossRefGoogle Scholar
  35. 35.
    Becker AM, Meyers E, Sloan A, Rennaker R, Kilgard M, Goldberg MP. An automated task for the training and assessment of distal forelimb function in a mouse model of ischemic stroke. J Neurosci Methods 2016, 258: 16–23.CrossRefGoogle Scholar
  36. 36.
    Erlich JC, Bialek M, Brody CD. A cortical substrate for memory-guided orienting in the rat. Neuron 2011, 72: 330–343.CrossRefGoogle Scholar
  37. 37.
    Poddar R, Kawai R, Olveczky BP. A fully automated high-throughput training system for rodents. PLoS One 2013, 8: e83171.CrossRefGoogle Scholar
  38. 38.
    Gallistel CR, Balci F, Freestone D, Kheifets A, King A. Automated, quantitative cognitive/behavioral screening of mice: for genetics, pharmacology, animal cognition and undergraduate instruction. J Vis Exp 2014: e51047.Google Scholar
  39. 39.
    Romberg C, Horner AE, Bussey TJ, Saksida LM. A touch screen-automated cognitive test battery reveals impaired attention, memory abnormalities, and increased response inhibition in the TgCRND8 mouse model of Alzheimer’s disease. Neurobiol Aging 2013, 34: 731–744.CrossRefGoogle Scholar
  40. 40.
    Spellman T, Rigotti M, Ahmari SE, Fusi S, Gogos JA, Gordon JA. Hippocampal-prefrontal input supports spatial encoding in working memory. Nature 2015, 522: 309–314.CrossRefGoogle Scholar
  41. 41.
    Hanks TD, Kopec CD, Brunton BW, Duan CA, Erlich JC, Brody CD. Distinct relationships of parietal and prefrontal cortices to evidence accumulation. Nature 2015, 520: 220–223.CrossRefGoogle Scholar
  42. 42.
    Brunton BW, Botvinick MM, Brody CD. Rats and humans can optimally accumulate evidence for decision-making. Science 2013, 340: 95–98.CrossRefGoogle Scholar
  43. 43.
    Fuster JM. The prefrontal cortex : anatomy, physiology, and neuropsychology of the frontal lobe. 3rd ed. Philadelphia: Lippincott-Raven, 1997.Google Scholar
  44. 44.
    Smith EE, Jonides J. Storage and executive processes in the frontal lobes. Science 1999, 283: 1657–1661.CrossRefGoogle Scholar
  45. 45.
    D’Esposito M, Postle BR. The cognitive neuroscience of working memory. Annu Rev Psychol 2015, 66: 115–142.CrossRefGoogle Scholar
  46. 46.
    Goldman-Rakic PS. Cellular basis of working memory. Neuron 1995, 14: 477–485.CrossRefGoogle Scholar
  47. 47.
    Lewis DA, Gonzalez-Burgos G. Pathophysiologically based treatment interventions in schizophrenia. Nat Med 2006, 12: 1016–1022.CrossRefGoogle Scholar
  48. 48.
    Ito HT, Zhang SJ, Witter MP, Moser EI, Moser MB. A prefrontal-thalamo-hippocampal circuit for goal-directed spatial navigation. Nature 2015, 522: 50–55.CrossRefGoogle Scholar
  49. 49.
    Bolkan SS, Stujenske JM, Parnaudeau S, Spellman TJ, Rauffenbart C, Abbas AI, et al. Thalamic projections sustain prefrontal activity during working memory maintenance. Nat Neurosci 2017, 20: 987–996.CrossRefGoogle Scholar
  50. 50.
    Yamamoto J, Suh J, Takeuchi D, Tonegawa S. Successful execution of working memory linked to synchronized high-frequency gamma oscillations. Cell 2014, 157: 845–857.CrossRefGoogle Scholar
  51. 51.
    Kolb B, Nonneman AJ, Singh RK. Double dissociation of spatial impairments and perseveration following selective prefrontal lesions in rats. J Comp Physiol Psychol 1974, 87: 772–780.CrossRefGoogle Scholar
  52. 52.
    Baeg EH, Kim YB, Huh K, Mook-Jung I, Kim HT, Jung MW. Dynamics of population code for working memory in the prefrontal cortex. Neuron 2003, 40: 177–188.CrossRefGoogle Scholar
  53. 53.
    Fujisawa S, Amarasingham A, Harrison MT, Buzsaki G. Behavior-dependent short-term assembly dynamics in the medial prefrontal cortex. Nat Neurosci 2008, 11: 823–833.CrossRefGoogle Scholar
  54. 54.
    Sigurdsson T, Stark KL, Karayiorgou M, Gogos JA, Gordon JA. Impaired hippocampal-prefrontal synchrony in a genetic mouse model of schizophrenia. Nature 2010, 464: 763–767.CrossRefGoogle Scholar
  55. 55.
    Pioli EY, Gaskill BN, Gilmour G, Tricklebank MD, Dix SL, Bannerman D, et al. An automated maze task for assessing hippocampus-sensitive memory in mice. Behav Brain Res 2014, 261: 249–257.CrossRefGoogle Scholar
  56. 56.
    Rawlins JN, Maxwell TJ, Sinden JD. The effects of fornix section on win-stay/lose-shift and win-shift/lose-stay performance in the rat. Behav Brain Res 1988, 31: 17–28.CrossRefGoogle Scholar
  57. 57.
    McDaniel WF, Jones PD, Weaver TL. Medial frontal lesions, postoperative treatment with an ACTH(4–9) analog, and acquisition of a win-shift spatial strategy. Behav Brain Res 1991, 44: 107–112.CrossRefGoogle Scholar
  58. 58.
    Ritzmann RF, Kling A, Melchior CL, Glasky AJ. Effect of age and strain on working memory in mice as measured by win-shift paradigm. Pharmacol Biochem Behav 1993, 44: 805–807.CrossRefGoogle Scholar
  59. 59.
    Randall CK, Zentall TR. Win-stay/lose-shift and win-shift/lose-stay learning by pigeons in the absence of overt response mediation. Behav Processes 1997, 41: 227–236.CrossRefGoogle Scholar
  60. 60.
    Sage JR, Knowlton BJ. Effects of US devaluation on win-stay and win-shift radial maze performance in rats. Behav Neurosci 2000, 114: 295–306.CrossRefGoogle Scholar
  61. 61.
    Taylor CL, Latimer MP, Winn P. Impaired delayed spatial win-shift behaviour on the eight arm radial maze following excitotoxic lesions of the medial prefrontal cortex in the rat. Behav Brain Res 2003, 147: 107–114.CrossRefGoogle Scholar
  62. 62.
    Olton DS, Schlosberg P. Food-searching strategies in young rats: Win-shift predominates over win-stay. J Comp Physiol Psychol 1978, 92: 609–618.CrossRefGoogle Scholar
  63. 63.
    Taube JS, Muller RU, Ranck JB, Jr. Head-direction cells recorded from the postsubiculum in freely moving rats. I. Description and quantitative analysis. J Neurosci 1990, 10: 420–435.Google Scholar
  64. 64.
    Taube JS, Muller RU, Ranck JB, Jr. Head-direction cells recorded from the postsubiculum in freely moving rats. II. Effects of environmental manipulations. J Neurosci 1990, 10: 436–447.Google Scholar
  65. 65.
    Kropff E, Carmichael JE, Moser MB, Moser EI. Speed cells in the medial entorhinal cortex. Nature 2015, 523: 419–424.CrossRefGoogle Scholar
  66. 66.
    Hafting T, Fyhn M, Molden S, Moser MB, Moser EI. Microstructure of a spatial map in the entorhinal cortex. Nature 2005, 436: 801–806.CrossRefGoogle Scholar

Copyright information

© Shanghai Institutes for Biological Sciences, CAS 2019

Authors and Affiliations

  1. 1.Institute of Neuroscience, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, CAS Center for Excellence in Brain Science and Intelligence TechnologyShanghai Center for Brain Science and Brain-Inspired TechnologyShanghaiChina
  2. 2.School of Future TechnologyUniversity of Chinese Academy of SciencesBeijingChina

Personalised recommendations