Advertisement

Cognitive Computation

, Volume 10, Issue 5, pp 687–702 | Cite as

Visual and Category Representations Shaped by the Interaction Between Inferior Temporal and Prefrontal Cortices

  • Yuki Abe
  • Kazuhisa Fujita
  • Yoshiki Kashimori
Article
  • 155 Downloads

Abstract

The ability to group items and events into functional categories is a fundamental function for visual recognition. Experimental studies have shown the different roles in information representations of inferior temporal (IT) and prefrontal cortices (PFC) in a categorization task. However, it remains elusive how category information is generated in PFC and maintained in a delay period and how the interaction between IT and PFC influences category performance. To address these issues, we develop a network model of visual system, which performs a delayed match-to-category task. The model consists of networks of V4, IT, and PFC. We show that in IT visual information required for categorization is represented by a combination of prototype features. We also show that category information in PFC is represented by two dynamical attractors weakly linked, resulting from the difference in firing thresholds of PFC neurons. Lower and higher firing thresholds contribute to working memory maintenance and decision-making, respectively. Furthermore, we show that top-down signal from PFC to IT improves the ability of PFC neurons to categorize the mixed images that are closer to a category boundary. Our model may provide a clue for understanding the neural mechanism underlying categorization task.

Keywords

Categorization Inferior temporal cortex Prefrontal cortex Top-down Neural model 

Notes

Compliance with Ethical Standards

Conflict of Interests

The authors declare that they have no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. 1.
    Mishkin M, Ungerleider LG, Macko KA. Object vision and spatial vision: two cortical pathways. Trends Neurosci. 1983;6:414–7.CrossRefGoogle Scholar
  2. 2.
    Ungerleider LG, Mishkin M. Two cortical visual systems. Ingle DJ et al. editors. Analysis of visual behavior, pages 549–586, The MIT Press; 1982.Google Scholar
  3. 3.
    Bruce C, Desimone R, Gross CG. Visual properties of neurons in a polysensory area in superior temporal sulcus in the macaque. J Neurophysiol. 1981;46:369–84.CrossRefPubMedGoogle Scholar
  4. 4.
    Desimone R, Albright TD, Gross CG, Bruce C. Stimulus-selective properties of inferior temporal neurons in the macaque. J Neurosci. 1984;4:2051–62.CrossRefPubMedGoogle Scholar
  5. 5.
    Gross CG. Visual functions of inferotemporal cortex. In: Autrum H, Jung R, Loewenstein WR, Mckay D, Teuber HL, editors. Handbook of sensory physiology, Vol. VII/3B. Berlin: Springer; 1973. p. 451–82.Google Scholar
  6. 6.
    Logothetis NK, Sheinberg DL. Visual object recognition. Annu Rev Neurosci. 1996;19:577–621.CrossRefPubMedGoogle Scholar
  7. 7.
    Perrett DI, Rolls ET, Caan W. Visual neurons responsive to faces in the monkey temporal cortex. Exp Brain Res. 1982;47:329–42.CrossRefPubMedGoogle Scholar
  8. 8.
    Tanaka K. Columns for complex visual object features in the inferotemporal cortex: clustering of cells with similar but slightly different stimulus selectivities. Cereb Cortex. 2003;13:90–9.CrossRefPubMedGoogle Scholar
  9. 9.
    Baker CI, Behrmann M, Olson CR. Impact of learniong on representation of parts and wholes in monkey inferotemporal cortex. Nat Neurosci. 2002;5:1210–6.CrossRefPubMedGoogle Scholar
  10. 10.
    Booth MC, Rolls ET. View-invariant representations of familiar objects by neurons in the inferior temporal cortex. Cereb Cortex. 1998;8:510–23.CrossRefPubMedGoogle Scholar
  11. 11.
    Kobatake E, Wang G, Tanaka K. Effects of shape-discrimination training on the selectivity of inferotemporal cells in adult monkeys. J Neurophysiol. 1998;80:324–30.CrossRefPubMedGoogle Scholar
  12. 12.
    Logothetis NK, Pauls J, Possio T. Shape representation in the inferior temporal cortex of monkeys. Curr Biol. 1995;5:552–63.CrossRefPubMedGoogle Scholar
  13. 13.
    Miyashita Y. Neuronal correlate of visual associative long-term memory in the primate temporal cortex. Nature. 1988;335:817–20.CrossRefPubMedGoogle Scholar
  14. 14.
    Palmeri TJ, Gauthier I. Visual object understanding. Nat Rev Neurosci. 2004;5:291–303.CrossRefPubMedGoogle Scholar
  15. 15.
    Seger CA, Miller EK. Category learning in the brain. Annu Rev Neurosci. 2010;33:203–19.CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Ungerleider LG, Gaffan D, Pelak VS. Projections from inferior temporal cortex to prefrontal cortex via the uncinated fascicle in rhesus monkeys. Exp Brain Res. 1989;76:473–84.CrossRefPubMedGoogle Scholar
  17. 17.
    Webster MJ, Bachevalier J, Ungerleider LG. Connections of inferior temporal areas TEO and TE with parietal and frontal cortex in macaque monkeys. Cereb Cortex. 1994;4:470–83.CrossRefPubMedGoogle Scholar
  18. 18.
    Vogels R. Categorization of complex visual images by rhesus monkeys. Part 2: single cell study. Eur J Neurosci. 1999;11:1239–55.CrossRefPubMedGoogle Scholar
  19. 19.
    Sigala N, Logothetis NK. Visual categorization shapes feature selectivity in the primate temporal cortex. Nature. 2002;415:318–20.CrossRefPubMedGoogle Scholar
  20. 20.
    Soga M, Kashimori Y. Functional connections between visual areas in extracting object features critical for a visual categorization task. Vis Res. 2009;49:337–47.CrossRefPubMedGoogle Scholar
  21. 21.
    Freedman DJ, Riesenhuber M, Poggio T, Miller EK. A comparison of primate pre-frontal and inferior temporal cortices during visual categorization. J Neurosci. 2003;23:5235–46.CrossRefPubMedGoogle Scholar
  22. 22.
    Mckee JL, Riesenhuber M, Miller EK, Freedman DJ. Task dependence of visual and category representations in prefrontal and inferior temporal cortices. J Neurosci. 2014;34:16065–75.CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Tanaka K. Inferotemporal cortex and object vision. Annu Rev Neurosci. 1996;19:109–39.CrossRefPubMedGoogle Scholar
  24. 24.
    Tsunoda K, Yamane Y, Nishizaki M, Tanifuji M. Complex objects are represented in macaque inferotemporal cortex by the combination of feature columns. Nat Neurosci. 2001;4:832–8.CrossRefPubMedGoogle Scholar
  25. 25.
    Yamane Y, Tsunoda K, Matsumoto K, Phillips A, Tanifuji M. Representation of the spatial relationship among object parts by neurons in macaque inferotemporal cortex. J Neurophysiol. 2006;96:3147–56.CrossRefPubMedGoogle Scholar
  26. 26.
    De Baene W, Ons B, Wagemans J, Vogels R. Effects of category learning on the stimulus selectivity of macaque inferior temporal neurons. Learn Mem. 2008;15:717–27.CrossRefPubMedGoogle Scholar
  27. 27.
    Wang XJ. Synaptic reverberation underlying mnemonic persistent activity. Trends Neurosci. 2001;24:455–63.CrossRefPubMedGoogle Scholar
  28. 28.
    Vitay J, Hamkar FH. Sustained activities and retrieval in a computational model of the perirhinal cortex. J Cogn Neurosci. 2008;20:1993–2005.CrossRefPubMedGoogle Scholar
  29. 29.
    Freedman DJ, Riesenhuber M, Possio T, Miller EK. Categorical representation of visual stimuli in the primate prefrontal cortex. Science. 2001;291:312–6.CrossRefPubMedGoogle Scholar
  30. 30.
    Freedman DJ, Riesenhuber M, Poggio T, Miller EK. Visual categorization and the primate prefrontal cortex: neurophysiology and behavior. J Neurophysiol. 2002;88:929–41.CrossRefPubMedGoogle Scholar
  31. 31.
    Cromer JA, Roy JE, Miller EK. Representation of multiple, independent categories in the primate prefrontal cortex. Neuron. 2010;66:796–807.CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Fusi S, Miller EK, Rigotti M. Why neurons mix: high dimensionality for higher cognition. Curr Opin Neurobiol. 2016;37:66–74.CrossRefPubMedGoogle Scholar
  33. 33.
    Mante V, Sussillo D, Shenoy KV, Newsome WT. Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature. 2013;503:78–84.CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Rigotti M, Barak O, Warden MR, Wang X-J, Daw ND, Miller EK, et al. The importance of mixed selectivity in complex cognitive tasks. Nature. 2013;497:585–90.CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Roy JE, Riesenhuber M, Poggio T, Miller EK. Prefrontal cortex activity during flexible categorization. J Neurosci. 2010;30:8519–28.CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Kohonen T. Self-organizing maps. Third, extended edition, volume 30 of Springer series in information sciences, Springer, NY. 2001.Google Scholar
  37. 37.
    Bienenstock EL, Cooper LN, Munro PW. Theory for the development of neural selectivity: orientation specificity and binocular interaction in visual cortex. J Neurosci. 1982;2:32–48.CrossRefPubMedGoogle Scholar
  38. 38.
    Lim S, McKee JI, Woloszyn L, Amit Y, Freedman DJ, Sheinberg D, et al. Inferring learning rules from distributions of firing rates in cortical neurons. Nat Neurosci. 2015;18:1804–10.CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Hoshino O, Inoue S, Kashimori Y, Kambara T. A hierarchical dynamical map as a basic frame for cortical mapping and its application to priming. Neural Comput. 2001;13(8):1781–810.CrossRefPubMedGoogle Scholar
  40. 40.
    Durstewitz D, Seamans JK, Sejnowski TJ. Neurocomputational models of working memory. Nat Neurosci. 2000;3:1184–91.CrossRefPubMedGoogle Scholar
  41. 41.
    Amit DJ, Brunel N. Model of global spontaneous activity and local structured activity during delay period in the cerebral cortex. Cereb Cortex. 1997;7:237–52.CrossRefPubMedGoogle Scholar
  42. 42.
    Amit DJ, Fusi S, Yakovlev V. Paradigmatic working memory (attractor) cell in IT cortex. Neural Comput. 1997;9:1071–92.CrossRefPubMedGoogle Scholar
  43. 43.
    Compte A, Brunel N, Goldman-Rakic PS, Wang XJ. Synaptic mechanisms and network dynamics underlying spatial working memory in a cortical network model. Cereb Cortex. 2000;10:910–23.CrossRefPubMedGoogle Scholar
  44. 44.
    Durstewitz D, Seamans JK, Sejnowski TJ. Dopamine-mediated stabilization of delay-period activity in a network model of prefrontal cortex. J Neurophysiol. 2000;83:1733–50.CrossRefPubMedGoogle Scholar
  45. 45.
    Meyers EM, Freedman DJ, Kreiman G, Miller EK, Poggio T. Dynamic population coding of category information in inferior temporal and prefrontal cortex. J Neurophysiol. 2008;100:1407–19.CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Stokes MG, Kusunoki M, Sigala N, Nili H, Gaffan D, Duncan J. Dynamic coding for cognitive control in prefrontal cortex. Neuron. 2013;78:364–75.CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Sussillo D, Toyoizumi T, Mass W. Self-tuning of neural circuits through short-term synaptic plasticity. J Neurophysiol. 2007;97:4079–95.CrossRefPubMedGoogle Scholar
  48. 48.
    Mongillo G, Barak O, Tsodyks M. Synaptic theory of working memory. Science. 2008;319:1543–6.CrossRefPubMedGoogle Scholar
  49. 49.
    Fiebig F, Lansner A. A spiking working memory model based on Hebbian short-term potentiation. J Neurosci. 2017;37:83–96.CrossRefPubMedPubMedCentralGoogle Scholar
  50. 50.
    Chaisangmongkon W, Swaminathan SK, Freedman DJ, Wang JX. Computing by robust transience: how the front-parietal network performs sequential, category-based decisions. Neuron. 2017;93:1504–17.CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    Riesenhuber M, Poggio T. Hierarchical models of object recognition in cortex. Nat Neurosci. 1999;2:1019–25.CrossRefPubMedGoogle Scholar
  52. 52.
    Riesenhuber M, Poggio T. Neural mechanisms of object recognition. Curr Opin Neurobiol. 2002;12:162–8.CrossRefPubMedGoogle Scholar
  53. 53.
    Knoblich U, Freedman DJ, Riesenhuber M. Categorization in IT and PFC; model and experiments, vol. 2002-007. Cambridge: MIT AI Laboratory; 2002.Google Scholar
  54. 54.
    Minami T, Inui T. Roles of prefrontal neurons in delayed maching-to-category task: a modeling study. Neurocomputing. 2005;65-66:609–16.CrossRefGoogle Scholar
  55. 55.
    Pannunzi M, Gigante G, Mattia M, Deco D, Fusi S, Giudice PD. Learning selective top-down control enhances performance in a visual categorization task. J Neurophysiol. 2012;108:3124–37.CrossRefPubMedGoogle Scholar
  56. 56.
    Ding S, Meng L, Han Y, Xue Y. A review of feature binding theory and its functions observed in perceptual process. Cogn Comput. 2017;9:194–206.CrossRefGoogle Scholar
  57. 57.
    Jamalian A, Beuth F, Hamkar FH. The performance of a biologically plausible model of visual attention to localize objects in a virtual reality. In: Villa AEP, et al., editors. Notes in Computer Science, vol. 9887. Switzerland: Springer International Publishing; 2016. p. 447–54.Google Scholar
  58. 58.
    Wyatte D, Curran T, O’Relly R. The limit of feedforward vision: recurrent processing promotes robust object recognition when objects are degraded. J Cogn Neurosci. 2012;24:2248–61.CrossRefPubMedGoogle Scholar
  59. 59.
    Desimone R, Duncan J. Neural mechanisms of selective visual attention. Annu Rev Neurosci. 1995;18:193–222.CrossRefPubMedGoogle Scholar
  60. 60.
    Reynolds JH, Pasternak T, Desimone R. Attention increases sensitivity of V4 neurons. Neuron. 2000;26:703–14.CrossRefPubMedGoogle Scholar
  61. 61.
    Azouz R, Gray CM. Dynamic spike threshold reveals a mechanism for synaptic coincidence detection in cortical neuron in vivo. Proc Natl Acad Science U S A. 2000;97:8110–5.CrossRefGoogle Scholar
  62. 62.
    Azouz R, Gray CM. Adaptive coincidence detection and dynamic gain control in visual cortical neurons in vivo. Neuron. 2003;37:513–23.CrossRefPubMedGoogle Scholar
  63. 63.
    Wang Y, Markram H, Goodman PH, Berger TK, Ma J, Goldman-Rakic PS. Heterogeneity in the pyramidal network of the medial prefrontal cortex. Nat Neurosci. 2006;9:534–42.CrossRefPubMedGoogle Scholar
  64. 64.
    Rainer G, Miller EK. Timecourse of object-related neural activity in the primate prefrontal cortex during a short-term memory task. Eur J Neurosci. 2002;15:1244–54.CrossRefPubMedGoogle Scholar
  65. 65.
    Rainer G, Rao SC, Miller EK. Prospective coding for objects in primate prefrontal cortex. J Neurosci. 1999;19:5493–505.CrossRefPubMedGoogle Scholar
  66. 66.
    Rao RPN, Ballard DH. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nat Neurosci. 1999;2(1):79–87.CrossRefPubMedGoogle Scholar
  67. 67.
    Spratling MW. A hierarchical predictive coding model of object recognition in natural images. Cogn Comput. 2017;9:151–67.CrossRefGoogle Scholar
  68. 68.
    Arnal LH, Giraud A-L. Cortical oscillations and sensory predictions. Trends Cogn Neurosci. 2012;16:390–8.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Engineering ScienceUniversity of Electro-CommunicationsChofuJapan
  2. 2.Department of Clinical Engineering, Faculty of Health SciencesKomatsu UniversityKomatsuJapan

Personalised recommendations