Advertisement

On the Role of Dopamine in Cognitive Vision

  • Julien Vitay
  • Fred H. Hamker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4840)

Abstract

Although dopamine is one of the most studied neurotransmitter in the brain, its exact function is still unclear. This short review focuses on its role in different levels of cognitive vision: visual processing, visual attention and working memory. Dopamine can influence cognitive vision either through direct modulation of visual cells or through gating of basal ganglia functioning. Even if its classically assigned role is to signal reward prediction error, we review evidence that dopamine is also involved in novelty detection and attention shifting and discuss the possible implications for computational modeling.

Keywords

Prefrontal Cortex Basal Ganglion Ventral Tegmental Area Superior Colliculus Visual Area 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Nieoullon, A.: Dopamine and the regulation of cognition and attention. Prog Neurobiol. 67(1), 53–83 (2002)CrossRefGoogle Scholar
  2. 2.
    Hurd, Y.L., Suzuki, M., Sedvall, G.C.: D1 and D2 dopamine receptor mRNA expression in whole hemisphere sections of the human brain. J. Chem. Neuroanat. 22(1-2), 127–137 (2001)CrossRefGoogle Scholar
  3. 3.
    Yang, C.R., Seamans, J.K.: Dopamine D1 receptor actions in layers V-VI rat prefrontal cortex neurons in vitro: modulation of dendritic-somatic signal integration. J. Neurosci. 16(5), 1922–1935 (1996)Google Scholar
  4. 4.
    Seamans, J.K, Yang, C.R: The principal features and mechanisms of dopamine modulation in the prefrontal cortex. Prog. Neurobiol. 74(1), 1–58 (2004)CrossRefGoogle Scholar
  5. 5.
    Witkovsky, P.: Dopamine and retinal function. Doc. Ophthalmol. 108(1), 17–40 (2004)CrossRefGoogle Scholar
  6. 6.
    Reader, T.A., Quesney, L.F.: Dopamine in the visual cortex of the cat. Experientia 42(11-12), 1242–1244 (1986)CrossRefGoogle Scholar
  7. 7.
    Müller, C.P., Huston, J.P.: Dopamine activity in the occipital and temporal cortices of rats: dissociating effects of sensory but not pharmacological stimulation. Synapse 61(4), 254–258 (2007)CrossRefGoogle Scholar
  8. 8.
    Carpenter, G.A., Grossberg, S.: A massively parallel architecture for a self-organizing neural pattern recognition machine. Comput. Vis. Graphs Image Proc. 37, 54–115 (1987)CrossRefzbMATHGoogle Scholar
  9. 9.
    Mogami, T., Tanaka, K.: Reward association affects neuronal responses to visual stimuli in macaque te and perirhinal cortices. J. Neurosci. 26(25), 6761–6770 (2006)CrossRefGoogle Scholar
  10. 10.
    Rolls, E.T., Judge, S.J., Sanghera, M.K.: Activity of neurones in the inferotemporal cortex of the alert monkey. Brain Res. 130(2), 229–238 (1977)CrossRefGoogle Scholar
  11. 11.
    Thorpe, S.J., Rolls, E.T., Maddison, S.: The orbitofrontal cortex: neuronal activity in the behaving monkey. Exp. Brain Res. 49(1), 93–115 (1983)CrossRefGoogle Scholar
  12. 12.
    Liu, Z., Richmond, B.J, Murray, E.A, Saunders, R.C, Steenrod, S., Stubblefield, B.K, Montague, D.M, Ginns, E.I: DNA targeting of rhinal cortex D2 receptor protein reversibly blocks learning of cues that predict reward. Proc. Natl. Acad. Sci. 101(33), 12336–12341 (2004)CrossRefGoogle Scholar
  13. 13.
    Vitay, J., Hamker, F.H.: Sustained activities and retrieval in a computational model of perirhinal cortex. Submitted to J. Cog. Neurosci. (June 2007)Google Scholar
  14. 14.
    Ranganath, C., D’Esposito, M.: Directing the mind’s eye: prefrontal, inferior and medial temporal mechanisms for visual working memory. Curr. Opin. Neurobiol. 15(2), 175–182 (2005)CrossRefGoogle Scholar
  15. 15.
    Buckley, M.J., Gaffan, D.: Perirhinal cortex ablation impairs visual object identification. J. Neurosci. 18(6), 2268–2275 (1998)Google Scholar
  16. 16.
    Miller, E.K., Gochin, P.M., Gross, C.G.: Suppression of visual responses of neurons in inferior temporal cortex of the awake macaque monkey by addition of a second stimulus. Brain Res. 616, 25–29 (1993)CrossRefGoogle Scholar
  17. 17.
    Hamker, F.H., Wiltschut, J.: Homeostatic scaling and hebbian learning in dynamic rate-coded neurons (in preparation, 2007)Google Scholar
  18. 18.
    Hamker, F.H: The reentry hypothesis: the putative interaction of the frontal eye field, ventrolateral prefrontal cortex, and areas V4, IT for attention and eye movement. Cereb Cortex 15(4), 431–447 (2005)CrossRefGoogle Scholar
  19. 19.
    Schultz, W., Dayan, P., Montague, P.R.: A neural substrate of prediction and reward. Science 275(5306), 1593–1599 (1997)CrossRefGoogle Scholar
  20. 20.
    Nakamura, K., Ono, T.: Lateral hypothalamus neuron involvement in integration of natural and artificial rewards and cue signals. J. Neurophysiol. 55(1), 163–181 (1986)Google Scholar
  21. 21.
    Semba, K., Fibiger, H.C.: Afferent connections of the laterodorsal and the pedunculopontine tegmental nuclei in the rat: a retro- and antero-grade transport and immunohistochemical study. J. Comp. Neurol. 323(3), 387–410 (1992)CrossRefGoogle Scholar
  22. 22.
    Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge, MA (1998)Google Scholar
  23. 23.
    Houk, J.C., Adams, J.L., Barto, A.G.: A model of how the basal ganglia generate and use neural signal that predict reinforcement. In: Houk, J.C., Davis, J.L., Beiser, D.G. (eds.) Models of information processing in the basal ganglia, The MIT Press, Cambridge, MA (1995)Google Scholar
  24. 24.
    Suri, R.E., Schultz, W.: Temporal difference model reproduces anticipatory neural activity. Neural Comput. 13(4), 841–862 (2001)CrossRefzbMATHGoogle Scholar
  25. 25.
    Daw, N.D, Touretzky, D.S: Long-term reward prediction in td models of the dopamine system. Neural Comput. 14(11), 2567–2583 (2002)CrossRefzbMATHGoogle Scholar
  26. 26.
    Kirkpatrick, K., Church, R.M.: Stimulus and temporal cues in classical conditioning. J. Exp. Psychol. Anim. Behav. Process 26(2), 206–219 (2000)CrossRefGoogle Scholar
  27. 27.
    Brown, J., Bullock, D., Grossberg, S.: How the basal ganglia use parallel excitatory and inhibitory learning pathways to selectively respond to unexpected rewarding cues. J. Neurosci. 19(23), 10502–10511 (1999)Google Scholar
  28. 28.
    O’Reilly, R.C., Frank, M.J.: Making working memory work: A computational model of learning in the frontal cortex and basal ganglia. Neur. Comput. 18, 283–328 (2006)CrossRefzbMATHGoogle Scholar
  29. 29.
    Horvitz, J.C.: Mesolimbocortical and nigrostriatal dopamine responses to salient non-reward events. Neuroscience 96(4), 651–656 (2000)CrossRefGoogle Scholar
  30. 30.
    Cheng, K., Saleem, K.S., Tanaka, K.: Organization of corticostriatal and corticoamygdalar projections arising from the anterior inferotemporal area te of the macaque monkey: a phaseolus vulgaris leucoagglutinin study. J. Neurosci. 17(20), 7902–7925 (1997)Google Scholar
  31. 31.
    Redgrave, P., Gurney, K.: The short-latency dopamine signal: a role in discovering novel actions? Nat. Rev. Neurosci. 7(12), 967–975 (2006)CrossRefGoogle Scholar
  32. 32.
    Coizet, V., Comoli, E., Westby, G.W.M., Redgrave, P.: Phasic activation of substantia nigra and the ventral tegmental area by chemical stimulation of the superior colliculus: an electrophysiological investigation in the rat. Eur. J. Neurosci. 17(1), 28–40 (2003)CrossRefGoogle Scholar
  33. 33.
    Dommett, E., Coizet, V., Blaha, C.D., Martindale, J., Lefebvre, V., Walton, N., Mayhew, J.E.W., Overton, P.G., Redgrave, P.: How visual stimuli activate dopaminergic neurons at short latency. Science 307(5714), 1476–1479 (2005)CrossRefGoogle Scholar
  34. 34.
    Oyster, C.W., Takahashi, E.S.: Responses of rabbit superior colliculus neurons to repeated visual stimuli. J. Neurophysiol. 38(2), 301–312 (1975)Google Scholar
  35. 35.
    Wurtz, R.H., Albano, J.E.: Visual-motor function of the primate superior colliculus. Annu. Rev. Neurosci. 3, 189–226 (1980)CrossRefGoogle Scholar
  36. 36.
    Ljungberg, T., Ungerstedt, U.: Sensory inattention produced by 6-hydroxydopamine-induced degeneration of ascending dopamine neurons in the brain. Exp. Neurol. 53(3), 585–600 (1976)CrossRefGoogle Scholar
  37. 37.
    Hikosaka, O., Takikawa, Y., Kawagoe, R.: Role of the basal ganglia in the control of purposive saccadic eye movements. Physiol. Rev. 80(3), 953–978 (2000)Google Scholar
  38. 38.
    Hikosaka, O., Nakamura, K., Nakahara, H.: Basal ganglia orient eyes to reward. J. Neurophysiol. 95(2), 567–584 (2006)CrossRefGoogle Scholar
  39. 39.
    Sommer, M.A, Wurtz, R.H: Influence of the thalamus on spatial visual processing in frontal cortex. Nature 444(7117), 374–377 (2006)CrossRefGoogle Scholar
  40. 40.
    Alexander, G.E., Crutcher, M.D., DeLong, M.R.: Basal ganglia-thalamocortical circuits: parallel substrates for motor, oculomotor, ”prefrontal” and ”limbic” functions. Prog. Brain Res. 85, 119–146 (1990)CrossRefGoogle Scholar
  41. 41.
    Moore, T., Fallah, M.: Control of eye movements and spatial attention. Proc. Natl. Acad. Sci. 98(3), 1273–1276 (2001)CrossRefGoogle Scholar
  42. 42.
    Rizzolatti, G., Riggio, L., Dascola, I., Ulmita, C.: Reorienting attention across the horizontal and vertical meridians: Evidence in favor of a premotor theory of attention. Neuropsychol. 25, 31–40 (1987)CrossRefGoogle Scholar
  43. 43.
    Silkis, I.: A hypothetical role of cortico-basal ganglia-thalamocortical loops in visual processing. Biosystems 89(1-3), 227–235 (2007)CrossRefGoogle Scholar
  44. 44.
    Matsumoto, N., Minamimoto, T., Graybiel, A.M., Kimura, M.: Neurons in the thalamic CM-Pf complex supply striatal neurons with information about behaviorally significant sensory events. J. Neurophysiol. 85(2), 960–976 (2001)Google Scholar
  45. 45.
    Lange, K.W., Robbins, T.W., Marsden, C.D., James, M., Owen, A.M., Paul, G.M.: L-dopa withdrawal in parkinson’s disease selectively impairs cognitive performance in tests sensitive to frontal lobe dysfunction. Psychopharmacology (Berl) 107(2-3), 394–404 (1992)CrossRefGoogle Scholar
  46. 46.
    Kori, A., Miyashita, N., Kato, M., Hikosaka, O., Usui, S., Matsumura, M.: Eye movements in monkeys with local dopamine depletion in the caudate nucleus. ii. deficits in voluntary saccades. J. Neurosci. 15(1 Pt 2), 928–941 (1995)Google Scholar
  47. 47.
    Goldman-Rakic, P.S.: Cellular basis of working memory. Neuron. 14(3), 477–485 (1995)CrossRefGoogle Scholar
  48. 48.
    Fuster, J.M., Alexander, G.E.: Neuron activity related to short-term memory. Science 173, 652–654 (1971)CrossRefGoogle Scholar
  49. 49.
    Alexander, G.E.: Selective neuronal discharge in monkey putamen reflects intended direction of planned limb movements. Exp. Brain Res. 67(3), 623–634 (1987)CrossRefGoogle Scholar
  50. 50.
    Courtney, S.M., Ungerleider, L.G., Keil, K., Haxby, J.V.: Transient and sustained activity in a distributed neural system for human working memory. Nature 386(6625), 608–611 (1997)CrossRefGoogle Scholar
  51. 51.
    Braver, T.S., Barch, D.M., Cohen, J.D.: Cognition and control in schizophrenia: A computational model of dopamine and prefrontal function. Biol. Psychiatry 46(3), 312–328 (1999)CrossRefGoogle Scholar
  52. 52.
    Durstewitz, D., Seamans, J.K., Sejnowski, T.J.: Neurocomputational models of working memory. Nat. Neurosci. Supp. 3, 1184–1191 (2000)CrossRefGoogle Scholar
  53. 53.
    Compte, A., Brunel, N., Goldman-Rakic, P.S., Wang, X.J.: Synaptic mechanisms and network dynamics underlying spatial working memory in a cortical network model. Cereb. Cortex 10(9), 910–923 (2000)CrossRefGoogle Scholar
  54. 54.
    Brunel, N., Wang, X.J.: Effects of neuromodulation in a cortical network model of object working memory dominated by recurrent inhibition. J. Comput. Neurosci. 11(1), 63–85 (2001)CrossRefGoogle Scholar
  55. 55.
    Dreher, J.C., Guigon, E., Burnod, Y.: A model of prefrontal cortex dopaminergic modulation during the delayed alternation task. J. Cogn. Neurosci. 14(6), 853–865 (2002)CrossRefGoogle Scholar
  56. 56.
    Frank, M.J., Loughry, B., O’Reilly, R.C.: Interactions between frontal cortex and basal ganglia in working memory: a computational model. Cogn. Affect Behav. Neurosci. 1(2), 137–160 (2001)CrossRefGoogle Scholar
  57. 57.
    Postle, B.R., D’Esposito, M.: Dissociation of human caudate nucleus activity in spatial and nonspatial working memory: an event-related fmri study. Brain Res. Cogn. Brain Res. 8(2), 107–115 (1999)CrossRefGoogle Scholar
  58. 58.
    Lewis, S.J G, Dove, A., Robbins, T.W, Barker, R.A, Owen, A.M: Striatal contributions to working memory: a functional magnetic resonance imaging study in humans. Eur. J. Neurosci. 19(3), 755–760 (2004)CrossRefGoogle Scholar
  59. 59.
    Wilson, C.J., Kawaguchi, Y.: The origins of two-state spontaneous membrane potential fluctuations of neostriatal spiny neurons. J. Neurosci. 16(7), 2397–2410 (1996)Google Scholar
  60. 60.
    Middleton, F.A, Strick, P.L: Basal-ganglia ’projections’ to the prefrontal cortex of the primate. Cereb Cortex 12(9), 926–935 (2002)CrossRefGoogle Scholar
  61. 61.
    Ashby, F.G., Ell, S.W, Valentin, V.V, Casale, M.B: Frost: a distributed neurocomputational model of working memory maintenance. J. Cogn. Neurosci. 17(11), 1728–1743 (2005)CrossRefGoogle Scholar
  62. 62.
    Gruber, A.J, Dayan, P., Gutkin, B.S, Solla, S.A: Dopamine modulation in the basal ganglia locks the gate to working memory. J. Comput. Neurosci. 20(2), 153–166 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  63. 63.
    Funahashi, S., Bruce, C.J., Goldman-Rakic, P.S.: Mnemonic coding of visual space in the monkey’s dorsolateral prefrontal cortex. J. Neurophysiol. 61, 331–349 (1989)Google Scholar
  64. 64.
    Koch, C., Ullman, S.: Shifts in selective visual attention: towards the underlying neural circuitry. Hum. Neurobiol. 4, 219–227 (1985)Google Scholar
  65. 65.
    Desimone, R., Duncan, J.: Neural mechanisms of selective visual attention. Ann. Rev. Neurosci. 18, 193–222 (1995)CrossRefGoogle Scholar
  66. 66.
    Itti, L., Koch, C.: Computational modelling of visual attention. Nat. Rev. Neurosci. 2, 1–10 (2001)CrossRefGoogle Scholar
  67. 67.
    Deco, G., Rolls, E.T: A neurodynamical cortical model of visual attention and invariant object recognition. Vision Res. 44(6), 621–642 (2004)CrossRefGoogle Scholar
  68. 68.
    Luck, S.J., Vogel, E.K.: The capacity of visual working memory for features and conjunctions. Nature 390(6657), 279–281 (1997)CrossRefGoogle Scholar
  69. 69.
    Lee, D., Chun, M.M.: What are the units of visual short-term memory, objects or spatial locations? Percept Psychophys. 63(2), 253–257 (2001)MathSciNetCrossRefGoogle Scholar
  70. 70.
    Ranganath, C.: Working memory for visual objects: complementary roles of inferior temporal, medial temporal, and prefrontal cortex. Neurosci. 139(1), 277–289 (2006)CrossRefGoogle Scholar
  71. 71.
    Supèr, H., Spekreijse, H., Lamme, V.A.: A neural correlate of working memory in the monkey primary visual cortex. Science 293(5527), 120–124 (2001)CrossRefGoogle Scholar
  72. 72.
    Rolls, E.T.: Hippocampo-cortical and cortico-cortical backprojections. Hippocampus 10(4), 380–388 (2000)CrossRefGoogle Scholar
  73. 73.
    Sakai, K., Rowe, J.B., Passingham, R.E.: Active maintenance in prefrontal area 46 creates distractor-resistant memory. Nat. Neurosci. 5(5), 479–484 (2002)Google Scholar
  74. 74.
    D’Esposito, M., Postle, B.R., Ballard, D., Lease, J.: Maintenance versus manipulation of information held in working memory: an fMRI study. Brain and Cognition 41, 66–86 (1999)CrossRefGoogle Scholar
  75. 75.
    Parent, A., Cicchetti, F.: The current model of basal ganglia organization under scrutiny. Mov. Disord. 13(2), 199–202 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Julien Vitay
    • 1
  • Fred H. Hamker
    • 1
  1. 1.Allgemeine Psychologie, Psychologisches Institut II, Westf. Wilhelms-Universität MünsterGermany

Personalised recommendations