Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Computational models of schizophrenia and dopamine modulation in the prefrontal cortex

Nature Reviews Neuroscience volume 9, pages 696–709 (2008)Cite this article

Key Points

  • In attractor networks, a reduction in the depth of the basins of attraction of cortical attractor states destabilizes the activity at the network level owing to the constant statistical fluctuations that are caused by the stochastic spiking of neurons.

  • A decrease in the NMDA (N-methyl-D-aspartate)-receptor (NMDAR) conductances, which reduces the depth of the attractor basins, reduces the stability of short-term memory states and increases distractibility. These effects decrease the signal-to-noise ratio of the networks.

  • The cognitive symptoms of schizophrenia, such as distractibility, working-memory deficits and poor attention, could be caused by such instability of attractor states in prefrontal cortical networks.

  • Information processing in patients with schizophrenia is characterized by a diminished cortical signal-to-noise ratio during tasks that require the allocation of attention and short-term memory (as suggested by, for example, electrophysiological recordings and functional MRI).

  • In patients with schizophrenia, reduced dopamine D1 receptor activation in the prefrontal cortex can decrease the signal-to-noise ratio, at least in part by reducing NMDAR-activated synaptic currents. The underlying reason for this might be the decrease in the stability of the cortical attractor networks that is produced by a reduction in NMDAR-activated currents: this reduction decreases the depth of the basins of attraction, making short-term memory and attention unstable in the context of the spiking-related noise in cortical networks.

  • This computational approach enables us to link factors that modulate currents in synapses to the effects of this modulation on the global performance of a network — for example, to implement cognitive processes such as short-term memory and attention.

  • A reduction of NMDAR-activated synaptic conductances produces lower firing rates in neurons; in the orbitofrontal and anterior cingulate cortices this could account for the negative symptoms of schizophrenia, including reduced emotions.

  • Decreasing the GABA (g-aminobutyric acid) and the NMDA conductances produces not only switches between the attractor states, but also jumps from spontaneous activity into one of the attractor states. This might be related to the positive symptoms of schizophrenia, including delusions, paranoia and hallucinations: these symptoms could arise because the basins of attraction are shallow and there is instability in temporal lobe semantic-memory networks, leading thoughts to move too freely around the attractor energy landscape.

Abstract

Computational neuroscience models can be used to understand the diminished stability and noisy neurodynamical behaviour of prefrontal cortex networks in schizophrenia. These neurodynamical properties can be captured by simulated neural networks with randomly spiking neurons that introduce noise into the system and produce trial-by-trial variation of postsynaptic potentials. Theoretical and experimental studies have aimed to understand schizophrenia in relation to noise and signal-to-noise ratio, which are promising concepts for understanding the symptoms that characterize this heterogeneous illness. Simulations of biologically realistic neural networks show how the functioning of NMDA (N-methyl-D-aspartate), GABA (g-aminobutyric acid) and dopamine receptors is connected to the concepts of noise and variability, and to related neurophysiological findings and clinical symptoms in schizophrenia.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Connectionist models.
Figure 2: Dopamine effects in the prefrontal cortex.
Figure 3: Attractor hypothesis of schizophrenia.
Figure 4: Increased cortical response variability ('noise') in patients with schizophrenia.
Figure 5: Molecular events following dopamine D1 and D2 receptor stimulation.

References

  1. Liddle, P. F. The symptoms of chronic schizophrenia. A re-examination of the positive–negative dichotomy. Br. J. Psychiatry 151, 145–151 (1987).

    Article  CAS  PubMed  Google Scholar 

  2. Green, M. F. What are the functional consequences of neurocognitive deficits in schizophrenia? Am. J. Psychiatry 153, 321–330 (1996).

    Article  CAS  PubMed  Google Scholar 

  3. Mueser, K. T. & McGurk, S. R. Schizophrenia. Lancet 363, 2063–2072 (2004). This paper reviewed the clinical aspects of schizophrenia.

    Article  PubMed  Google Scholar 

  4. Wang, X. J. Synaptic basis of cortical persistent activity: the importance of NMDA receptors to working memory. J. Neurosci. 19, 9587–9603 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. 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, 910–923 (2000).

    Article  CAS  PubMed  Google Scholar 

  6. Durstewitz, D., Seamans, J. K. & Sejnowski, T. J. Neurocomputational models of working memory. Nature Neurosci. 3 (Suppl. 1),184–191 (2000).

    Google Scholar 

  7. 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, 63–85 (2001). This paper described an integrate-and-fire attractor network for working memory with a consistent mean-field analysis.

    Article  CAS  PubMed  Google Scholar 

  8. Wang, X. J. Synaptic reverberation underlying mnemonic persistent activity. Trends Neurosci. 24, 455–463 (2001).

    Article  CAS  PubMed  Google Scholar 

  9. Durstewitz, D., Seamans, J. K. & Sejnowski, T. J. Dopamine-mediated stabilization of delay-period activity in a network model of prefrontal cortex. J. Neurophysiol. 83, 1733–1750 (2000).

    Article  CAS  PubMed  Google Scholar 

  10. Usher, M. & Niebur, E. Modelling the temporal dynamics of IT neurons in visual search: a mechanism for top-down selective attention. J. Cogn. Neurosci. 8, 311–327 (1996).

    Article  CAS  PubMed  Google Scholar 

  11. Miller, E. K. & Cohen, J. D. An integrative theory of prefrontal cortex function. Annu. Rev. Neurosci. 24, 167–202 (2001).

    Article  CAS  PubMed  Google Scholar 

  12. Deco, G. & Rolls, E. T. Attention and working memory: a dynamical model of neuronal activity in the prefrontal cortex. Eur. J. Neurosci. 18, 2374–2390 (2003).

    Article  PubMed  Google Scholar 

  13. Wang, X. J. Probabilistic decision making by slow reverberation in cortical circuits. Neuron 36, 955–968 (2002).

    Article  CAS  PubMed  Google Scholar 

  14. Bogacz, R. Optimal decision-making theories: linking neurobiology with behaviour. Trends Cogn. Sci. 11, 118–125 (2007).

    Article  PubMed  Google Scholar 

  15. Gold, J. I. & Shadlen, M. N. The neural basis of decision making. Annu. Rev. Neurosci. 30, 535–574 (2007).

    Article  CAS  PubMed  Google Scholar 

  16. Rolls, E. T. Memory, Attention, and Decision-Making: A Unifying Computational Neuroscience Approach (Oxford Univ. Press, Oxford, 2008). This book describes computational neuroscience and how it can be applied in a multidisciplinary approach to many aspects of brain function, including not only memory, attention and decision making, but also emotion and visual-object recognition.

    Google Scholar 

  17. Winterer, G. et al. Schizophrenia: reduced signal-to-noise ratio and impaired phase-locking during information processing. Clin. Neurophysiol. 111, 837–849 (2000). This paper was the first report that abnormal electrophysiological phase synchrony (noise) is increased in schizophrenia and predicts cortical activation abnormalities in schizophrenia with high diagnostic specificity.

    Article  CAS  PubMed  Google Scholar 

  18. Winterer, G. et al. Prefrontal broadband noise, working memory, and genetic risk for schizophrenia. Am. J. Psychiatry 161, 490–500 (2004). This was the first report on abnormal electrophysiological noise as a genetically determined risk factor for schizophrenia and cognitive deficits.

    Article  PubMed  Google Scholar 

  19. Braitenberg, V. & Schütz, A. Anatomy of the Cortex (Springer, Berlin, 1991).

    Book  Google Scholar 

  20. Abeles, M. Corticonics — Neural Circuits of the Cerebral Cortex (Cambridge Univ. Press, New York, 1991).

    Book  Google Scholar 

  21. Goldman-Rakic, P. S. Cellular basis of working memory. Neuron 14, 477–485 (1995).

    Article  CAS  PubMed  Google Scholar 

  22. Rolls, E. T. & Deco, G. Computational Neuroscience of Vision (Oxford Univ. Press, Oxford, 2002).

    Google Scholar 

  23. Hopfield, J. J. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl Acad. Sci. USA 79, 2554–2558 (1982).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Amit, D. J. Modeling Brain Function (Cambridge Univ. Press, Cambridge, 1989).

    Book  Google Scholar 

  25. Hertz, J., Krogh, A. & Palmer, R. G. An Introduction to the Theory of Neural Computation (Addison–Wesley, Wokingham, 1991).

    Book  Google Scholar 

  26. Goldman-Rakic, P. S. The prefrontal landscape: implications of functional architecture for understanding human mentation and the central executive. Philos. Trans. R. Soc. Lond. B Biol. Sci. 351, 1445–1453 (1996).

    Article  CAS  PubMed  Google Scholar 

  27. Fuster, J. M. Executive frontal functions. Exp. Brain Res. 133, 66–70 (2000).

    Article  CAS  PubMed  Google Scholar 

  28. Fuster, J. M. & Alexander, G. E. Neuron activity related to short-term memory. Science 173, 652–654 (1971).

    Article  CAS  PubMed  Google Scholar 

  29. Kubota, K. & Niki, H. Prefrontal cortical unit activity and delayed alternation performance in monkeys. J. Neurophysiol. 34, 337–347 (1971).

    Article  CAS  PubMed  Google Scholar 

  30. Funahashi, S., Bruce, C. J. & Goldman-Rakic, P. S. Mnemonic coding of visual space in monkey dorsolateral prefrontal cortex. J. Neurophysiol. 61, 331–349 (1989).

    Article  CAS  PubMed  Google Scholar 

  31. Fuster, J. M. Memory in the Cerebral Cortex (MIT Press, Cambridge, Massachusetts, 1995).

    Google Scholar 

  32. Desimone, R. & Duncan, J. Neural mechanisms of selective visual attention. Annu. Rev. Neurosci. 18, 193–222 (1995).

    Article  CAS  PubMed  Google Scholar 

  33. Deco, G. & Rolls, E. T. Attention, short-term memory, and action selection: a unifying theory. Prog. Neurobiol. 76, 236–256 (2005).

    Article  PubMed  Google Scholar 

  34. Goldman-Rakic, P. S. & Leung, H.-C. in Principles of Frontal Lobe Function (eds Stuss, D. T. & Knight, R. T.) 85–95 (Oxford Univ. Press, New York, 2002).

    Book  Google Scholar 

  35. Seidman, L. J. et al. Relationship of prefrontal and temporal lobe MRI measures to neuropsychological performance in chronic schizophrenia. Biol. Psychiatry 35, 235–246 (1994).

    Article  CAS  PubMed  Google Scholar 

  36. Weinberger, D. R. & Berman, K. F. Prefrontal function in schizophrenia: confounds and controversies. Philos. Trans. R. Soc. Lond. B Biol. Sci. 351, 1495–503 (1996).

    Article  CAS  PubMed  Google Scholar 

  37. Frith, C. & Dolan, R. J. Brain mechanisms associated with top-down processes in perception. Philos. Trans. R. Soc. Lond. B Biol. Sci. 352, 1221–1230 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Loh, M., Rolls, E. T. & Deco, G. A dynamical systems hypothesis of schizophrenia. PLoS Comput. Biol. 3, e228 (2007). This study took an attractor-based approach that linked alterations in glutamate function to the different symptoms of schizophrenia.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Coyle, J. T. Glutamate and schizophrenia: beyond the dopamine hypothesis. Cell. Mol. Neurobiol. 26, 365–384 (2006). This paper gave an account of how glutamate function might be related to schizophrenia.

    Article  CAS  PubMed  Google Scholar 

  40. Rolls, E. T., Loh, M. & Deco, G. An attractor hypothesis of obsessive-compulsive disorder. Eur. J. Neurosci. 28, 782–793 (2008).

    Article  PubMed  Google Scholar 

  41. Brunel, N. & Hakim, V. Fast global oscillations in networks of integrate-and-fire neurons with low firing rates. Neural Comput. 11, 1621–1671 (1999).

    Article  CAS  PubMed  Google Scholar 

  42. Mattia, M. & Del Giudice, P. Population dynamics of interacting spike neurons. Phys. Rev. E 66, 51917–51919 (2002).

    Article  CAS  Google Scholar 

  43. Mattia, M. & Del Giudice, P. Finite-size dynamics of inhibitory and excitatory interacting spiking neurons. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 70, 052903 (2004).

    Article  CAS  PubMed  Google Scholar 

  44. Deco, G. & Rolls, E. T. Decision-making and Weber's law: a neurophysiological model. Eur. J. Neurosci. 24, 901–916 (2006).

    Article  PubMed  Google Scholar 

  45. Faisal, A. A., Selen, L. P. & Wolpert, D. M. Noise in the nervous system. Nature Rev. Neurosci. 9, 292–303 (2008).

    Article  CAS  Google Scholar 

  46. Benes, F. M. Emerging principles of altered neural circuitry in schizophrenia. Brain Res. Brain Res. Rev. 31, 251–269 (2000).

    Article  CAS  PubMed  Google Scholar 

  47. Hashimoto, T. et al. Alterations in GABA-related transcriptome in the dorsolateral prefrontal cortex of subjects with schizophrenia. Mol. Psychiatry 13, 147–161 (2008).

    Article  CAS  PubMed  Google Scholar 

  48. Lisman, J. E., Fellous, J. M. & Wang, X. J. A role for NMDA-receptor channels in working memory. Nature Neurosci. 1, 273–275 (1998).

    Article  CAS  PubMed  Google Scholar 

  49. Tegner, J., Compte, A. & Wang, X. J. The dynamical stability of reverberatory neural circuits. Biol. Cybern. 87, 471–481 (2002).

    Article  PubMed  Google Scholar 

  50. Hoffman, R. E. & Dobscha, S. K. Cortical pruning and the development of schizophrenia: a computer model. Schizophr. Bull. 15, 477–490 (1989).

    Article  CAS  PubMed  Google Scholar 

  51. Hoffman, R. E. Neural network simulations, cortical connectivity, and schizophrenic psychosis. MD Comput. 14, 200–208 (1997).

    CAS  PubMed  Google Scholar 

  52. Hoffman, R. E. & McGlashan, T. H. Neural network models of schizophrenia. Neuroscientist 7, 441–454 (2001).

    Article  CAS  PubMed  Google Scholar 

  53. Treves, A. Are spin-glass effects relevant to understanding realistic auto-associative networks. J. Phys. A 24, 2645–2654 (1991).

    Article  Google Scholar 

  54. Rolls, E. T. & Tovee, M. J. Sparseness of the neuronal representation of stimuli in the primate temporal visual cortex. J. Neurophysiol. 73, 713–726 (1995).

    Article  CAS  PubMed  Google Scholar 

  55. McGlashan, T. H. & Hoffman, R. E. Schizophrenia as a disorder of developmentally reduced synaptic connectivity. Arch. Gen. Psychiatry 57, 637–648 (2000).

    Article  CAS  PubMed  Google Scholar 

  56. O'Kane, D. & Treves, A. Why the simplest notion of neocortex as an autoassociative memory would not work. Network 3, 379–384 (1992).

    Article  Google Scholar 

  57. Rolls, E. T. & Treves, A. Neural Networks and Brain Function (Oxford Univ. Press, Oxford, 1998).

    Google Scholar 

  58. Stephan, K. E., Baldeweg, T. & Friston, K. J. Synaptic plasticity and dysconnection in schizophrenia. Biol. Psychiatry 59, 929–939 (2006).

    Article  CAS  PubMed  Google Scholar 

  59. Friston, K. J. Dysfunctional connectivity in schizophrenia. World Psychiatry 1, 66–71 (2002).

    PubMed  PubMed Central  Google Scholar 

  60. Cohen, J. D., Braver, T. S. & O'Reilly, R. C. A computational approach to prefrontal cortex, cognitive control and schizophrenia: recent developments and current challenges. Philos. Trans. R. Soc. Lond. B Biol. Sci. 351, 1515–1527 (1996).

    Article  CAS  PubMed  Google Scholar 

  61. 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, 312–328 (1999).

    Article  CAS  PubMed  Google Scholar 

  62. Cohen, J. D. & Servan-Schreiber, D. Context, cortex, and dopamine: a connectionist approach to behavior and biology in schizophrenia. Psychol. Rev. 99, 45–77 (1992).

    Article  CAS  PubMed  Google Scholar 

  63. Braver, T. S. & Cohen, J. D. in Disorders of Brain, Behavior, and Cognition: the Neurocomputational Perspective (eds Reggia, J. A., Ruppin, E. & Glanzman, D. L.) 327–350 (Elsevier, New York, 1999).

    Book  Google Scholar 

  64. Durstewitz, D., Kelc, M. & Gunturkun, O. A neurocomputational theory of the dopaminergic modulation of working memory functions. J. Neurosci. 19, 2807–2822 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Seamans, J. K. & Yang, C. R. The principal features and mechanisms of dopamine modulation in the prefrontal cortex. Prog. Neurobiol. 74, 1–58 (2004).

    Article  CAS  PubMed  Google Scholar 

  66. Durstewitz, D. A few important points about dopamine's role in neural network dynamics. Pharmacopsychiatry 39 (Suppl. 1), S72–S75 (2006).

    Article  CAS  PubMed  Google Scholar 

  67. Durstewitz, D. in Monoaminergic Modulation of Cortical Excitability (eds Tseng, K. Y. & Atzori, M.) 217–234 (Springer, Berlin, 2007).

    Book  Google Scholar 

  68. Winterer, G. & Weinberger, D. R. Genes, dopamine and cortical signal-to-noise ratio in schizophrenia. Trends Neurosci. 27, 683–690 (2004). This paper provides a comprehensive overview of the noise concept in schizophrenia and of how different molecular mechanisms (dopamine, GABA and glutamate) contribute to cortical microcircuit stability by changing neural synchrony (noise).

    Article  CAS  PubMed  Google Scholar 

  69. Wang, X. J. Toward a prefrontal microcircuit model for cognitive deficits in schizophrenia. Pharmacopsychiatry 39 (Suppl. 1), S80–S87 (2006).

    Article  PubMed  Google Scholar 

  70. Wang, X. J., Tegner, J., Constantinidis, C. & Goldman-Rakic, P. S. Division of labor among distinct subtypes of inhibitory neurons in a cortical microcircuit of working memory. Proc. Natl Acad. Sci. USA 101, 1368–1373 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Lewis, D. A., Hashimoto, T. & Volk, D. W. Cortical inhibitory neurons and schizophrenia. Nature Rev. Neurosci. 6, 312–324 (2005).

    Article  CAS  Google Scholar 

  72. Rolls, E. T. Emotion Explained (Oxford Univ. Press, Oxford, 2005).

    Book  Google Scholar 

  73. Loh, M., Rolls, E. T. & Deco, G. Statistical fluctuations in attractor networks related to schizophrenia. Pharmacopsychiatry 40, S78–S84 (2007).

    Article  Google Scholar 

  74. Coyle, J. T., Tsai, G. & Goff, D. Converging evidence of NMDA receptor hypofunction in the pathophysiology of schizophrenia. Ann. NY Acad. Sci. 1003, 318–327 (2003).

    Article  CAS  PubMed  Google Scholar 

  75. Goldman-Rakic, P. S. Working memory dysfunction in schizophrenia. J. Neuropsychiatry Clin. Neurosci. 6, 348–357 (1994).

    Article  CAS  PubMed  Google Scholar 

  76. Goldman-Rakic, P. S. The physiological approach: functional architecture of working memory and disordered cognition in schizophrenia. Biol. Psychiatry 46, 650–661 (1999).

    Article  CAS  PubMed  Google Scholar 

  77. Durstewitz, D. & Seamans, J. K. The computational role of dopamine D1 receptors in working memory. Neural Netw. 15, 561–572 (2002).

    Article  PubMed  Google Scholar 

  78. Bilder, R. M. et al. Neurocognitive effects of clozapine, olanzapine, risperidone, and haloperidol in patients with chronic schizophrenia or schizoaffective disorder. Am. J. Psychiatry 159, 1018–1028 (2002).

    Article  PubMed  Google Scholar 

  79. Delawalla, Z. et al. Factors mediating cognitive deficits and psychopathology among siblings of individuals with schizophrenia. Schizophr. Bull. 32, 525–537 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  80. Jacobs, J., Kahana, M. J., Ekstrom, A. D. & Fried, I. Brain oscillations control timing of single-neuron activity in humans. J. Neurosci. 27, 3839–3844 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Gallinat, J. et al. Frontal and temporal dysfunction of auditory stimulus processing in schizophrenia. Neuroimage 17, 110–127 (2002).

    Article  PubMed  Google Scholar 

  82. Rolls, E. T. The Brain and Emotion (Oxford Univ. Press, Oxford, 1999).

    Google Scholar 

  83. Paus, T. Primate anterior cingulate cortex: where motor control, drive and cognition interface. Nature Rev. Neurosci. 2, 417–424 (2001).

    Article  CAS  Google Scholar 

  84. Winterer, G., Adams, C. M., Jones, D. W. & Knutson, B. Volition to action — an event-related fMRI study. Neuroimage 17, 851–858 (2002).

    Article  PubMed  Google Scholar 

  85. Rolls, E. T. in The Orbitofrontal Cortex (eds Zald, D. H. & Rauch, S. L.) 95–124 (Oxford Univ. Press, Oxford, 2006).

    Book  Google Scholar 

  86. Seamans, J. K., Durstewitz, D., Christie, B. R., Stevens, C. F. & Sejnowski, T. J. Dopamine D1/D5 receptor modulation of excitatory synaptic inputs to layer V prefrontal cortex neurons. Proc. Natl Acad. Sci. USA 98, 301–306 (2001).

    Article  CAS  PubMed  Google Scholar 

  87. Friston, K. J. The labile brain. III. Transients and spatio-temporal receptive fields. Philos. Trans. R. Soc. Lond. B Biol. Sci. 355, 253–265 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Friston, K. J. The labile brain. II. Transients, complexity and selection. Philos. Trans. R. Soc. Lond. B Biol. Sci. 355, 237–252 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Friston, K. J. The labile brain. I. Neuronal transients and nonlinear coupling. Philos. Trans. R Soc. Lond. B Biol. Sci. 355, 215–236 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Fries, P., Roelfsema, P. R., Engel, A. K., Konig, P. & Singer, W. Synchronization of oscillatory responses in visual cortex. Proc. Natl Acad. Sci. USA 94, 12699–12704 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Franco, L., Rolls, E. T., Aggelopoulos, N. C. & Treves, A. The use of decoding to analyze the contribution to the information of the correlations between the firing of simultaneously recorded neurons. Exp. Brain Res. 155, 370–384 (2004).

    Article  PubMed  Google Scholar 

  92. Aggelopoulos, N. C., Franco, L. & Rolls, E. T. Object perception in natural scenes: encoding by inferior temporal cortex simultaneously recorded neurons. J. Neurophysiol. 93, 1342–1357 (2005).

    Article  PubMed  Google Scholar 

  93. Softky, W. R. & Koch, C. The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs. J. Neurosci. 13, 334–350 (1993).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Shadlen, M. N. & Newsome, W. T. Noise, neural codes and cortical organization. Curr. Opin. Neurobiol. 4, 569–579 (1994).

    Article  CAS  PubMed  Google Scholar 

  95. Durstewitz, D. & Gabriel, T. Dynamical basis of irregular spiking in NMDA-driven prefrontal cortex neurons. Cereb. Cortex 17, 894–908 (2007).

    Article  PubMed  Google Scholar 

  96. Niessing, J. et al. Hemodynamic signals correlate tightly with synchronized gamma oscillations. Science 309, 948–951 (2005).

    Article  CAS  PubMed  Google Scholar 

  97. Chawla, D., Lumer, E. D. & Friston, K. J. The relationship between synchronization among neuronal populations and their mean activity levels. Neural. Comput. 11, 1389–1411 (1999).

    Article  CAS  PubMed  Google Scholar 

  98. Mitzdorf, U. & Singer, W. Prominent excitatory pathways in the cat visual cortex (A 17 and A 18): a current source density analysis of electrically evoked potentials. Exp. Brain Res. 33, 371–394 (1978).

    Article  CAS  PubMed  Google Scholar 

  99. Logothetis, N. K., Pauls, J., Augath, M., Trinath, T. & Oeltermann, A. Neurophysiological investigation of the basis of the fMRI signal. Nature 412, 150–157 (2001).

    Article  CAS  PubMed  Google Scholar 

  100. Kwon, J. S. et al. Gamma frequency-range abnormalities to auditory stimulation in schizophrenia. Arch. Gen. Psychiatry 56, 1001–1005 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Uhlhaas, P. J. & Singer, W. Neural synchrony in brain disorders: relevance for cognitive dysfunctions and pathophysiology. Neuron 52, 155–168 (2006).

    Article  CAS  PubMed  Google Scholar 

  102. Izhikevich, E. M. Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting (MIT Press, Cambridge, Massachusetts, 2007).

    Google Scholar 

  103. Winterer, G. et al. Cortical activation, signal-to-noise ratio and stochastic resonance during information processing in man. Clin. Neurophysiol. 110, 1193–1203 (1999).

    Article  CAS  PubMed  Google Scholar 

  104. Tateno, T., Harsch, A. & Robinson, H. P. Threshold firing frequency-current relationships of neurons in rat somatosensory cortex: type 1 and type 2 dynamics. J. Neurophysiol. 92, 2283–2294 (2004).

    Article  CAS  PubMed  Google Scholar 

  105. Ward, L. M., Doesburg, S. M., Kitajo, K., MacLean, S. E. & Roggeveen, A. B. Neural synchrony in stochastic resonance, attention, and consciousness. Can. J. Exp. Psychol. 60, 319–326 (2006).

    Article  PubMed  Google Scholar 

  106. Melloni, L. et al. Synchronization of neural activity across cortical areas correlates with conscious perception. J. Neurosci. 27, 2858–2865 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Winterer, G. et al. Prefrontal electrophysiologic “noise” and catechol-O-methyltransferase genotype in schizophrenia. Biol. Psychiatry 60, 578–584 (2006). This study demonstrated that dopamine reduces electrophysiological noise.

    Article  CAS  PubMed  Google Scholar 

  108. Krishnan, G. P. et al. Steady state visual evoked potential abnormalities in schizophrenia. Clin. Neurophysiol. 116, 614–624 (2005).

    Article  PubMed  Google Scholar 

  109. Kim, D. et al. A method for multi-group inter-participant correlation: abnormal synchrony in patients with schizophrenia during auditory target detection. Neuroimage 39, 1129–1141 (2008).

    Article  CAS  PubMed  Google Scholar 

  110. Rolls, E. T., Thorpe, S. J., Boytim, M., Szabo, I. & Perrett, D. I. Responses of striatal neurons in the behaving monkey. 3. Effects of iontophoretically applied dopamine on normal responsiveness. Neuroscience 12, 1201–1212 (1984).

    Article  CAS  PubMed  Google Scholar 

  111. Brozoski, T. J., Brown, R. M., Rosvold, H. E. & Goldman, P. S. Cognitive deficit caused by regional depletion of dopamine in prefrontal cortex of rhesus monkey. Science 205, 929–932 (1979).

    Article  CAS  PubMed  Google Scholar 

  112. Sawaguchi, T., Matsumura, M. & Kubota, K. Dopamine enhances the neuronal activity of spatial short-term memory task in the primate prefrontal cortex. Neurosci. Res. 5, 465–473 (1988).

    Article  CAS  PubMed  Google Scholar 

  113. Sawaguchi, T., Matsumura, M. & Kubota, K. Catecholaminergic effects on neuronal activity related to a delayed response task in monkey prefrontal cortex. J. Neurophysiol. 63, 1385–1400 (1990).

    Article  CAS  PubMed  Google Scholar 

  114. Sawaguchi, T. & Goldman-Rakic, P. S. The role of D1-dopamine receptor in working memory: local injections of dopamine antagonists into the prefrontal cortex of rhesus monkeys performing an oculomotor delayed-response task. J. Neurophysiol. 71, 515–528 (1994).

    Article  CAS  PubMed  Google Scholar 

  115. Sawaguchi, T. & Goldman-Rakic, P. S. D1 dopamine receptors in prefrontal cortex: involvement in working memory. Science 251, 947–950 (1991).

    Article  CAS  PubMed  Google Scholar 

  116. Goldman-Rakic, P. S., Muly, E. C. & Williams, G. V. D1 receptors in prefrontal cells and circuits. Brain Res. Brain Res. Rev. 31, 295–301 (2000).

    Article  CAS  PubMed  Google Scholar 

  117. Williams, G. V. & Goldman-Rakic, P. S. Modulation of memory fields by dopamine D1 receptors in prefrontal cortex. Nature 376, 572–575 (1995).

    Article  CAS  PubMed  Google Scholar 

  118. Vijayraghavan, S., Wang, M., Birnbaum, S. G., Williams, G. V. & Arnsten, A. F. Inverted-U dopamine D1 receptor actions on prefrontal neurons engaged in working memory. Nature Neurosci. 10, 376–384 (2007).

    Article  CAS  PubMed  Google Scholar 

  119. Castner, S. A., Williams, G. V. & Goldman-Rakic, P. S. Reversal of antipsychotic-induced working memory deficits by short-term dopamine D1 receptor stimulation. Science 287, 2020–2022 (2000).

    Article  CAS  PubMed  Google Scholar 

  120. Floresco, S. B., Magyar, O., Ghods-Sharifi, S., Vexelman, C. & Tse, M. T. Multiple dopamine receptor subtypes in the medial prefrontal cortex of the rat regulate set-shifting. Neuropsychopharmacology 31, 297–309 (2006).

    Article  CAS  PubMed  Google Scholar 

  121. Stemme, A., Deco, G. & Busch, A. The neuronal dynamics underlying cognitive flexibility in set shifting tasks. J. Comput. Neurosci. 23, 313–331 (2007).

    Article  PubMed  Google Scholar 

  122. Akil, M. et al. Lamina-specific alterations in the dopamine innervation of the prefrontal cortex in schizophrenic subjects. Am. J. Psychiatry 156, 1580–1589 (1999).

    Article  CAS  PubMed  Google Scholar 

  123. Okubo, Y., Suhara, T., Sudo, Y. & Toru, M. Possible role of dopamine D1 receptors in schizophrenia. Mol. Psychiatry 2, 291–292 (1997).

    Article  CAS  PubMed  Google Scholar 

  124. Okubo, Y. et al. Decreased prefrontal dopamine D1 receptors in schizophrenia revealed by PET. Nature 385, 634–636 (1997).

    Article  CAS  PubMed  Google Scholar 

  125. Abi-Dargham, A. et al. Prefrontal dopamine D1 receptors and working memory in schizophrenia. J. Neurosci. 22, 3708–3719 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Egan, M. F. et al. Effect of COMT Val108/158 Met genotype on frontal lobe function and risk for schizophrenia. Proc. Natl Acad. Sci. USA 98, 6917–6922 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Winterer, G. et al. Instability of prefrontal signal processing in schizophrenia. Am. J. Psychiatry 163, 1960–1968 (2006). This was the first report on abnormal noise in schizophrenia, as measured with fMRI.

    Article  PubMed  Google Scholar 

  128. Winterer, G. et al. COMT genotype predicts BOLD signal and noise characteristics in prefrontal circuits. Neuroimage 32, 1722–1732 (2006). This study demonstrated that dopamine reduces cortical noise, as measured with fMRI.

    Article  PubMed  Google Scholar 

  129. Durstewitz, D. & Seamans, J. K. The dual-state theory of prefrontal cortex dopamine function with relevance to COMT genotypes and schizophrenia. Biol. Psychiatry 11 Jul 2008 (doi:10.1016/j.biopsych.2008.05.015). This recent paper analysed the role of dopamine in the computations that are performed by cortical networks, and its relevance to schizophrenia.

    Article  CAS  PubMed  Google Scholar 

  130. Muly, E. C., Szigeti, K. & Goldman-Rakic, P. S. D1 receptor in interneurons of macaque prefrontal cortex: distribution and subcellular localization. J. Neurosci. 18, 10553–10565 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. Winterer, G. Prefrontal dopamine signaling in schizophrenia — the corticocentric model. Pharmacopsychiatry 40 (Suppl. 1), S45–S53 (2007).

    Article  CAS  Google Scholar 

  132. Lewis, D. A. & Gonzalez-Burgos, G. Pathophysiologically based treatment interventions in schizophrenia. Nature Med. 12, 1016–1022 (2006).

    Article  CAS  PubMed  Google Scholar 

  133. Hebb, D. O. The Organization of Behavior: A Neuropsychological Theory (Wiley, New York, 1949).

    Google Scholar 

Download references

Acknowledgements

This work was supported by the European Union, grant EC005-024; by the Spanish Research Project BFU2007-61710/BFI and CONSOLIDER INGENIO 2010 (G.D.); by two grants from the German Research Foundation (Deutsche Forschungsgemeinschaft): Wi1316/2-1 and Wi1316/2-2 (G.W.); by a Fellowship from the Fogarty Foundation (G.W.); by the Oxford McDonnell Centre for Cognitive Neuroscience (E.T.R.); and by the Boehringer Ingelheim Fonds (M.L.).

Author information

Authors and Affiliations

  1. Oxford Centre for Computational Neuroscience, Oxford, UK

    Edmund T. Rolls

  2. Computational Neuroscience, Universitat Pompeu Fabra, Barcelona, 08003, Spain

    Marco Loh & Gustavo Deco

  3. Institució Catalana de Recerca i Estudis Avançats, Barcelona, 08010, Spain

    Gustavo Deco

  4. Department of Psychiatry, Laboratory of Neuropsychiatric Research, Heinrich-Heine University, Duesseldorf, 40629, Germany

    Georg Winterer

  5. Institute of Neurosciences and Biophysics, Juelich Research Centre, Juelich, 52425, Germany

    Georg Winterer

Authors
  1. Edmund T. Rolls
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marco Loh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gustavo Deco
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Georg Winterer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Edmund T. Rolls.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary information S1 (box)

A neurodynamical model of schizophrenia. (PDF 314 kb)

Related links

Related links

FURTHER INFORMATION

Edmund Rolls' homepage

SUPPLEMENTARY INFORMATION

See online article

S1 (box)

Glossary

Dysexecutive syndrome

A disorder of the planning and organization of actions that is typically produced by damage to the prefrontal cortex.

Time constant

The time the system takes to reach 1/e of its initial value.

Attractor networks

Neural networks in which sets of neurons with strong interconnections have stable high-firing-rate states into which they can be attracted by memory-retrieval cues. The strong interconnections are formed during a learning period in which a set of neurons is active. An attractor net can be used to implement a short-term memory.

Long-term potentiation

(LTP). A long-term increase in synaptic strength.

Poissonian

With a Poisson distribution.

Hopfield equation

This is a measure of the stability of an attractor state that reflects the depth of the basin of attraction.

Disconnection hypothesis of schizophrenia

A hypothesis which suggests that brain regions such as the frontal and temporal lobes become relatively disconnected in schizophrenia.

Gain function

The sensitivity of a working-memory system to external stimuli in some models.

Multi-compartment Hodgkin–Huxley neurons

Models of neurons with separate biophysical parameters and modelling for the different parts of neurons, including different parts of the dendritic tree.

Basins of attraction

The shape in state space of the gradients of the low-energy, stable states into which a subset of neurons in an attractor network can be drawn.

Blood-oxygen-level-dependent (BOLD) brain response

A signal that can be extracted with fMRI and that reflects the change in the amount of deoxyhaemoglobin that is induced by changes in the activity of neurons and their synapses in a region of the brain. The signal thus reflects the activity in a local brain region.

Local field potentials

The potentials in a local brain area that reflect the activity of many neurons and their synaptic inputs.

Event-related potentials

The potentials elicited in a brain area by the activity of neurons and their synaptic inputs in response to an event or stimulus.

Gamma frequency band

The spectral frequency band of the electrical activity of the brain that is close to 40 Hz.

Phase locking

Time locking of brain oscillations to (sensory) stimuli or (motor) responses.

Broadband phase synchrony

A measure of whether the energy in different spectral frequency bands of the electrical activity of the brain is in phase.

Catechol-O-methyl-transferase (COMT) gene

The gene that encodes the COMT enzyme, which provides one of the ways in which dopamine is degraded by methylation and therefore removed from the activity at a synapse. If COMT is too active there are likely to be low levels of dopamine in the prefrontal cortex, and this might be related to the cognitive symptoms of schizophrenia.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Rolls, E., Loh, M., Deco, G. et al. Computational models of schizophrenia and dopamine modulation in the prefrontal cortex. Nat Rev Neurosci 9, 696–709 (2008). https://doi.org/10.1038/nrn2462

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrn2462

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing