Stochastic dynamics as a principle of brain function

Progress in Neurobiology

Volumn 88, Issue 1, 2009, Pages 1-16

  Deco, Gustavo ^a   Rolls, Edmund T ^b   Romo, Ranulfo ^c  

^a UNIVERSITAT POMPEU FABRA   (Spain)

^b UNIVERSITY OF OXFORD   (United Kingdom)

^c INSTITUTO DE FISIOLOGÍA CELULAR   (Mexico)

Author keywords

Decision making; Noise; Probabilistic behavior; Signal detection; Stochastic neurodynamics

Indexed keywords

ATTENTION; BEHAVIOR; BRAIN CORTEX; BRAIN FUNCTION; BRAIN NERVE CELL; DECISION MAKING; DYNAMICS; HUMAN; NERVE CELL NETWORK; NEUROPHYSIOLOGY; NOISE; NONHUMAN; PRIORITY JOURNAL; PROBABILITY; RECALL; REVIEW; SHORT TERM MEMORY; SIGNAL DETECTION; STOCHASTIC MODEL;

ANIMALS; BRAIN; DECISION MAKING; HUMANS; MODELS, NEUROLOGICAL; NEURAL NETWORKS (COMPUTER); STOCHASTIC PROCESSES;

EID: 65049089933     PISSN: 03010082     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.pneurobio.2009.01.006     Document Type: Review

References (103)

1. Amit D.J., and Brunel N. Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex. Cereb. Cortex 7 (1997) 237-252
2. Barto, A.G., 1985. Learning by statistical cooperation of self-interested neuron-like computing elements. In: Computer and Information Science, vol. 85. Massachusetts, Amherst.
3. Battaglia F.P., and Treves A. Stable and rapid recurrent processing in realistic auto-associative memories. Neural Comput. 10 (1998) 431-450
4. Blackerby R.F. Application of Chaos Theory to Psychological Models (1993), Performance Strategies Publications, Austin, TX
5. Blake R., and Logothetis N.K. Visual competition. Nat. Rev. Neurosci. 3 (2002) 13-21
6. Bogacz R., Brown E., Moehlis J., Holmes P., and Cohen J.D. The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks. Psychol. Rev. 113 (2006) 700-765
7. Brody C.D., Romo R., and Kepecs A. Basic mechanisms for graded persistent activity: discrete attractors, continuous attractors, and dynamic representations. Curr. Opin. Neurobiol. 13 (2003) 204-211
8. Brown S., and Heathcote A. A ballistic model of choice response time. Psychol. Rev. 112 (2005) 117-128
9. Brunel N., and Wang X.J. Effects of neuromodulation in a cortical network model of object working memory dominated by recurrent inhibition. J. Comput. Neurosci. 11 (2001) 63-85
10. Carpenter R.H.S., and Williams M.L. Neural computation of log likelihood in control of saccadic eye movements. Nature 377 (1995) 59-62
11. Churchland A.K., Kiani R., and Shadlen M.N. Decision-making with multiple alternatives. Nat. Neurosci. 11 (2008) 693-702
12. Corchs S., and Deco G. Large-scale neural model for visual attention: integration of experimental single-cell and fmri data. Cereb. Cortex 12 (2002) 339-348
13. Corrado G.S., Sugrue L.P., Seung H.S., and Newsome W.T. Linear-nonlinear-Poisson models of primate choice dynamics. J. Exp. Anal. Behav. 84 (2005) 581-617
14. Dawkins M.S. Unravelling Animal Behaviour (1995), Longman, Harlow
15. de Lafuente V., and Romo R. Neuronal correlates of subjective sensory experience. Nat. Neurosci. 12 (2005) 1698-1703
16. de Lafuente V., and Romo R. Neural correlate of subjective sensory experience gradually builds up across cortical areas. Proc. Natl. Acad. Sci. U.S.A. 103 (2006) 14266-14271
17. Deco G., and Marti D. Deterministic analysis of stochastic bifurcations in multi-stable neurodynamical systems. Biol. Cybern. 96 (2007) 487-496
18. Deco G., and Marti D. Extended method of moments for deterministic analysis of stochastic multistable neurodynamical systems. Phys. Rev. E: Stat. Nonlin. Soft Matter Phys. 75 (2007) 031913
19. Deco G., Perez-Sanagustin M., de Lafuente V., and Romo R. Perceptual detection as a dynamical bistability phenomenon: a neurocomputational correlate of sensation. Proc. Natl. Acad. Sci. U.S.A. 104 (2007) 20073-20077
20. Deco G., and Rolls E.T. Attention and working memory: a dynamical model of neuronal activity in the prefrontal cortex. Eur. J. Neurosci. 18 (2003) 2374-2390
21. Deco G., and Rolls E.T. Neurodynamics of biased competition and co-operation for attention: a model with spiking neurons. J. Neurophysiol. 94 (2005) 295-313
22. Deco G., and Rolls E.T. Sequential memory: a putative neural and synaptic dynamical mechanism. J. Cogn. Neurosci. 17 (2005) 294-307
23. Deco G., and Rolls E.T. Synaptic and spiking dynamics underlying reward reversal in orbitofrontal cortex. Cereb. Cortex 15 (2005) 15-30
24. Deco G., and Rolls E.T. Decision-making and Weber's law: a neurophysiological model. Eur. J. Neurosci. 24 (2006) 901-916
25. Deco G., Rolls E.T., and Horwitz B. "What" and "Where" in visual working memory: a computational neurodynamical perspective for integrating fmri and single-neuron data. J. Cogn. Neurosci. 16 (2004) 683-701
26. Deco G., Scarano L., and Soto-Faraco S. Weber's law in decision making: integrating behavioral data in humans with a neurophysiological model. J. Neurosci. 27 (2007) 11192-11200
27. Faisal A.A., Selen L.P., and Wolpert D.M. Noise in the nervous system. Nat. Rev. Neurosci. 9 (2008) 292-303
28. Glimcher P.W. The neurobiology of visual-saccadic decision making. Annu. Rev. Neurosci. 26 (2003) 133-179
29. Glimcher P.W. Indeterminacy in brain and behavior. Annu. Rev. Psychol. 56 (2005) 25-56
30. Gold J.I., and Shadlen M.N. Representation of a perceptual decision in developing oculomotor commands. Nature 404 (2000) 390-394
31. Gold J.I., and Shadlen M.N. Banburismus and the brain: decoding the relationship between sensory stimuli decisions reward. Neuron 36 (2002) 299-308
32. Green D., and Swets J. Signal Detection Theory and Psychophysics (1966), Wiley, New York
33. Heinrichs D.W. Antidepressants and the chaotic brain: implications for the respectful treatment of selves. Phil. Psychiatr. Psychol. 12 (2005) 215-227
34. Hernandez A., Salinas E., Garcia R., and Romo R. Discrimination in the sense of flutter: new psychophysical measurements in monkeys. J. Neurosci. 17 (1997) 6391-6400
35. Hernandez A., Zainos A., and Romo R. Temporal evolution of a decision-making process in medial premotor cortex. Neuron 33 (2002) 959-972
36. Horne J. Sleepfaring: A Journey Through the Science of Sleep (2006), Oxford University Press, Oxford
37. Hubel D.H., and Wiesel T.N. Receptive fields and functional architecture of macaque monkey striate cortex. J. Physiol. 195 (1968) 215-243
38. Jackson B.S. Including long-range dependence in integrate-and-fire models of the high interspike-interval variability of cortical neurons. Neural Comput. 16 (2004) 2125-2195
39. Kacelnik A., and Brito e Abreu F. Risky choice and Weber's law. J. Theor. Biol. 194 (1998) 289-298
40. In: Kandel E.R., Schwartz J.H., and Jessel T.H. (Eds). Principles of Neural Science (2000), McGraw-Hill, New York
41. Kramers H. Brownian motion in a field of force and the diffusion model of chemical reactions. Physicas 7 (1940) 284-304
42. In: Krebs J.R., and Davies N.B. (Eds). Behavioural Ecology (1991), Blackwell, Oxford
43. La Camera G., Rauch A., Luscher H.R., Senn W., and Fusi S. Minimal models of adapted neuronal response to in vivo-like input currents. Neural Comput. 16 (2004) 2101-2124
44. Lewis M.D. Bridging emotion theory and neurobiology through dynamic systems modeling. Behav. Brain Sci. 28 (2005) 169-194 (discussion 194-245)
45. Loh M., Rolls E.T., and Deco G. A dynamical systems hypothesis of schizophrenia. PLoS Comput. Biol. 3 (2007) e228 doi:210.1371/journal. pcbi.0030228
46. Loh M., Rolls E.T., and Deco G. Statistical fluctuations in attractor networks related to schizophrenia. Pharmacopsychiatry 40 (2007) S78-S84
47. Machens C.K., Romo R., and Brody C.D. Flexible control of mutual inhibition: a neural model of two-interval discrimination. Science 307 (2005) 1121-1124
48. Maier A., Logothetis N.K., and Leopold D.A. Global competition dictates local suppression in pattern rivalry. J. Vis. 5 (2005) 668-677
49. Marti D., Deco G., Del Giudice P., and Mattia M. Reward-based probabilistic decision-making: mean-field predictions and spiking simulations. Neurocomputing 69 (2006) 1175-1178
50. Marti D., Deco G., Mattia M., Gigante G., and Del Giudice P. A fluctuation-driven mechanism for slow decision processes in reverberant networks. PLoS ONE 3 (2008) e2534
51. Mattia M., and Del Giudice P. Attention and working memory: a dynamical model of neuronal activity in the prefrontal cortex. Phys. Rev. E 66 (2002) 51917-51919
52. Maynard Smith J. Evolution and the Theory of Games (1982), Cambridge University Press, Cambridge
53. Maynard Smith J. Game theory and the evolution of behaviour. Behav. Brain Sci. 7 (1984) 95-125
54. Mountcastle V.B., Talbot W.H., Darian-Smith I., and Kornhuber H.H. Neural basis of the sense of flutter-vibration. Science 155 (1967) 597-600
55. Mountcastle V.B., Talbot W.H., Sakata H., and Hyvarinen J. Cortical neuronal mechanisms in flutter-vibration studied in unanesthetized monkeys. Neuronal periodicity and frequency discrimination. J. Neurophysiol. 32 (1969) 452-484
56. Newsome W.T., Britten K.H., and Movshon J.A. Neuronal correlates of a perceptual decision. Nature 341 (1989) 52-54
57. Panzeri S., Rolls E.T., Battaglia F., and Lavis R. Speed of information retrieval in multilayer networks of integrate-and-fire neurons. Netw. Comput. Neural Syst. 12 (2001) 423-440
58. Peled A. From plasticity to complexity: a new diagnostic method for psychiatry. Med. Hypotheses 63 (2004) 110-114
59. Platt M.L., and Glimcher P.W. Neural correlates of decision variables in parietal cortex. Nature 400 (1999) 233-238
60. Ratcliff R., Van Zandt T., and McKoon G. Connectionist and diffusion models of reaction time. Psychol. Rev. 106 (1999) 261-300
61. Renart A., Brunel N., and Wang X.-J. Mean field theory of irregularly spiking neuronal populations and working memory in recurrent cortical networks. In: Feng J. (Ed). Computational Neuroscience: A Comprehensive Approach (2003), Chapman and Hall, Boca-Raton 431-490
62. Ricciardi L.M., and Sacerdote L. The Ornstein-Uhlenbeck process as a model for neuronal activity. I. Mean and variance of the firing time. Biol. Cybern. 35 (1979) 1-9
63. Riley M.A., and Turvey M.T. The self-organizing dynamics of intentions and actions. Am. J. Psychol. 114 (2001) 160-169
64. Rolls E.T. Emotion Explained (2005), Oxford University Press, Oxford
65. Rolls E.T. A computational neuroscience approach to consciousness. Neural Netw. 20 (2007) 962-982
66. Rolls E.T. Emotion, higher order syntactic thoughts, and consciousness. In: Weiskrantz L., and Davies M.K. (Eds). Frontiers of Consciousness (2008), Oxford University Press, Oxford 131-167
67. Rolls E.T. Memory, Attention, and Decision-making: A Unifying Computational Neuroscience Approach (2008), Oxford University Press, Oxford
68. Rolls E.T., and Deco G. Computational Neuroscience of Vision (2002), Oxford University Press, Oxford
69. Rolls, E.T., Deco, G., in preparation. Stochastic Dynamics as a Principle of Brain Function.
70. Rolls E.T., and Kesner R.P. A computational theory of hippocampal function, and empirical tests of the theory. Prog. Neurobiol. 79 (2006) 1-48
71. Rolls E.T., Loh M., and Deco G. An attractor hypothesis of obsessive-compulsive disorder. Eur. J. Neurosci. 28 (2008) 782-793
72. Rolls E.T., Loh M., Deco G., and Winterer G. Computational models of schizophrenia and dopamine modulation in the prefrontal cortex. Nat. Rev. Neurosci. 9 (2008) 696-709
73. Rolls E.T., McCabe C., and Redoute J. Expected value, reward outcome, and temporal difference error representations in a probabilistic decision task. Cereb. Cortex 18 (2008) 652-663
74. Rolls E.T., and Treves A. Neural Networks and Brain Function (1998), Oxford University Press, Oxford
75. Rolls E.T., Tromans J., and Stringer S.M. Spatial scene representations formed by self-organizing learning in a hippocampal extension of the ventral visual system. Eur. J. Neurosci. 28 (2008) 2116-2127
76. Romo R., Brody C.D., Hernandez A., and Lemus L. Neuronal correlates of parametric working memory in the prefrontal cortex. Nature 399 (1999) 470-473
77. Romo R., Hernandez A., and Zainos A. Neuronal correlates of a perceptual decision in ventral premotor cortex. Neuron 41 (2004) 165-173
78. Romo R., Hernandez A., Zainos A., Lemus L., and Brody C.D. Neuronal correlates of decision-making in secondary somatosensory cortex. Nat. Neurosci. 5 (2002) 1217-1225
79. Romo R., Hernandez A., Zainos A., and Salinas E. Somatosensory discrimination based on cortical microstimulation. Nature 392 (1998) 387-390
80. Romo R., Hernandez A., Zainos A., and Salinas E. Correlated neuronal discharges that increase coding efficiency during perceptual discrimination. Neuron 38 (2003) 649-657
81. Romo R., and Salinas E. Touch and go: decision-making mechanisms in somatosensation. Annu. Rev. Neurosci. 24 (2001) 107-137
82. Romo R., and Salinas E. Flutter discrimination: neural codes, perception, memory and decision making. Nat. Rev. Neurosci. 4 (2003) 203-218
83. Roxin A., and Ledberg A. Neurobiological models of two-choice decision making can be reduced to a one-dimensional nonlinear diffusion equation. PLoS Comput. Biol. 4 (2008) e1000046
84. Schall J.D. Neural basis of deciding, choosing and acting. Nat. Rev. Neurosci. 2 (2001) 33-42
85. Shadlen M.N., and Newsome W.T. Motion perception: seeing and deciding. Proc. Natl. Acad. Sci. U.S.A. 93 (1996) 628-633
86. Shulman G.L., Ollinger J.M., Linenweber M., Petersen S.E., and Corbetta M. Multiple neural correlates of detection in the human brain. Proc. Natl. Acad. Sci. U.S.A. 98 (2001) 313-318
87. Sigala N., and Logothetis N.K. Visual categorization shapes feature selectivity in the primate temporal cortex. Nature 415 (2002) 318-320
88. Smith P.L., and Ratcliff R. Psychology and neurobiology of simple decisions. Trends Neurosci. 27 (2004) 161-168
89. Stocks N.G. Suprathreshold stochastic resonance in multilevel threshold systems. Phys. Rev. Lett. 84 (2000) 2310-2313
90. Sugrue L.P., Corrado G.S., and Newsome W.T. Choosing the greater of two goods: neural currencies for valuation and decision making. Nat. Rev. Neurosci. 6 (2005) 363-375
91. Sutton R.S., and Barto A.G. Towards a modern theory of adaptive networks: expectation and prediction. Psychol. Rev. 88 (1981) 135-170
92. Szabo M., Almeida R., Deco G., and Stetter M. Cooperation and biased competition model can explain attentional filtering in the prefrontal cortex. Eur. J. Neurosci. 19 (2004) 1969-1977
93. Szabo M., Deco G., Fusi S., Del Giudice P., Mattia M., and Stetter M. Learning to attend: modeling the shaping of selectivity in infero-temporal cortex in a categorization task. Biol. Cybern. 94 (2006) 351-365
94. Talbot W.H., Darian-Smith I., Kornhuber H.H., and Mountcastle V.B. The sense of flutter-vibration: comparison of the human capacity with response patterns of mechanoreceptive afferents from the monkey hand. J. Neurophysiol. 31 (1968) 301-334
95. Tanji J. Sequential organization of multiple movements: involvement of cortical motor areas. Annu. Rev. Neurosci. 24 (2001) 631-651
96. Thompson K.G., Hanes D.P., Bichot N.P., and Schall J.D. Perceptual and motor processing stages identified in the activity of macaque frontal eye field neurons during visual search. J. Neurophysiol. 76 (1996) 4040-4055
97. Treves A. Mean-field analysis of neuronal spike dynamics. Network 4 (1993) 259-284
98. Tuckwell H. Introduction to Theoretical Neurobiology (1988), Cambridge University Press, Cambridge
99. Usher M., and McClelland J.L. The time course of perceptual choice: the leaky, competing accumulator model. Psychol. Rev. 108 (2001) 550-592
100. Wang X.-J. Synaptic basis of cortical persistent activity: the importance of NMDA receptors to working memory. J. Neurosci. 19 (1999) 9587-9603
101. Wang X.J. Probabilistic decision making by slow reverberation in cortical circuits. Neuron 36 (2002) 955-968
102. In: Welford A.T. (Ed). Reaction Times (1980), Academic Press, London
103. Wong K.F., and Wang X.J. A recurrent network mechanism of time integration in perceptual decisions. J. Neurosci. 26 (2006) 1314-1328