Variability in neural activity and behavior

Current Opinion in Neurobiology

Volumn 25, Issue , 2014, Pages 211-220

  Renart, Alfonso ^a   Machens, Christian K ^a  

^a CHAMPALIMAUD FOUNDATION   (Portugal)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYTIC METHOD; ANIMAL BEHAVIOR; BEHAVIOR; BRAIN FUNCTION; CONNECTOME; DECISION MAKING; EXTERNAL VARIABILITY; HUMAN; INTERNAL VARIABILITY; NERVE CELL; NERVE CELL NETWORK; NONHUMAN; PRIORITY JOURNAL; REVIEW; ANIMAL; NERVOUS SYSTEM FUNCTION; PHYSIOLOGY;

ANIMALS; BEHAVIOR, ANIMAL; NERVE NET; NERVOUS SYSTEM PHYSIOLOGICAL PHENOMENA; NEURONS;

EID: 84895941908     PISSN: 09594388     EISSN: 18736882     Source Type: Journal    
DOI: 10.1016/j.conb.2014.02.013     Document Type: Review

References (73)

1. Britten K.H., Newsome W.T., Shadlen M.N., Celebrini S., Movshon J.A. A relationship between behavioral choice and the visual responses of neurons in macaque MT. Visual Neurosci 1996, 13:87-100.
2. Newsome W.T., Britten K.H., Movshon J.A. Neuronal correlates of a perceptual decision. Nature 1989, 341:52-54.
3. Bair W., Koch C. Temporal precision of spike trains in extrastriate cortex of the behaving macaque monkey. Neural Comput 1996, 8:1185-1202.
4. Shusterman R., Smear M.C., Koulakov A.A., Rinberg D. Precise olfactory responses tile the sniff cycle. Nat Neurosci 2011, 14:1039-1044.
5. Macrides F., Chorover S.L. Olfactory bulb units: activity correlated with inhalation cycles and odor quality. Science 1972, 175:84-87.
6. Niell C.M., Stryker M.P. Modulation of visual responses by behavioral state in mouse visual cortex. Neuron 2010, 65:472-479.
7. Chiappe M.E., Seelig J.D., Reiser M.B., Jayaraman V. Walking modulates speed sensitivity in drosophila motion vision. Curr Biol 2010, 20:1470-1475.
8. Cowen S., McNaughton B.L. Selective delay activity in the medial prefrontal cortex of the rat: contribution of sensorimotor information and contingency. J Neurophysiol 2007, 98:303-316.
9. Bulmer M.G., Howarth C.I., Cane V., Gregory R.L., Barlow H.B. Noise and the visual threshold. Nature 1957, 180:1403-1405.
10. Sugrue L.P., Corrado G.S., Newsome W.T. Matching behavior and the representation of value in the parietal cortex. Science 2004, 304:1782-1787.
11. Gold J.I., Law C.T.T., Connolly P., Bennur S. The relative influences of priors and sensory evidence on an oculomotor decision variable during perceptual learning. J Neurophysiol 2008, 100:2653-2668.
12. Marcos E., Pani P., Brunamonti E., Deco G., Ferraina S., Verschure P. Neural variability in premotor cortex is modulated by trial history and predicts behavioral performance. Neuron 2013, 78:249-255.
13. Churchland M.M., Byron M.Y., Cunningham J.P., Sugrue L.P., Cohen M.R., Corrado G.S., et al. Stimulus onset quenches neural variability: a widespread cortical phenomenon. Nat Neurosci 2010, 13:369-378.
14. Harris K.D., Thiele A. Cortical state and attention. Nat Rev Neurosci 2011, 12:509-523.
15. Goard M., Dan Y. Basal forebrain activation enhances cortical coding of natural scenes. Nat Neurosci 2009, 12:1444-1449.
16. Marguet S., Harris K.D. State-dependent representation of amplitude-modulated noise stimuli in rat auditory cortex. J Neurosci 2011, 31:6414-6420.
17. Renart A., de la Rocha J., Bartho P., Hollender L., Parga N., Reyes A., et al. The asynchronous state in cortical circuits. Science 2010, 327:587-590.
18. Rutishauser U., Kotowicz A., Laurent G. A method for closed-loop presentation of sensory stimuli conditional on the internal brain-state of awake animals. J Neurosci Methods 2013, 215:139-155.
19. Mitchell J.F., Sundberg K.A., Reynolds J.H. Differential attention-dependent response modulation across cell classes in macaque visual area v4. Neuron 2007, 55:131-141.
20. Mitchell J.F., Sundberg K.A., Reynolds J.H. Spatial attention decorrelates intrinsic activity fluctuations in macaque area v4. Neuron 2009, 63:879-888.
21. Cohen M.R., Maunsell J.H. Attention improves performance primarily by reducing interneuronal correlations. Nat Neurosci 2009, 12:1594-1600.
22. Herrero J., Roberts M., Delicato L., Gieselmann M., Dayan P., Thiele A. Acetylcholine contributes through muscarinic receptors to attentional modulation in v1. Nature 2008, 454:1110-1114.
23. Buffalo E.A., Fries P., Landman R., Buschman T.J., Desimone R. Laminar differences in gamma and alpha coherence in the ventral stream. Proc Natl Acad Sci U S A 2011, 108:11262-11267.
24. Roelfsema P.R., Lamme V.A., Spekreijse H. Synchrony and covariation of firing rates in the primary visual cortex during contour grouping. Nat Neurosci 2004, 7:982-991.
25. Cohen M.R., Maunsell J.H.R. A neuronal population measure of attention predicts behavioral performance on individual trials. J Neurosci 2010, 30:15241-15253.
26. Briggman K.L., Abarbanel H.D., Kristan W.B. Optical imaging of neuronal populations during decision-making. Science 2005, 307:896-901.
27. Faisal A.A., Selen L.P., Wolpert D.M. Noise in the nervous system. Nat Rev Neurosci 2008, 9:292-303.
28. van Vreeswijk C., Sompolinsky H. Chaos in neuronal networks with balanced excitatory and inhibitory activity. Science 1996, 274:1724-1726.
29. Monteforte M., Wolf F. Dynamic flux tubes form reservoirs of stability in neuronal circuits. Phys Rev X 2012, 2:041007.
30. Sompolinsky H., Crisanti A., Sommers H. Chaos in random neural networks. Phys Rev Lett 1988, 61:259-262.
31. Rajan K., Abbott L.F., Sompolinsky H. Stimulus-dependent suppression of chaos in recurrent neural networks. Phys Rev E 2011, 82:011903.
32. Brecht M., Schneider M., Sakmann B., Margrie T.W. Whisker movements evoked by stimulation of single pyramidal cells in rat motor cortex. Nature 2004, 427:704-710.
33. Yang Y., DeWeese M.R., Otazu G.H., Zador A.M. Millisecond-scale differences in neural activity in auditory cortex can drive decisions. Nat Neurosci 2008, 11:1262-1263.
34. London M., Roth A., Beeren L., Häusser M., Latham P.E. Sensitivity to perturbations in vivo implies high noise and suggests rate coding in cortex. Nature 2010, 466:123-127.
35. Zohary E., Shadlen M.N., Newsome W.T. Correlated neuronal discharge rate and its implications for psychophysical performance. Nature 1994, 370:140-143.
36. Ginzburg I., Sompolinsky H. Theory of correlations in stochastic neural networks. Phys Rev E 1994, 50:3171-3191.
37. Boerlin M., Machens C.K., Denève S. Predictive coding of dynamical variables in balanced spiking networks. PLoS Comput Biol 2013, 9:e1003258.
38. Ecker A.S., Berens P., Keliris G.A., Bethge M., Logothetis N.K., Tolias A.S. Decorrelated neuronal firing in cortical microcircuits. Science 2010, 327:584-587.
39. Gu Y., Liu S., Fetsch C.R., Yang Y., Fok S., Sunkara A., et al. Perceptual learning reduces interneuronal correlations in macaque visual cortex. Neuron 2011, 71:750-761.
40. Miura K., Mainen Z.F., Uchida N. Odor representations in olfactory cortex: distributed rate coding and decorrelated population activity. Neuron 2012, 74:1087-1098.
41. Amit D.J., Brunel N. Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex. Cereb Cortex 1997, 7:237-252.
42. Litwin-Kumar A., Doiron B. Slow dynamics and high variability in balanced cortical networks with clustered connections. Nat Neurosci 2012, 15:1498-1505.
43. Rabinovich M., Huerta R., Laurent G. Transient dynamics for neural processing (Neuroscience). Science 2008, 321:48-50.
44. Murphy B.K., Miller K.D. Balanced amplification: a new mechanism of selective amplification of neural activity patterns. Neuron 2009, 61:635-648.
45. Miller P., Katz D.B. Stochastic transitions between neural states in taste processing and decision-making. J Neurosci 2010, 30:2559-2970.
46. Durstewitz D., Vittoz N.M., Floresco S.B., Seamans J.K. Abrupt transitions between prefrontal neural ensemble states accompany behavioral transitions during rule learning. Neuron 2010, 66:438-448.
47. Beck J.M., Ma W.J., Pitkow X., Latham P.E., Pouget A. Not noisy, just wrong: the role of suboptimal inference in behavioral variability. Neuron 2012, 74:30-39.
48. Burr D.C., Santoro L. Temporal integration of optic flow, measured by contrast and coherence thresholds. Vision Res 2001, 41:1891-1899.
49. Kiani R., Shadlen M.N. Representation of confidence associated with a decision by neurons in the parietal cortex. Science 2009, 324:759-764.
50. Brunton B.W., Botvinick M.M., Brody C.D. Rats and humans can optimally accumulate evidence for decision-making. Science 2013, 340:95-98.
51. Uchida N., Mainen Z.F. Speed and accuracy of olfactory discrimination in the rat. Nat Neurosci 2003, 6:1224-1229.
52. Zariwala H.A., Kepecs A., Uchida N., Hirokawa J., Mainen Z.F. The limits of deliberation in a perceptual decision task. Neuron 2013, 78:339-351.
53. Kepecs A., Uchida N., Zariwala H.A., Mainen Z.F. Neural correlates, computation and behavioural impact of decision confidence. Nature 2008, 455:227-231.
54. Nienborg H.R., Cohen M., Cumming B.G. Decision-related activity in sensory neurons: correlations among neurons and with behavior. Annu Rev Neurosci 2012, 35:463-483.
55. Shadlen M.N., Britten K.H., Newsome W.T., Movshon J.A. A computational analysis of the relationship between neuronal and behavioral responses to visual motion. J Neurosci 1996, 16:1486-1510.
56. Haefner R.M., Gerwinn S., Macke J.H., Bethge M. Inferring decoding strategies from choice probabilities in the presence of correlated variability. Nat Neurosci 2013, 16:235-242.
57. Cohen M.R., Kohn A. Measuring and interpreting neuronal correlations. Nat Neurosci 2011, 14:811-819.
58. Wohrer A., Machens C.K. Percept and the single neuron. Nat Neurosci 2013, 16:112-113.
59. Nienborg H., Cumming B.G. Decision-related activity in sensory neurons reflects more than a neuron's causal effect. Nature 2009, 459:89-92.
60. Neri P., Levi D.M. Receptive versus perceptive fields from the reverse-correlation viewpoint. Vision Res 2006, 46:2465-2474.
61. Kiani R., Hanks T.D., Shadlen M.N. Bounded integration in parietal cortex underlies decisions even when viewing duration is dictated by the environment. J Neurosci 2008, 28:3017-3029.
62. Osborne L.C., Lisberger S.G., Bialek W. A sensory source for motor variation. Nature 2005, 437:412-416.
63. Koulakov A.A., Rinberg D.A., Tsigankov D.N. How to find decision makers in neural networks. Biol Cybern 2005, 93:447-462.
64. Sutton R.S., Barto A.G. Reinforcement Learning: An Introduction 1998, Cambridge University Press.
65. Dayan P., Niv Y. Reinforcement learning: the good, the bad and the ugly. Curr Opin Neurobiol 2008, 18:185-196.
66. Daw N., Courville A. The pigeon as particle filter. Adv Neural Inf Process Syst 2008, 20:369-376.
67. Ernst M.O., Banks M.S. Humans integrate visual and haptic information in a statistically optimal fashion. Nature 2002, 415:429-433.
68. Hoyer P.O., Hyvarinen A. Interpreting neural response variability as Monte Carlo sampling of the posterior. Adv Neural Inf Process Syst 2003, 293-300.
69. Fiser J., Berkes P., Orbán G., Lengyel M. Statistically optimal perception and learning: from behavior to neural representations. Trends Cognit Sci 2010, 14:119-130.
70. Moreno-Bote R., Knill D.C., Pouget A. Bayesian sampling in visual perception. Proc Natl Acad Sci U S A 2011, 108:12491-12496.
71. Ölveczky B.P., Andalman A.S., Fee M.S. Vocal experimentation in the juvenile songbird requires a basal ganglia circuit. PLoS Biol 2005, 3:e153.
72. Ölveczky B.P., Otchy T.M., Goldberg J.H., Aronov D., Fee M.S. Changes in the neural control of a complex motor sequence during learning. J Neurophysiol 2011, 106:386-397.
73. Wu H.G., Miyamoto Y.R., Castro L.N.G., Ölveczky B.P., Smith M.A. Temporal structure of motor variability is dynamically regulated and predicts motor learning ability. Nat Neurosci 2014, 17:312-321.