Information-limiting correlations

Nature Neuroscience

Volumn 17, Issue 10, 2014, Pages 1410-1417

  Moreno Bote, Rubén ^a,b   Beck, Jeffrey ^c   Kanitscheider, Ingmar ^d   Pitkow, Xaq ^c,e,f   Latham, Peter ^g   Pouget, Alexandre ^c,d,g  

^a PARC SANITARI SANT JOAN DE DÉU   (Spain)

^b CENTRO DE INVESTIGACIÓN BIOMÉDICA EN RED DE SALUD MENTAL CIBERSAM   (Spain)

^c UNIVERSITY OF ROCHESTER   (United States)

^d UNIVERSITY OF GENEVA   (Switzerland)

^e BAYLOR COLLEGE OF MEDICINE   (United States)

^f Rice University   (United States)

^g UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CORRELATION ANALYSIS; CORRELATION COEFFICIENT; INFORMATION LIMITING CORRELATION; INFORMATION PROCESSING; NERVE CELL MEMBRANE POTENTIAL; PERCEPTIVE DISCRIMINATION; PRIORITY JOURNAL; TUNING CURVE; WHITE NOISE; WORKING MEMORY; BIOLOGICAL MODEL; BRAIN; COMPUTER SIMULATION; CYTOLOGY; HUMAN; NERVE CELL; PHYSIOLOGY; STATISTICS;

BRAIN; COMPUTER SIMULATION; HUMANS; MODELS, NEUROLOGICAL; NEURONS; STATISTICS AS TOPIC;

EID: 84914704885     PISSN: 10976256     EISSN: 15461726     Source Type: Journal    
DOI: 10.1038/nn.3807     Document Type: Article

References (41)

1. Tolhurst, D.J., Movshon, J.A. & Dean, A.D. The statistical reliability of signals in single neurons in cat and monkey visual cortex. Vision Res. 23, 775-785 (1983).
2. Averbeck, B.B., Latham, P.E. & Pouget, A. Neural correlations, population coding and computation. Nat. Rev. Neurosci. 7, 358-366 (2006).
3. Zohary, E., Shadlen, M.N. & Newsome, W.T. Correlated neuronal discharge rate and its implication for psychophysical performance. Nature 370, 140-143 (1994).
4. Maynard, E.M. et al. Neuronal interactions improve cortical population coding of movement direction. J. Neurosci. 19, 8083-8093 (1999).
5. Averbeck, B.B. & Lee, D. Neural noise and movement-related codes in the macaque supplementary motor area. J. Neurosci. 23, 7630-7641 (2003).
6. Gu, Y. et al. Perceptual learning reduces interneuronal correlations in macaque visual cortex. Neuron 71, 750-761 (2011).
7. Adibi, M., McDonald, J.S., Clifford, C.W. & Arabzadeh, E. Adaptation improves neural coding efficiency despite increasing correlations in variability. J. Neurosci. 33, 2108-2120 (2013).
8. Abbott, L.F. & Dayan, P. The effect of correlated variability on the accuracy of a population code. Neural Comput. 11, 91-101 (1999).
9. Renart, A. et al. The asynchronous state in cortical circuits. Science 327, 587-590 (2010).
10. Cohen, M.R. & Maunsell, J.H. Attention improves performance primarily by reducing interneuronal correlations. Nat. Neurosci. 12, 1594-1600 (2009).
11. Mitchell, J.F., Sundberg, K.A. & Reynolds, J.H. Spatial attention decorrelates intrinsic activity fluctuations in macaque area V4. Neuron 63, 879-888 (2009).
12. Ecker, A.S. et al. Decorrelated neuronal firing in cortical microcircuits. Science 327, 584-587 (2010).
13. Sompolinsky, H., Yoon, H., Kang, K. & Shamir, M. Population coding in neuronal systems with correlated noise. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 64, 051904 (2001).
14. Yoon, H. & Sompolinsky, H. The effect of correlations on the Fisher information of population codes. in Advances in Neural Information Processing Systems (eds. Kearns, M.S., Solla, S. & Cohn, D.A.) 167-173 (MIT Press, Cambridge, Massachusetts, 1999).
15. Shamir, M. & Sompolinsky, H. Implications of neuronal diversity on population coding. Neural Comput. 18, 1951-1986 (2006).
16. Ecker, A.S., Berens, P., Tolias, A.S. & Bethge, M. The effect of noise correlations in populations of diversely tuned neurons. J. Neurosci. 31, 14272-14283 (2011).
17. Papoulis, A. Probability, Random Variables and Stochastic Process (McGraw-Hill, New York, 1991).
18. Paradiso, M.A. A theory of the use of visual orientation information which exploits the columnar structure of striate cortex. Biol. Cybern. 58, 35-49 (1988).
19. Graf, A.B., Kohn, A., Jazayeri, M. & Movshon, J.A. Decoding the activity of neuronal populations in macaque primary visual cortex. Nat. Neurosci. 14, 239-245 (2011).
20. Berens, P. et al. A fast and simple population code for orientation in primate V1. J. Neurosci. 32, 10618-10626 (2012).
21. Maimon, G. & Assad, J.A. Beyond Poisson: increased spike-time regularity across primate parietal cortex. Neuron 62, 426-440 (2009).
22. Gershon, E.D., Wiener, M.C., Latham, P.E. & Richmond, B.J. Coding strategies in monkey V1 and inferior temporal cortices. J. Neurophysiol. 79, 1135-1144 (1998).
23. Qi, X.L. & Constantinidis, C. Variability of prefrontal neuronal discharges before and after training in a working memory task. PLoS ONE 7, e41053 (2012).
24. Churchland, M.M. et al. Stimulus onset quenches neural variability: a widespread cortical phenomenon. Nat. Neurosci. 13, 369-378 (2010).
25. Beck, J., Bejjanki, V.R. & Pouget, A. Insights from a simple expression for linear fisher information in a recurrently connected population of spiking neurons. Neural Comput. 23, 1484-1502 (2011).
26. Churchland, M.M. & Shenoy, K.V. Temporal complexity and heterogeneity of single-neuron activity in premotor and motor cortex. J. Neurophysiol. 97, 4235-4257 (2007).
27. Berens, P., Ecker, A.S., Gerwinn, S., Tolias, A.S. & Bethge, M. Reassessing optimal neural population codes with neurometric functions. Proc. Natl. Acad. Sci. USA 108, 4423-4428 (2011).
28. 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 74, 30-39 (2012).
29. Shadlen, M.N. & Newsome, W.T. The variable discharge of cortical neurons: Implications for connectivity, computation and information coding. J. Neurosci. 18, 3870-3896 (1998).
30. Romo, R., Hernandez, A., Zainos, A. & Salinas, E. Correlated neuronal discharges that increase coding efficiency during perceptual discrimination. Neuron 38, 649-657 (2003).
31. Goris, R.L., Movshon, J.A. & Simoncelli, E.P. Partitioning neuronal variability. Nat. Neurosci. 17, 858-865 (2014).
32. Ecker, A.S. et al. State dependence of noise correlations in macaque primary visual cortex. Neuron 82, 235-248 (2014).
33. Kohn, A. & Smith, M.A. Stimulus dependence of neuronal correlation in primary visual cortex of the macaque. J. Neurosci. 25, 3661-3673 (2005).
34. Wimmer, K., Nykamp, D.Q., Constantinidis, C. & Compte, A. Bump attractor dynamics in prefrontal cortex explains behavioral precision in spatial working memory. Nat. Neurosci. 17, 431-439 (2014).
35. Huang, X. & Lisberger, S.G. Noise correlations in cortical area MT and their potential impact on trial-by-trial variation in the direction and speed of smooth-pursuit eye movements. J. Neurophysiol. 101, 3012-3030 (2009).
36. Ponce-Alvarez, A., Thiele, A., Albright, T.D., Stoner, G.R. & Deco, G. Stimulus-dependent variability and noise correlations in cortical MT neurons. Proc. Natl. Acad. Sci. USA 110, 13162-13167 (2013).
37. Newsome, W.T., Britten, K.H. & Movshon, J.A. Neuronal correlates of a perceptual decision. Nature 341, 52-54 (1989).
38. Shadlen, M.N., Britten, K.H., Newsome, W.T. & Movshon, T.A. A computational analysis of the relationship between neuronal and behavioral responses to visual motion. J. Neurosci. 16, 1486-1510 (1996).
39. Series, P., Latham, P. & Pouget, A. Tuning curve sharpening for orientation selectivity: coding efficiency and the impact of correlations. Nat. Neurosci. 7, 1129-1135 (2004).
40. Barlow, H. Redundancy reduction revisited. Network 12, 241-253 (2001).
41. Beck, J., Bejjanki, V.R. & Pouget, A. Insights from a simple expression for linear Fisher information in a recurrently connected population of spiking neurons. Neural Comput. 23, 1484-1502 (2011).