The spatial structure of correlated neuronal variability

Nature Neuroscience

Volumn 20, Issue 1, 2017, Pages 107-114

  Rosenbaum, Robert ^a   Smith, Matthew A ^b,c   Kohn, Adam ^d   Rubin, Jonathan E ^c,e   Doiron, Brent ^c,e  

^a UNIVERSITY OF NOTRE DAME   (United States)

^b UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE   (United States)

^c CENTER FOR THE NEURAL BASIS OF COGNITION   (United States)

^d ALBERT EINSTEIN COLLEGE OF MEDICINE   (United States)

^e UNIVERSITY OF PITTSBURGH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; COMPUTER SIMULATION; CONNECTOME; CORRELATIONAL STUDY; MACACA; MATHEMATICAL ANALYSIS; NERVE CELL; NERVE CELL NETWORK; NEURONAL VARIABILITY; NONHUMAN; PREDICTION; PRIORITY JOURNAL; SPATIAL ANALYSIS; STRIATE CORTEX; SYNAPTIC TRANSMISSION; ACTION POTENTIAL; ANIMAL; BIOLOGICAL MODEL; PHYSIOLOGY; VISUAL CORTEX;

ACTION POTENTIALS; ANIMALS; COMPUTER SIMULATION; MACACA; MODELS, NEUROLOGICAL; NERVE NET; NEURONS; VISUAL CORTEX;

EID: 84992699645     PISSN: 10976256     EISSN: 15461726     Source Type: Journal    
DOI: 10.1038/nn.4433     Document Type: Article

References (50)

1. Ermentrout, B. Neural networks as spatio-temporal pattern-forming systems. Rep. Prog. Phys. 61, 353 (1998).
2. Faisal, A.A., Selen, L.P. & Wolpert, D.M. Noise in the nervous system. Nat. Rev. Neurosci. 9, 292-303 (2008).
3. Pouget, A., Beck, J.M., Ma, W.J. & Latham, P.E. Probabilistic brains: knowns and unknowns. Nat. Neurosci. 16, 1170-1178 (2013).
4. Shamir, M. Emerging principles of population coding: in search for the neural code. Curr. Opin. Neurobiol. 25, 140-148 (2014).
5. 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).
6. Shadlen, M.N. & Newsome, W.T. Noise, neural codes and cortical organization. Curr. Opin. Neurobiol. 4, 569-579 (1994).
7. van Vreeswijk, C. & Sompolinsky, H. Chaos in neuronal networks with balanced excitatory and inhibitory activity. Science 274, 1724-1726 (1996).
8. Denève, S. & Machens, C.K. Efficient codes and balanced networks. Nat. Neurosci. 19, 375-382 (2016).
9. Lim, S. & Goldman, M.S. Balanced cortical microcircuitry for spatial working memory based on corrective feedback control. J. Neurosci. 34, 6790-6806 (2014).
10. Hansel, D. & van Vreeswijk, C. The mechanism of orientation selectivity in primary visual cortex without a functional map. J. Neurosci. 32, 4049-4064 (2012).
11. Shu, Y., Hasenstaub, A. & McCormick, D.A. Turning on and off recurrent balanced cortical activity. Nature 423, 288-293 (2003).
12. Haider, B., Duque, A., Hasenstaub, A.R. & McCormick, D.A. Neocortical network activity in vivo is generated through a dynamic balance of excitation and inhibition. J. Neurosci. 26, 4535-4545 (2006).
13. Okun, M. & Lampl, I. Instantaneous correlation of excitation and inhibition during ongoing and sensory-evoked activities. Nat. Neurosci. 11, 535-537 (2008).
14. Dorrn, A.L., Yuan, K., Barker, A.J., Schreiner, C.E. & Froemke, R.C. Developmental sensory experience balances cortical excitation and inhibition. Nature 465, 932-936 (2010).
15. Graupner, M. & Reyes, A.D. Synaptic input correlations leading to membrane potential decorrelation of spontaneous activity in cortex. J. Neurosci. 33, 15075-15085 (2013).
16. Zhou, M. et al. Scaling down of balanced excitation and inhibition by active behavioral states in auditory cortex. Nat. Neurosci. 17, 841-850 (2014).
17. Xue, M., Atallah, B.V. & Scanziani, M. Equalizing excitation-inhibition ratios across visual cortical neurons. Nature 511, 596-600 (2014).
18. Amit, D.J. & Brunel, N. Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex. Cereb. Cortex 7, 237-252 (1997).
19. Ko, H. et al. Functional specificity of local synaptic connections in neocortical networks. Nature 473, 87-91 (2011).
20. Fino, E. & Yuste, R. Dense inhibitory connectivity in neocortex. Neuron 69, 1188-1203 (2011).
21. Levy, R.B. & Reyes, A.D. Spatial profile of excitatory and inhibitory synaptic connectivity in mouse primary auditory cortex. J. Neurosci. 32, 5609-5619 (2012).
22. Oswald, A.M., Doiron, B., Rinzel, J. & Reyes, A.D. Spatial profile and differential recruitment of GABAB modulate oscillatory activity in auditory cortex. J. Neurosci. 29, 10321-10334 (2009).
23. Renart, A. et al. The asynchronous state in cortical circuits. Science 327, 587-590 (2010).
24. Ecker, A.S. et al. Decorrelated neuronal firing in cortical microcircuits. Science 327, 584-587 (2010).
25. Cohen, M.R. & Kohn, A. Measuring and interpreting neuronal correlations. Nat. Neurosci. 14, 811-819 (2011).
26. Doiron, B., Litwin-Kumar, R., Rosenbaum, R., Ocker, G.K. & Josić, K. The mechanics of state-dependent neural correlations. Nat. Neurosci. 19, 383-393 (2016).
27. Kohn, A., Zandvakili, A. & Smith, M.A. Correlations and brain states: from electrophysiology to functional imaging. Curr. Opin. Neurobiol. 19, 434-438 (2009).
28. Poulet, J.F. & Petersen, C.C. Internal brain state regulates membrane potential synchrony in barrel cortex of behaving mice. Nature 454, 881-885 (2008).
29. Cohen, M.R. & Maunsell, J.H. Attention improves performance primarily by reducing interneuronal correlations. Nat. Neurosci. 12, 1594-1600 (2009).
30. Mochol, G., Hermoso-Mendizabal, A., Sakata, S., Harris, K.D. & de la Rocha, J. Stochastic transitions into silence cause noise correlations in cortical circuits. Proc. Natl. Acad. Sci. USA 112, 3529-3534 (2015).
31. Ecker, A.S. et al. State dependence of noise correlations in macaque primary visual cortex. Neuron 82, 235-248 (2014).
32. Hansen, B.J., Chelaru, M.I. & Dragoi, V. Correlated variability in laminar cortical circuits. Neuron 76, 590-602 (2012).
33. Smith, M.A., Jia, X., Zandvakili, A. & Kohn, A. Laminar dependence of neuronal correlations in visual cortex. J. Neurophysiol. 109, 940-947 (2013).
34. Lund, J.S., Angelucci, A. & Bressloff, P.C. Anatomical substrates for functional columns in macaque monkey primary visual cortex. Cereb. Cortex 13, 15-24 (2003).
35. Wimmer, K. et al. The dynamics of sensory integration in a hierarchical network explains choice probabilities in MT. Nat. Commun. 6, 6177 (2015).
36. Smith, M.A. & Kohn, A. Spatial and temporal scales of neuronal correlation in primary visual cortex. J. Neurosci. 28, 12591-12603 (2008).
37. Rosenbaum, R. & Doiron, B. Balanced networks of spiking neurons with spatially dependent recurrent connections. Phys. Rev. X 4, 021039 (2014).
38. Yu, B., Kohn, A. & Smith, M.A. Estimating shared firing rate fluctuations in neural populations. Soc. Neurosci. abstr. 483.18 (2011).
39. Lin, I.C., Okun, M., Carandini, M. & Harris, K.D. The nature of shared cortical variability. Neuron 87, 644-656 (2015).
40. Schölvinck, M.L., Saleem, A.B., Benucci, A., Harris, K.D. & Carandini, M. Cortical state determines global variability and correlations in visual cortex. J. Neurosci. 35, 170-178 (2015).
41. Yu, B.M. et al. Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity. J. Neurophysiol. 102, 614-635 (2009).
42. Litwin-Kumar, A. & Doiron, B. Slow dynamics and high variability in balanced cortical networks with clustered connections. Nat. Neurosci. 15, 1498-1505 (2012).
43. Chelaru, M.I. & Dragoi, V. Negative correlations in visual cortical networks. Cereb. Cortex 26, 246-256 (2016).
44. Bosking, W.H., Zhang, Y., Schofield, B. & Fitzpatrick, D. Orientation selectivity and the arrangement of horizontal connections in tree shrew striate cortex. J. Neurosci. 17, 2112-2127 (1997).
45. Pfeffer, C.K., Xue, M., He, M., Huang, Z.J. & Scanziani, M. Inhibition of inhibition in visual cortex: the logic of connections between molecularly distinct interneurons. Nat. Neurosci. 16, 1068-1076 (2013).
46. Kriener, B., Helias, M., Rotter, S., Diesmann, M. & Einevoll, G.T. How pattern formation in ring networks of excitatory and inhibitory spiking neurons depends on the input current regime. Front. Comput. Neurosci. 7, 187 (2014).
47. Rikhye, R.V. & Sur, M. Spatial correlations in natural scenes modulate response reliability in mouse visual cortex. J. Neurosci. 35, 14661-14680 (2015).
48. Tan, A.Y., Chen, Y., Scholl, B., Seidemann, E. & Priebe, N.J. Sensory stimulation shifts visual cortex from synchronous to asynchronous states. Nature 509, 226-229 (2014).
49. Kelly, R.C. et al. Comparison of recordings from microelectrode arrays and single electrodes in the visual cortex. J. Neurosci. 27, 261-264 (2007).
50. De Valois, R.L., Albrecht, D.G. & Thorell, L.G. Spatial frequency selectivity of cells in macaque visual cortex. Vision Res. 22, 545-559 (1982).