Spike avalanches in vivo suggest a driven, slightly subcritical brain state

Frontiers in Systems Neuroscience

Volumn 8, Issue JUNE, 2014, Pages

  Priesemann, Viola ^a,b,c,d   Wibral, Michael ^e,f   Valderrama, Mario ^g   Pröpper, Robert ^h,i   Le Van Quyen, Michel ^j   Geisel, Theo ^a,b   Triesch, Jochen ^c   Nikolić, Danko ^c,d,f,k   Munk, Matthias H J ^l  

^a MAX PLANCK INSTITUTE FOR DYNAMICS AND SELF ORGANIZATION   (Germany)

^b Bernstein Center for Computational Neuroscience ^*  (Germany)

^c FRANKFURT INSTITUTE FOR ADVANCED STUDIES   (Germany)

^d MAX PLANCK INSTITUTE FOR BRAIN RESEARCH   (Germany)

^e GOETHE UNIVERSITY   (Germany)

^f ERNST STRÜNGMANN INSTITUTE ESI FOR NEUROSCIENCE IN COOPERATION WITH MAX PLANCK SOCIETY   (Germany)

^g UNIVERSIDAD DE LOS ANDES   (Colombia)

^h TECHNISCHE UNIVERSITÄT BERLIN   (Germany)

ⁱ BERNSTEIN CENTER FOR COMPUTATIONAL NEUROSCIENCE   (Germany)

^j SORBONNE UNIVERSITÉ   (France)

^k UNIVERSITY OF ZAGREB   (Croatia)

^l MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

more..

Author keywords

Cortex; Highly parallel recordings; Human intracranial recordings; Monkeys; Multiunit activity; Self organized criticality; Spike train analysis; Spiking neural networks

Indexed keywords

ARTICLE; BRAIN DEPTH STIMULATION; BRAIN ELECTROPHYSIOLOGY; BRAIN FUNCTION; BRAIN NERVE CELL; BRANCHING MORPHOGENESIS; CORTICAL NEURONAL AVALANCHE DYNAMICS; CORTICAL SYNCHRONIZATION; HUMAN; NERVE CELL NETWORK; NERVE CONDUCTION; NONHUMAN; PREFRONTAL CORTEX; PREMOTOR CORTEX; SAMPLING; SPATIAL ANALYSIS; SPATIOTEMPORAL ANALYSIS; SPIKE WAVE; STATISTICAL DISTRIBUTION; SUBCRITICAL BRAIN STATE; VISUAL CORTEX;

EID: 84907297750     PISSN: 16625137     EISSN: None     Source Type: Journal    
DOI: 10.3389/fnsys.2014.00108     Document Type: Article

References (90)

1. Baiesi, M., and Paczuski, M. (2004). Scale-free networks of earthquakes and aftershocks. Phys. Rev. E 69:066106. doi: 10.1103/PhysRevE.69.066106
2. Bak, P., Tang, C., and Wiesenfeld, K. (1987). Self-organized criticality: an explanation of 1/f noise. Phys. Rev. Lett. 59, 381-384. doi: 10.1103/PhysRevLett.59.381
3. Bak, P., Tang, C., and Wiesenfeld, K. (1988). Self-organized criticality. Phys. Rev. A 38, 364-374. doi: 10.1103/PhysRevA.38.364
4. Bedard, C., Kroeger, H., and Destexhe, A. (2006). Does the 1/f frequency scaling of brain signals reflect self-organized critical states? Phys. Rev. Lett. 97:118102. doi: 10.1103/PhysRevLett.97.118102
5. Beggs, J. M. (2007). Neuronal avalanche. Scholarpedia 2:1344. doi: 10.4249/scholarpedia.1344
6. Beggs, J. M. (2008). The criticality hypothesis: how local cortical networks might optimize information processing. Philos. Trans. R. Soc. Math. Phys. Eng. Sci. 366, 329-343. doi: 10.1098/rsta.2007.2092
7. Beggs, J. M., and Plenz, D. (2003). Neuronal avalanches in neocortical circuits. J. Neurosci. 23, 11167-11177.
8. Beggs, J. M., and Plenz, D. (2004). Neuronal avalanches are diverse and precise activity patterns that are stable for many hours in cortical slice cultures. J. Neurosci. 24, 5216-5229. doi: 10.1523/JNEUROSCI.0540-04.2004
9. Beggs, J. M., and Timme, N. (2012). Being critical of criticality in the brain. Front. Physiol. 3:163. doi: 10.3389/fphys.2012.00163
10. Benayoun, M., Cowan, J. D., van Drongelen, W., and Wallace, E. (2010). Avalanches in a stochastic model of spiking neurons. PLoS Comput. Biol. 6:e1000846. doi: 10.1371/journal.pcbi.1000846
11. Bertschinger, N., and Natschläger, T. (2004). Real-time computation at the edge of chaos in recurrent neural networks. Neural Comput. 16, 1413-1436. doi: 10.1162/089976604323057443
12. Blanche, T. (2009). Multi-Neuron Recordings in Primary Visual Cortex. Available online at: CRCNS.org
13. Blanche, T. J., and Swindale, N. V. (2006). Nyquist interpolation improves neuron yield in multiunit recordings. J. Neurosci. Methods 155, 81-91. doi: 10.1016/j.jneumeth.2005.12.031
14. Boedecker, J., Obst, O., Lizier, J. T., Mayer, N. M., and Asada, M. (2012). Information processing in echo state networks at the edge of chaos. Theory Biosci. 131, 205-213. doi: 10.1007/s12064-011-0146-8
15. Bonachela, J. A., de Franciscis, S., Torres, J. J., and Munoz, M. A. (2010). Self-organization without conservation: are neuronal avalanches generically critical? J. Stat. Mech. Theory Exp. 2010:P02015. doi: 10.1088/17425468/2010/02/P02015
16. Bonachela, J. A., and Muñoz, M. A. (2009). Self-organization without conservation: true or just apparent scale-invariance? J. Stat. Mech. Theory Exp. 2009: P09009. doi: 10.1088/1742-5468/2010/02/P02015
17. Clar, S., Drossel, B., Schenk, K., and Schwabl, F. (1999). Self-organized criticality in forest-fire models. Phys. Stat. Mech. Appl. 266, 153-159. doi: 10.1016/S03784371(98)00587-1
18. Clar, S., Drossel, B., and Schwabl, F. (1996). Forest fires and other examples of self-organized criticality. J. Phys. Condens. Matter 8:6803. doi: 10.1088/09538984/8/37/004
19. Clauset, A., Young, M., and Gleditsch, K. S. (2007). On the frequency of severe terrorist events. J. Conflict Resolut. 51, 58-87. doi: 10.1177/0022002706296157
20. De Menech, M., Stella, A. L., and Tebaldi, C. (1998). Rare events and breakdown of simple scaling in the Abelian sandpile model. Phys. Rev. E 58, R2677-R2680. doi: 10.1103/PhysRevE.58.R2677
21. Dhar, D. (2006). Theoretical studies of self-organized criticality. Phys. Stat. Mech. Appl. 369, 29-70. doi: 10.1016/j.physa.2006.04.004
22. Dickman, R., Muñoz, M. A., Vespignani, A., and Zapperi, S. (2000). Paths to self-organized criticality. Braz. J. Phys. 30, 27-41. doi: 10.1590/S010397332000000100004
23. Drossel, B., and Schwabl, F. (1992). Self-organized criticality in a forest-fire model. Phys. Stat. Mech. Appl. 191, 47-50. doi: 10.1016/0378-4371(92)90504-J
24. Dunkelmann, S., and Radons, G. (1994). Neural Networsk and Abelian Sandpile Models of Self-Organized Criticality. Berlin; Heidelberg: Springer.
25. Eurich, C. W., Herrmann, J. M., and Ernst, U. A. (2002). Finite-size effects of avalanche dynamics. Phys. Rev. E 66:066137. doi: 10.1103/PhysRevE.66.066137
26. Field, D. J. (1987). Relations between the statistics of natural images and the response properties of cortical cells. J. Opt. Soc. Am. A 4, 2379-2394. doi: 10.1364/JOSAA.4.002379
27. Franke, F. (2011). Real-Time Analysis of Extracellular Multielectrode Recordings. Berlin: Universitätsbibliothek.
28. Franke, F., Natora, M., Boucsein, C., Munk, M. H., and Obermayer, K. (2010). An online spike detection and spike classification algorithm capable of instantaneous resolution of overlapping spikes. J. Comput. Neurosci. 29, 127-148. doi: 10.1007/s10827-009-0163-5
29. Fredj, N. B., and Burrone, J. (2009). A resting pool of vesicles is responsible for spontaneous vesicle fusion at the synapse. Nat. Neurosci. 12, 751-758. doi: 10.1038/nn.2317
30. Frette, V., Christensen, K., Malthe-Sørenssen, A., Feder, J., Jøssang, T., and Meakin, P. (1996). Avalanche dynamics in a pile of rice. Nature 379, 49-52. doi: 10.1038/379049a0
31. Friedman, N., Ito, S., Brinkman, B. A., Shimono, M., DeVille, R. L., Dahmen, K. A., et al. (2012). Universal critical dynamics in high resolution neuronal avalanche data. Phys. Rev. Lett. 108:208102. doi: 10.1103/PhysRevLett.108.208102
32. Frigg, R. (2003). Self-organised criticality-what it is and what it isn't. Stud. Hist. Philos. Sci. Part A 34, 613-632. doi: 10.1016/S0039-3681(03) 00046-3
33. Gal, A., and Marom, S. (2013). Self-organized criticality in single-neuron excitability. Phys. Rev. E 88:062717. doi: 10.1103/PhysRevE.88.062717
34. Girardi-Schappo, M., Kinouchi, O., and Tragtenberg, M. H. R. (2013). Critical avalanches and subsampling in map-based neural networks coupled with noisy synapses. Phys. Rev. E 88:024701. doi: 10.1103/PhysRevE.88.024701
35. Gutenberg, B., and Richter, C. F. (1944). Frequency of earthquakes in California. Bull. Seismol. Soc. Am. 34, 185-188.
36. Hahn, G., Petermann, T., Havenith, M. N., Yu, S., Singer, W., Plenz, D., et al. (2010). Neuronal avalanches in spontaneous activity in vivo. J. Neurophysiol. 104, 3312-3322. doi: 10.1152/jn.00953.2009
37. Haldeman, C., and Beggs, J. M. (2005). Critical branching captures activity in living neural networks and maximizes the number of metastable states. Phys. Rev. Lett. 94:058101. doi: 10.1103/PhysRevLett.94.058101
38. Harris, T. E. (1963). The Theory of Branching Processes. Berlin: Springer Verlag, 232.
39. Hartley, C., Taylor, T. J., Kiss, I. Z., Farmer, S. F., and Berthouze, L. (2013). Identification of criticality in neuronal avalanches: II. A theoretical and empirical investigation of the driven case. J. Math. Neurosci. 4:9. doi: 10.1186/21908567-4-9
40. Hsu, D., Chen, W., Hsu, M., and Beggs, J. M. (2008). An open hypothesis: is epilepsy learned, and can it be unlearned? Epilepsy Behav. 13, 511-522. doi: 10.1016/j.yebeh.2008.05.007
41. Jensen, H. J. (1998). Self-Organized Criticality: Emergent Complex Behavior in Physical and Biological Systems. Cambridge: Cambridge University Press.
42. Kadanoff, L. P., Nagel, S. R., Wu, L., and Zhou, S. (1989). Scaling and universality in avalanches. Phys. Rev. A 39:6524. doi: 10.1103/PhysRevA.39.6524
43. Kantelhardt, J. W., Zschiegner, S. A., Koscielny-Bunde, E., Havlin, S., Bunde, A., and Stanley, H. E. (2002). Multifractal detrended fluctuation analysis of nonstationary time series. Phys. Stat. Mech. Appl. 316, 87-114. doi: 10.1016/S03784371(02)01383-3
44. Kinouchi, O., and Copelli, M. (2006). Optimal dynamical range of excitable networks at criticality. Nat. Phys. 2, 348-351. doi: 10.1038/nphys289
45. Klaus, A., Yu, S., and Plenz, D. (2011). Statistical analyses support power law distributions found in neuronal avalanches. PLoS ONE 6:e19779. doi: 10.1371/journal.pone.0019779
46. Ktitarev, D. V., Lübeck, S., Grassberger, P., and Priezzhev, V. B. (2000). Scaling of waves in the Bak-Tang-Wiesenfeld sandpile model. Phys. Rev. E 61:81. doi: 10.1103/PhysRevE.61.81
47. Levina, A., Ernst, U., and Michael Herrmann, J. (2007b). Criticality of avalanche dynamics in adaptive recurrent networks. Neurocomputing 70, 1877-1881. doi: 10.1016/j.neucom.2006.10.056
48. Levina, A., Herrmann, J. M., and Geisel, T. (2007a). Dynamical synapses causing self-organized criticality in neural networks. Nat. Phys. 3, 857-860. doi: 10.1038/nphys758
49. Levina, A., Herrmann, J. M., and Geisel, T. (2009). Phase transitions towards criticality in a neural system with adaptive interactions. Phys. Rev. Lett. 102:118110. doi: 10.1103/PhysRevLett.102.118110
50. Linkenkaer-Hansen, K., Nikouline, V. V., Palva, J. M., and Ilmoniemi, R. J. (2001). Long-range temporal correlations and scaling behavior in human brain oscillations. J. Neurosci. 21, 1370-1377.
51. Lizier, J. T. (2013). "Computation in complex systems," in The Local Information Dynamics of Distributed Computation in Complex Systems (Springer), 13-52. Available online at: http://link.springer.com/chapter/10.1007/978-3-642-32952-4_2
52. Longtin, A. (2013). Neuronal noise. Scholarpedia 8:1618. doi: 10.4249/scholarpedia.1618
53. Mazzoni, A., Broccard, F. D., Garcia-Perez, E., Bonifazi, P., Ruaro, M. E., and Torre, V. (2007). On the dynamics of the spontaneous activity in neuronal networks. PLoS ONE 2:e439. doi: 10.1371/journal.pone.0000439
54. Meisel, C., Storch, A., Hallmeyer-Elgner, S., Bullmore, E., and Gross, T. (2012). Failure of adaptive self-organized criticality during epileptic seizure attacks. PLoS Comput. Biol. 8:e1002312. doi: 10.1371/journal.pcbi.1002312
55. Mizuseki, K., Sirota, A., Pastalkova, E., and Buzsáki, G. (2009). Multi-Unit Recordings from the Rat Hippocampus Made During Open Field Foraging. Available online at: CRCNS.org
56. Nagler, J., Hauert, C., and Schuster, H. G. (1999). Self-organized criticality in a nutshell. Phys. Rev. E 60:2706. doi: 10.1103/PhysRevE.60.2706
57. Olami, Z., Feder, H. J. S., and Christensen, K. (1992). Self-organized criticality in a continuous, nonconservative cellular automaton modeling earthquakes. Phys. Rev. Lett. 68:1244. doi: 10.1103/PhysRevLett.68.1244
58. Papanikolaou, S., Bohn, F., Sommer, R. L., Durin, G., Zapperi, S., and Sethna, J. P. (2011). Universality beyond power laws and the average avalanche shape. Nat. Phys. 7, 316-320. doi: 10.1038/nphys1884
59. Pasquale, V., Massobrio, P., Bologna, L. L., Chiappalone, M., and Martinoia, S. (2008). Self-organization and neuronal avalanches in networks of dissociated cortical neurons. Neuroscience 153, 1354-1369. doi: 10.1016/j.neuroscience.2008.03.050
60. Peng, C. K., Buldyrev, S. V., Havlin, S., Simons, M., Stanley, H. E., and Goldberger, A. L. (1994). Mosaic organization of DNA nucleotides. Phys. Rev. E 49, 1685-1689. doi: 10.1103/PhysRevE.49.1685
61. Peng, C.-K., Havlin, S., Stanley, H. E., and Goldberger, A. L. (1995). Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos 5, 82-87. doi: 10.1063/1.166141
62. Petermann, T., Thiagarajan, T. C., Lebedev, M. A., Nicolelis, M. A., Chialvo, D. R., and Plenz, D. (2009). Spontaneous cortical activity in awake monkeys composed of neuronal avalanches. Proc. Natl. Acad. Sci. U.S.A. 106, 15921-15926. doi: 10.1073/pnas.0904089106
63. Pipa, G., Städtler, E. S., Rodriguez, E. F., Waltz, J. A., Muckli, L. F., Singer, W., et al. (2009). Performance-and stimulus-dependent oscillations in monkey prefrontal cortex during short-term memory. Front. Integr. Neurosci. 3:25. doi: 10.3389/neuro.07.025.2009
64. Plenz, D., and Thiagarajan, T. C. (2007). The organizing principles of neuronal avalanches: cell assemblies in the cortex? Trends Neurosci. 30, 101-110. doi: 10.1016/j.tins.2007.01.005
65. Poil, S.-S., Hardstone, R., Mansvelder, H. D., and Linkenkaer-Hansen, K. (2012). Critical-state dynamics of avalanches and oscillations jointly emerge from balanced excitation/inhibition in neuronal networks. J. Neurosci. 32, 9817-9823. doi: 10.1523/JNEUROSCI.5990-11.2012
66. Priesemann, V., Munk, M. H., and Wibral, M. (2009). Subsampling effects in neuronal avalanche distributions recorded in vivo. BMC Neurosci. 10:40. doi: 10.1186/1471-2202-10-40
67. Priesemann, V., Valderrama, M., Wibral, M., and Le Van Quyen, M. (2013). Neuronal avalanches differ from wakefulness to deep sleep-evidence from intracranial depth recordings in humans. PLoS Comput. Biol. 9:e1002985. doi: 10.1371/journal.pcbi.1002985
68. Pröpper, R., and Obermayer, K. (2013). Spyke viewer: a flexible and extensible platform for electrophysiological data analysis. Front. Neuroinformatics 7:26. doi: 10.3389/fninf.2013.00026
69. Pruessner, G. (2012). Self-Organised Criticality: Theory, Models and Characterisation. Cambridge: Cambridge University Press.
70. Ribeiro, T. L., Copelli, M., Caixeta, F., Belchior, H., Chialvo, D. R., Nicolelis, M. A., et al. (2010). Spike avalanches exhibit universal dynamics across the sleep-wake cycle. PLoS ONE 5:e14129. doi: 10.1371/journal.pone.0014129
71. Ribeiro, T. L., Ribeiro, S., Belchior, H., Caixeta, F., and Copelli, M. (2014). Undersampled critical branching processes on small-world and random networks fail to reproduce the statistics of spike avalanches. PLoS ONE 9:e94992. doi: 10.1371/journal.pone.0094992
72. Scarpetta, S., and de Candia, A. (2013). Neural avalanches at the critical point between replay and non-replay of spatiotemporal patterns. PLoS ONE 8:e64162. doi: 10.1371/journal.pone.0064162
73. Selverston, A. (2008). Stomatogastric ganglion. Scholarpedia 3:1661. doi: 10.4249/scholarpedia.1661
74. Sethna, J. P., Dahmen, K. A., and Myers, C. R. (2001). Crackling noise. Nature 410, 242-250. doi: 10.1038/35065675
75. Shew, W. L., and Plenz, D. (2013). The functional benefits of criticality in the cortex. Neuroscientist 19, 88-100. doi: 10.1177/1073858412445487
76. Shew, W. L., Yang, H., Petermann, T., Roy, R., and Plenz, D. (2009). Neuronal avalanches imply maximum dynamic range in cortical networks at criticality. J. Neurosci. 29, 15595-15600. doi: 10.1523/JNEUROSCI.3864-09.2009
77. Shriki, O., Alstott, J., Carver, F., Holroyd, T., Henson, R. N., Smith, M. L., et al. (2013). Neuronal Avalanches in the resting MEG of the human brain. J. Neurosci. 33, 7079-7090. doi: 10.1523/JNEUROSCI.4286-12.2013
78. Simoncelli, E. P., and Olshausen, B. A. (2001). Natural image statistics and neural representation. Annu. Rev. Neurosci. 24, 1193-1216. doi: 10.1146/annurev.neuro.24.1.1193
79. Sinz, F. H., Simoncelli, E. P., and Bethge, M. (2009). "Hierarchical modeling of local image features through L_p-nested symmetric distributions," in NIPS (Vancouver), 1696-1704.
80. Spasojević, D., Bukviæ, S., Miloševiæ, S., and Stanley, H. E. (1996). Barkhausen noise: elementary signals, power laws, and scaling relations. Phys. Rev. E 54, 2531-2546. doi: 10.1103/PhysRevE.54.2531
81. Stanley, H. E. (1971). Introduction to Phase Transitions and Critical Phenomena. Oxford: Oxford University Press, 348.
82. Stanley, H. E. (1999). Scaling, universality, and renormalization: three pillars of modern critical phenomena. Rev. Mod. Phys. 71:S358. doi: 10.1103/RevModPhys.71.S358
83. Stewart, C. V., and Plenz, D. (2008). Homeostasis of neuronal avalanches during postnatal cortex development in vitro. J. Neurosci. Methods 169, 405-416. doi: 10.1016/j.jneumeth.2007.10.021
84. Tagliazucchi, E., Balenzuela, P., Fraiman, D., and Chialvo, D. R. (2012). Criticality in large-scale brain FMRI dynamics unveiled by a novel point process analysis. Front. Physiol. 3:15. doi: 10.3389/fphys.2012.00015
85. Tetzlaff, C., Okujeni, S., Egert, U., Wörgötter, F., and Butz, M. (2010). Selforganized criticality in developing neuronal networks. PLoS Comput. Biol. 6:e1001013. doi: 10.1371/journal.pcbi.1001013
86. Van der Schaaf, A., and van Hateren, J. (1996). Modelling the power spectra of natural images: statistics and information. Vis. Res. 36, 2759-2770. doi: 10.1016/0042-6989(96)00002-8
87. Vespignani, A., Dickman, R., Muñoz, M. A., and Zapperi, S. (1998). Driving, conservation, and absorbing states in sandpiles. Phys. Rev. Lett. 81:5676. doi: 10.1103/PhysRevLett.81.5676
88. Vespignani, A., and Zapperi, S. (1997). Order parameter and scaling fields in self-organized criticality. Phys. Rev. Lett. 78, 4793-4796. doi: 10.1103/PhysRevLett.78.4793
89. Vespignani, A., and Zapperi, S. (1998). How self-organized criticality works: a unified mean-field picture. Phys. Rev. E 57, 6345-6362. doi: 10.1103/PhysRevE.57.6345
90. Wilson, K. G. (1975). The renormalization group: critical phenomena and the Kondo problem. Rev. Mod. Phys. 47:773. doi: 10.1103/RevModPhys.47.773