The functional benefits of criticality in the cortex

Neuroscientist

Volumn 19, Issue 1, 2013, Pages 88-100

  Shew, Woodrow L ^a   Plenz, Dietmar ^b  

^a UNIVERSITY OF ARKANSAS   (United States)

^b NATIONAL INSTITUTE OF MENTAL HEALTH   (United States)

Author keywords

cortex; criticality; information processing; information theory; network dynamics; neuronal avalanches

Indexed keywords

BRAIN CORTEX; BRAIN CORTEX CRITICALITY; BRAIN FUNCTION; COMMUNICATION THEORY; HUMAN; IN VITRO STUDY; IN VIVO STUDY; INFORMATION CAPACITY; MATHEMATICAL MODEL; NERVE CELL NETWORK; NERVE CONDUCTION; NERVOUS SYSTEM FUNCTION; NERVOUS SYSTEM PARAMETERS; PRIORITY JOURNAL; PROCESS OPTIMIZATION; REVIEW;

ANIMALS; CEREBRAL CORTEX; HUMANS; INFORMATION THEORY; MENTAL PROCESSES; MODELS, NEUROLOGICAL; NEURONS; NONLINEAR DYNAMICS;

EID: 84871566364     PISSN: 10738584     EISSN: 10894098     Source Type: Journal    
DOI: 10.1177/1073858412445487     Document Type: Review

References (99)

1. Alkire MT, Hudetz AG, Tononi G. Consciousness and anesthesia. Science. 2008 ; 322: 876-80
2. Alvarez-Dolado M, Calcagnotto ME, Karkar KM, Southwell DG, Jones-Davis DM, Estrada RC. Cortical inhibition modified by embryonic neural precursors grafted into the postnatal brain. J Neurosci. 2006 ; 26: 7380-9
3. Diagnostic and statistical manual of mental disorders. 2000. Diagnostic and statistical manual of mental disorders. 4th ed.Washington (DC): American Psychiatric Association. Washington (DC): American Psychiatric Association ; 2000 :
4. Arnold DV. Information-theoretic analysis of phase transitions. Complex Syst. 1996 ; 10: 143-55
5. Bacci A, Huguenard JR, Prince DA. Long-lasting self-inhibition of neocortical interneurons mediated by endocannabinoids. Nature. 2004 ; 431: 312-6
6. Beggs JM. The criticality hypothesis: how local cortical networks might optimize information processing. Philos Transact A Math Phys Eng Sci. 2008 ; 366: 329-43
7. Beggs JM, Plenz D. Neuronal avalanches in neocortical circuits. J Neurosci. 2003 ; 23: 11167-77
8. Beggs JM, Plenz D. Neuronal avalanches are diverse and precise activity patterns that are stable for many hours in cortical slice cultures. J Neurosci. 2004 ; 24: 5216-29
9. Bertschinger N, Natschläger T. Real-time computation at the edge of chaos in recurrent neural networks. Neural Comput. 2004 ; 16: 1413-36
10. Bienenstock E, Lehmann D. Regulated criticality in the brain?. Adv Complex Syst. 1998 ; 1: 361-84
11. Binney J, Dowrick N, Fisher A, Newman M The theory of critical phenomena. New York: Oxford University Press ; 1992 :
12. Buckley C, Nowotny T. Multiscale model of an inhibitory network shows optimal properties near bifurcation. Phys Rev Lett. 2011 ; 106: 238109
13. Buice MA, Cowan JD. Statistical mechanics of the neocortex. Prog Biophys Mol Biol. 2009 ; 99: 53-86
14. Chao H-T, Chen H, Samaco RC, Xue M, Chahrour M, Yoo J. Dysfunction in GABA signalling mediates autism-like stereotypies and Rett syndrome phenotypes. Nature. 2010 ; 468: 263-9
15. Chen D, Wu S, Guo A, Yang Z. Self-organized criticality in a cellular automaton model of pulse-coupled integrate-and-fire neurons. J Phys A Math Gen. 1995 ; 28: 5177-82
16. Chen W, Hobbs JP, Tang A, Beggs JM. A few strong connections: optimizing information retention in neuronal avalanches. BMC Neurosci. 2010 ; 11: 3
17. Chen X, Dzakpasu R. Observed network dynamics from altering the balance between excitatory and inhibitory neurons in cultured networks. Phys Rev E. 2010 ; 82: 1-8
18. Chialvo DR, Bak P. Learning from mistakes. Neurosci. 1999 ; 90: 1137-48
19. Cragg BG, Temperley HN. The organisation of neurones: a co-operative analogy. Electroencephalogr Clin Neurophysiol. 1954 ; 6: 85-92
20. Cragg BG, Temperley HN. Memory: the analogy with ferromagnetic hysteresis. Brain. 1955 ; 78: 304-16
21. Crutchfield JP, Young K. Inferring statistical complexity. Phys Rev Lett. 1989 ; 63: 105-8
22. Crutchfield JP, Young K Entropy, complexity, and physics of information. Zurek W, ed. Reading (MA): Addison-Wesley ; 1990: 223-69.
23. Dan Y, Atick JJ, Reid RC. Efficient coding of natural scenes in the lateral geniculate nucleus: experimental test of a computational theory. J Neurosci. 1996 ; 16: 3351-62
24. Dong D, Atick J. Temporal decorrelation: a theory of lagged and nonlagged responses in the lateral geniculate nucleus. Network Comput Neural Syst. 1995 ; 6: 159-78
25. Fatemi SH, Folsom TD, Reutiman TJ, Thuras PD. Expression of GABA(B) receptors is altered in brains of subjects with autism. Cerebellum. 2009 ; 8: 64-9
26. Fekete T, Pitowsky I, Grinvald A, Omer DB. Arousal increases the representational capacity of cortical tissue. J Comput Neurosci. 2009 ; 27: 211-27
27. Felleman DJ, Van Essen DC. Distributed hierarchical processing in the primate cerebral cortex. Cereb Cortex. 1991 ; 1: 1-47
28. Fraiman D, Balenzuela P, Foss J, Chialvo D. Ising-like dynamics in large-scale functional brain networks. Phys Rev E. 2009 ; 79: 1-10
29. Gireesh E, Plenz D. Neuronal avalanches organize as nested theta- and beta/gamma-oscillations during development of cortical layer 2/3. Proc Natl Acad Sci U S A. 2008 ; 105: 7576-81
30. Gogolla N, Leblanc JJ, Quast KB, Südhof T, Fagiolini M, Hensch TK. Common circuit defect of excitatory-inhibitory balance in mouse models of autism. J Neurodev Disord. 2009 ; 1: 172-81
31. Gomot M, Belmonte MK, Bullmore ET, Bernard FA, Baron-Cohen S. Brain hyper-reactivity to auditory novel targets in children with high-functioning autism. Brain. 2008 ; 131: 2479-88
32. Greenfield E, Lecar H. Mutual information in a dilute, asymmetric neural network model. Phys Rev E. 2001 ; 63: 1-10
33. Hahn G, Petermann T, Havenith MN, Yu S, Singer W, Plenz D. Neuronal avalanches in spontaneous activity in vivo. J Neurophysiol. 2010 ; 104: 3312-22
34. Haider B, Duque A, Hasenstaub AR, Yu Y, McCormick DA. Enhancement of visual responsiveness by spontaneous local network activity in vivo. J Neurophysiol. 2007 ; 97: 4186-202
35. Haider B, McCormick DA, Duque A, Hasenstaub AR. Neocortical network activity in vivo is generated through a dynamic balance of excitation and inhibition. J Neurosci. 2006 ; 26: 4535-45
36. Haldeman C, Beggs JM. Critical branching captures activity in living neural networks and maximizes the number of metastable states. Phys Rev Lett. 2005 ; 94: 058101
37. Herz A, Hopfield J. Earthquake cycles and neural reverberations: collective oscillations in systems with pulse-coupled threshold elements. Phys Rev Lett. 1995 ; 75: 1222-5
38. Hines RM, Wu L, Hines DJ, Steenland H, Mansour S, Dahlhaus R. Synaptic imbalance, stereotypies, and impaired social interactions in mice with altered neuroligin 2 expression. J Neurosci. 2008 ; 28: 6055-67
39. Hinrichsen H. Non-equilibrium phase transitions. Physica A. 2006 ; 369: 1-28
40. Hopkinson J Hopkinson B, ed. Cambridge (UK): Cambridge University Press ; 1901 :
41. Huberman BA. The performance of cooperative processes. Physica D. 1990 ; 42: 38-47
42. Kaiser M, Hilgetag CC. Optimal hierarchical modular topologies for producing limited sustained activation of neural networks. Front Neuroinform. 2010 ; 4: 8
43. Kauffman S. Requirements for evolvability in complex systems: orderly dynamics and frozen components. Physica D. 1990 ; 42: 135-52
44. Kello CT, Brown GDA, Ferrer-I-Cancho R, Holden JG, Linkenkaer-Hansen K, Rhodes T. Scaling laws in cognitive sciences. Trends Cogn Sci. 2010 ; 14: 223-32
45. Kentridge RW Eeckman FH Bower JM, ed. Boston: Kluwer ; 1993: 531-5.
46. Kentridge RW Perspectives in neural computing. Browne A, ed. Bristol: Institute of Physics Press ; 1997: 51-71.
47. Kinouchi O, Copelli M. Optimal dynamical range of excitable networks at criticality. Nat Phys. 2006 ; 2: 348-51
48. Kitzbichler MG, Smith ML, Christensen SR, Bullmore E. Broadband criticality of human brain network synchronization. PLoS Comput Biol. 2009 ; 5: e1000314
49. Kürten K. Critical phenomena in model neural networks. Phys Lett A. 1988 ; 129: 157-60
50. Langton CG. Computation at the edge of chaos: phase transitions and emergent computation. Physica D. 1990 ; 42: 12-37
51. Larremore DB, Shew WL, Ott E, Restrepo JG. Effects of network topology, transmission delays, and refractoriness on the response of coupled excitable systems to a stochastic stimulus. Chaos. 2011 ; 21: 025117
52. Larremore DB, Shew WL, Restrepo JG. Predicting criticality and dynamic range in complex networks: effects of topology. Phys Rev Lett. 2011 ; 106: 1-4
53. Laughlin S. A simple coding procedure enhances a neuron's information capacity. Z Naturforsc C. 1981 ; 36: 910-2
54. Legenstein R, Maass W. Edge of chaos and prediction of computational performance for neural circuit models. Neural Netw. 2007 ; 20: 323-34
55. Levina A, Herrmann JM, Geisel T. Dynamical synapses causing self-organized criticality in neural networks. Nat Phys. 2007 ; 3: 857-60
56. Li W, Packard NH, Langton Chris G. Transition phenomena in cellular automata rule space. Physica D. 1990 ; 45: 77-94
57. Lucas-Meunier E, Monier C, Amar M, Baux G, Frégnac Y, Fossier P. Involvement of nicotinic and muscarinic receptors in the endogenous cholinergic modulation of the balance between excitation and inhibition in the young rat visual cortex. Cereb Cortex. 2009 ; 19: 2411-27
58. Maffei A, Turrigiano G. Multiple modes of network homeostasis in visual cortical layer 2/3. J Neurosci. 2008 ; 28: 4377-84
59. Markram H, Rinaldi T, Markram K. The intense world syndrome-an alternative hypothesis for autism. Front Neurosci. 2007 ; 1: 77-96
60. Meisel C, Gross T. Adaptive self-organization in a realistic neural network model. Phys Rev E. 2009 ; 80: 1-6
61. Millman D, Mihalas S, Kirkwood A, Niebur E. Self-organized criticality occurs in non-conservative neuronal networks during "up" states. Nat Phys. 2010 ; 6: 801-5
62. Moreau AW, Amar M, Le Roux N, Morel N, Fossier P. Serotoninergic fine-tuning of the excitation-inhibition balance in rat visual cortical networks. Cereb Cortex. 2010 ; 20: 456-67
63. Muñoz M, Dickman R, Vespignani A, Zapperi S. Avalanche and spreading exponents in systems with absorbing states. Phys Rev E. 1999 ; 59: 6175-9
64. Oblak A, Gibbs TT, Blatt GJ. Decreased GABAA receptors and benzodiazepine binding sites in the anterior cingulate cortex in autism. Autism Res. 2009 ; 2: 205-19
65. Oblak AL, Gibbs TT, Blatt GJ. Decreased GABA(B) receptors in the cingulate cortex and fusiform gyrus in autism. J Neurochem. 2010 ; 114: 1414-23
66. Okun M, Lampl I. Instantaneous correlation of excitation and inhibition during ongoing and sensory-evoked activities. Nat Neurosci. 2008 ; 11: 535-7
67. Pasquale V, Massobrio P, Bologna LL, Chiappalone M, Martinoia S. Self-organization and neuronal avalanches in networks of dissociated cortical neurons. Neurosci. 2008 ; 153: 1354-69
68. Petermann T, Thiagarajan TC, Lebedev MA, Nicolelis MAL, Chialvo Dante R, Plenz D. Spontaneous cortical activity in awake monkeys composed of neuronal avalanches. Proc Natl Acad Sci U S A. 2009 ; 106: 15921-6
69. Poil S-S, van Ooyen A, Linkenkaer-Hansen K. Avalanche dynamics of human brain oscillations: relation to critical branching processes and temporal correlations. Hum Brain Mapp. 2008 ; 29: 770-7
70. Pouille F, Marin-Burgin A, Marin-Burgin A, Adesnik H, Atallah BV, Scanziani M. Input normalization by global feedforward inhibition expands cortical dynamic range. Nat Neurosci. 2009 ; 12: 1577-85
71. Ramo P, Kaufmann S, Kesseli J, Yliharja O. Measures for information propagation in Boolean networks. Physica D. 2007 ; 227: 100-4
72. Ribeiro TL, Copelli M, Caixeta F, Belchior H, Chialvo Dante R, Nicolelis MAL. Spike avalanches exhibit universal dynamics across the sleep-wake cycle. PLoS ONE. 2010 ; 5: e14129
73. Rieke F, Warland D, de Ruyter Van Steveninck R, Bialek W Spikes: exploring the neural code. Cambridge (MA): MIT Press ; 1997 :
74. Rubenstein J, Merzenich M. Model of autism: increased ratio of excitation/inhibition in key neural systems. Genes Brain Behav. 2003 ; 2: 255-67
75. Rubinov M, Sporns O, Thivierge J-P, Breakspear M. Neurobiologically realistic determinants of self-organized criticality in networks of spiking neurons. PLoS Comput Biol. 2011 ; 7: e1002038
76. Shannon CE Bell System Tech J. 1948: 379 623-423 56.
77. Shew WL, Yang H, Petermann T, Roy R, Plenz D. Neuronal avalanches imply maximum dynamic range in cortical networks at criticality. J Neurosci. 2009 ; 29: 15595-600
78. Shew WL, Yang H, Yu S, Roy Rajarshi Plenz D. Information capacity and transmission are maximized in balanced cortical networks with neuronal avalanches. J Neurosci. 2011 ; 31: 55-63
79. Shriki O Hebrew University of Jerusalem ; 2003 :
80. Shu Y, Hasenstaub A, McCormick DA. Turning on and off recurrent balanced cortical activity. Nature. 2003 ; 423: 288-93
81. Silverman JL, Tolu SS, Barkan CL, Crawley JN. Repetitive self-grooming behavior in the BTBR mouse model of autism is blocked by the mGluR5 antagonist MPEP. Neuropsychopharmacology. 2010 ; 35: 976-89
82. Sleigh JW, Steyn-Ross DA, Steyn-Ross ML, Grant C, Ludbrook G. Cortical entropy changes with general anaesthesia: theory and experiment. Physiol Meas. 2004 ; 25: 921-34
83. Spence SJ, Schneider MT. The role of epilepsy and epileptiform EEGs in autism spectrum disorders. Pediatr Res. 2009 ; 65: 599-606
84. Stassinopoulos D, Bak P. Democratic reinforcement: a principle for brain function. Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics. 1995 ; 51: 5033-9
85. Stewart CV, Plenz D. Inverted-U profile of dopamine-NMDA-mediated spontaneous avalanche recurrence in superficial layers of rat prefrontal cortex. J Neurosci. 2006 ; 26: 8148-59
86. Stewart CV, Plenz D. Homeostasis of neuronal avalanches during postnatal cortex development in vitro. J Neurosci Methods. 2008 ; 169: 405-16
87. Tanaka T, Kaneko T, Aoyagi T. Recurrent infomax generates cell assemblies, neuronal avalanches, and simple cell-like selectivity. Neural Comput. 2009 ; 21: 1038-67
88. Tannan V, Holden JK, Zhang Z, Baranek GT, Tommerdahl MA. Perceptual metrics of individuals with autism provide evidence for disinhibition. Autism Res. 2008 ; 1: 223-30
89. Terauchi A, Johnson-Venkatesh EM, Toth AB, Javed D, Sutton MA, Umemori H. Distinct FGFs promote differentiation of excitatory and inhibitory synapses. Nature. 2010 ; 465: 783-7
90. Tetzlaff C, Okujeni S, Egert U, Wörgötter F, Butz M. Self-organized criticality in developing neuronal networks. PLoS Comput Biol. 2010 ; 6: e1001013
91. Turrigiano G, Abbott LF, Marder E. Activity-dependent changes in the intrinsic properties of cultured neurons. Science. 1994 ; 264: 974-7
92. Usher M, Stemmler M, Koch C, Olami Z. Network amplification of local fluctuations causes high spike rate variability, fractal firing patterns and oscillatory local field potentials. Neural Comput. 1994 ; 6: 795-836
93. Verstraeten D, Schrauwen B, D'Haene M, Stroobandt D. An experimental unification of reservoir computing methods. Neural Netw. 2007 ; 20: 391-403
94. Wang S-J, Zhou C. Hierarchical modular structure enhances the robustness of self-organized criticality in neural networks. N J Physics. 2012 ; 14: 023005
95. Williams GV, Castner SA. Under the curve: critical issues for elucidating D1 receptor function in working memory. Neurosci. 2006 ; 139: 263-76
96. Wolfram S. Universality and complexity in cellular automata. Physica D. 1984 ; 10: 1-35
97. Yang H, Shew WL, Roy R, Plenz D. Maximal variability of phase synchrony in cortical networks with neuronal avalanches. J Neurosci. 2012 ; 32: 1061-72
98. Yizhar O, Fenno LE, Prigge M, Schneider F, Davidson TJ, O'Shea DJ. Neocortical excitation/inhibition balance in information processing and social dysfunction. Nature. 2011 ; 477: 171-8
99. Zhang Z, Jiao Y-Y, Sun Q-Q. Developmental maturation of excitation and inhibition balance in principal neurons across four layers of somatosensory cortex. Neuroscience. 2011 ; 174: 10-25