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Being Critical of Criticality in the Brain
Open accessAbstract
Relatively recent work has reported that networks of neurons can produce avalanches of activity whose sizes follow a power law distribution. This suggests that these networks may be operating near a critical point, poised between a phase where activity rapidly dies out and a phase where activity is amplified over time. The hypothesis that the electrical activity of neural networks in the brain is critical is potentially important, as many simulations suggest that information processing functions would be optimized at the critical point. This hypothesis, however, is still controversial. Here we will explain the concept of criticality and review the substantial objections to the criticality hypothesis raised by skeptics. Points and counter points are presented in dialog form.
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Citations
Cites
The Fractal Geometry of Nature
Mandelbrot, B. B., & Wheeler, J. A. (1983) · American Journal of Physics · doi:10.1119/1.13295 · Cited by 21,832
Phase Transitions and Critical Phenomena
(2016) · Statistical and Thermal Physics · doi:10.1201/9781439850541-13 · Cited by 11,176
Self-organized criticality: An explanation of the 1/f noise
Bak, P., Tang, C., & Wiesenfeld, K. (1987) · Physical Review Letters · doi:10.1103/physrevlett.59.381 · Cited by 7,591
Simple mathematical models with very complicated dynamics
May, R. M. (1976) · Nature · doi:10.1038/261459a0 · Cited by 7,001
Power-law distributions in empirical data
Clauset, A. J., Shalizi, C. R., & Newman, M. E. J. (2018) · Figshare · doi:10.1184/r1/6586835.v1 · Cited by 6,504
Newman (2005) · doi:10.1080/00107510500052444 · Cited by 5,749
Introduction to Phase Transitions and Critical Phenomena
Stanley, H. E., & Wong, V. K. (1972) · American Journal of Physics · doi:10.1119/1.1986710 · Cited by 4,085
Maass, W., Natschläger, T., & Markram, H. (2002) · Neural Computation · doi:10.1162/089976602760407955 · Cited by 4,025
Introduction to Phase Transitions and Critical Phenomena
Stanley, H. E., & Ahlers, G. (1973) · Physics Today · doi:10.1063/1.3127900 · Cited by 3,629
How Nature Works: The Science of Self-Organized Criticality
Bak, P., & Weissman, M. (1997) · American Journal of Physics · doi:10.1119/1.18610 · Cited by 2,699
Lectures on Phase Transitions and the Renormalization Group
Goldenfeld, N. (2018) · CRC Press eBooks · doi:10.1201/9780429493492 · Cited by 2,353
Neuronal Avalanches in Neocortical Circuits
Beggs, J. M., & Plenz, D. (2003) · The Journal of Neuroscience · doi:10.1523/jneurosci.23-35-11167.2003 · Cited by 2,325
Self-citationWeak pairwise correlations imply strongly correlated network states in a neural population
Schneidman, E., Berry, M. J., Segev, R., & Bialek, W. (2006) · Nature · doi:10.1038/nature04701 · Cited by 1,760
Exploring complexity: an introduction
(1990) · Choice Reviews Online · doi:10.5860/choice.27-4495 · Cited by 1,234
A Brief History of Generative Models for Power Law and Lognormal Distributions
Mitzenmacher, M. (2004) · Internet Mathematics · doi:10.1080/15427951.2004.10129088 · Cited by 1,188
Sethna, J. P., Dahmen, K. A., & Myers, C. R. (2001) · Nature · doi:10.1038/35065675 · Cited by 1,188
Emergent complex neural dynamics
Chialvo, D. R. (2010) · Nature Physics · doi:10.1038/nphys1803 · Cited by 1,165
Long-Range Temporal Correlations and Scaling Behavior in Human Brain Oscillations
Linkenkaer-Hansen, K., Nikouline, V. V., Palva, J. M., & Ilmoniemi, R. J. (2001) · The Journal of Neuroscience · doi:10.1523/jneurosci.21-04-01370.2001 · Cited by 1,149 · cited 2×
“POWER LAWS AND NEURAL DATA Critio: Well, there were several early reports that the nervous system could produce power law distributions (Chen et al., 1997; Teich et al., 1997; Linkenkaer-Hansen et al., 2001; Worrell et al., 2002).”
BackgroundScaling, universality, and renormalization: Three pillars of modern critical phenomena
Stanley, H. E. (1999) · Reviews of Modern Physics · doi:10.1103/revmodphys.71.s358 · Cited by 939
Real-Time Computation at the Edge of Chaos in Recurrent Neural Networks
Bertschinger, N., & Natschläger, T. (2004) · Neural Computation · doi:10.1162/089976604323057443 · Cited by 833
Optimal dynamical range of excitable networks at criticality
Kinouchi, O., & Copelli, M. (2006) · Nature Physics · doi:10.1038/nphys289 · Cited by 831
Statistical Mechanics of Phase Transitions
Yeomans, J. M. (1992) · doi:10.1093/oso/9780198517290.001.0001 · Cited by 809
Power-Law Scaling in the Brain Surface Electric Potential
Miller, K. J., Sorensen, L. B., Ojemann, J. G., & den Nijs, M. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000609 · Cited by 742
Are Biological Systems Poised at Criticality?
Mora, T., & Bialek, W. (2011) · Journal of Statistical Physics · doi:10.1007/s10955-011-0229-4 · Cited by 734
History of the Lenz-Ising Model
BRUSH, S. G. (1967) · Reviews of Modern Physics · doi:10.1103/revmodphys.39.883 · Cited by 721
Statistical Mechanics of Phase Transitions
Yeomans, J. M., & Rudnick, J. (1993) · Physics Today · doi:10.1063/1.2808979 · Cited by 685
Critical Truths About Power Laws
Stumpf, M. P. H., & Porter, M. A. (2012) · Science · doi:10.1126/science.1216142 · Cited by 633
Neuronal Avalanches Imply Maximum Dynamic Range in Cortical Networks at Criticality
Shew, W. L., Yang, H., Petermann, T., Roy, R., & Plenz, D. (2009) · The Journal of Neuroscience · doi:10.1523/jneurosci.3864-09.2009 · Cited by 613
Shew, W. L., Yang, H., Yu, S., Roy, R., & Plenz, D. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.4637-10.2011 · Cited by 588
Cascade Models of Synaptically Stored Memories
Fusi, S., Drew, P. J., & Abbott, L. F. (2005) · Neuron · doi:10.1016/j.neuron.2005.02.001 · Cited by 569
Spontaneous cortical activity in awake monkeys composed of neuronal avalanches
Petermann, T., Thiagarajan, T. C., Lebedev, M. A., Nicolelis, M. A. L., Chialvo, D. R., & Plenz, D. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0904089106 · Cited by 568
Dynamical synapses causing self-organized criticality in neural networks
Levina, A., Herrmann, J. M., & Geisel, T. (2007) · Nature Physics · doi:10.1038/nphys758 · Cited by 539
The criticality hypothesis: how local cortical networks might optimize information processing
Beggs, J. M. (2008) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2007.2092 · Cited by 533
Self-citationBroadband criticality of human brain network synchronization
Kitzbichler, M. G., Smith, M. L., Christensen, S. R., & Bullmore, E. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000314 · Cited by 527
Self-Organized Criticality: Emergent Complex Behavior in Physical and Biological Systems
Jensen, H. J., & Magnasco, M. O. (1999) · Physics Today · doi:10.1063/1.882869 · Cited by 521
Universal Critical Dynamics in High Resolution Neuronal Avalanche Data
Friedman, N., Ito, S., Brinkman, B. A. W., Shimono, M., DeVille, R. E. L., Dahmen, K. A., Beggs, J. M., & Butler, T. C. (2012) · Physical Review Letters · doi:10.1103/physrevlett.108.208102 · Cited by 501
Self-citationHaldeman, C., & Beggs, J. M. (2005) · Physical Review Letters · doi:10.1103/physrevlett.94.058101 · Cited by 469
Self-citationThe Structure of Multi-Neuron Firing Patterns in Primate Retina
Shlens, J., Field, G. D., Gauthier, J. L., Grivich, M. I., Petrusca, D., Sher, A., Litke, A. M., & Chichilnisky, E. J. (2006) · The Journal of Neuroscience · doi:10.1523/jneurosci.1282-06.2006 · Cited by 468
An Introduction to the Ising Model
Cipra, B. A. (1987) · The American Mathematical Monthly · doi:10.1080/00029890.1987.12000742 · Cited by 419
Does the 1 / f Frequency Scaling of Brain Signals Reflect Self-Organized Critical States?
Bédard, C., Kröger, H., & Destexhe, A. (2006) · Physical Review Letters · doi:10.1103/physrevlett.97.118102 · Cited by 392
Self-organization and neuronal avalanches in networks of dissociated cortical neurons
Pasquale, V., Massobrio, P., Bologna, L. L., Chiappalone, M., & Martinoia, S. (2008) · Neuroscience · doi:10.1016/j.neuroscience.2008.03.050 · Cited by 388
The organizing principles of neuronal avalanches: cell assemblies in the cortex?
Plenz, D., & Thiagarajan, T. C. (2007) · Trends in Neurosciences · doi:10.1016/j.tins.2007.01.005 · Cited by 375
Elements of Phase Transitions and Critical Phenomena
Nishimori, H., & Ortiz, G. (2010) · doi:10.1093/acprof:oso/9780199577224.001.0001 · Cited by 345
Gireesh, E. D., & Plenz, D. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0800537105 · Cited by 340
Genetic networks with canalyzing Boolean rules are always stable
Kauffman, S., Peterson, C., Samuelsson, B., & Troein, C. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0407783101 · Cited by 340
A Small World of Neuronal Synchrony
Yu, S., Huang, D., Singer, W., & Nikolić, D. (2008) · Cerebral Cortex · doi:10.1093/cercor/bhn047 · Cited by 334
Epilepsy in Small-World Networks
Netoff, T. I., Clewley, R., Arno, S., Keck, T., & White, J. A. (2004) · The Journal of Neuroscience · doi:10.1523/jneurosci.1509-04.2004 · Cited by 329
Scale invariance in biology: coincidence or footprint of a universal mechanism?
GISIGER, T. (2001) · Biological Reviews · doi:10.1017/s1464793101005607 · Cited by 323
Avalanches, Barkhausen Noise, and Plain Old Criticality
Perković, O., Dahmen, K., & Sethna, J. P. (1995) · Physical Review Letters · doi:10.1103/physrevlett.75.4528 · Cited by 303
A Maximum Entropy Model Applied to Spatial and Temporal Correlations from Cortical Networks In Vitro
Tang, A., Jackson, D., Hobbs, J., Chen, W., Smith, J. L., Patel, H., Prieto, A., Petrusca, D., Grivich, M. I., Sher, A., et al. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.3359-07.2008 · Cited by 302
Self-citationLong Memory Processes (1/fαType) in Human Coordination
Chen, Y., Ding, M., & Kelso, J. A. S. (1997) · Physical Review Letters · doi:10.1103/physrevlett.79.4501 · Cited by 288
On the dynamics of the spontaneous activity in neuronal networks
Mazzoni, A., Broccard, F. D., Garcia-Perez, E., Bonifazi, P., Ruaro, M. E., & Torre, V. (2007) · PLoS ONE · doi:10.1371/journal.pone.0000439 · Cited by 279
Reed, W. J., & Hughes, B. D. (2002) · Physical Review E · doi:10.1103/physreve.66.067103 · Cited by 278
Statistical Analyses Support Power Law Distributions Found in Neuronal Avalanches
Klaus, A., Yu, S., & Plenz, D. (2011) · PLoS ONE · doi:10.1371/journal.pone.0019779 · Cited by 269
Rubinov, M., Sporns, O., Thivierge, J.-P., & Breakspear, M. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002038 · Cited by 266
R. Chialvo, D. (2004) · Physica A: Statistical Mechanics and its Applications · doi:10.1016/j.physa.2004.05.064 · Cited by 266
Self-Organized Criticality Model for Brain Plasticity
de Arcangelis, L., Perrone-Capano, C., & Herrmann, H. J. (2006) · Physical Review Letters · doi:10.1103/physrevlett.96.028107 · Cited by 263
Scale‐free dynamics of global functional connectivity in the human brain
Stam, C. J., & de Bruin, E. A. (2004) · Human Brain Mapping · doi:10.1002/hbm.20016 · Cited by 248
Higher-Order Interactions Characterized in Cortical Activity
Yu, S., Yang, H., Nakahara, H., Santos, G. S., Nikolić, D., & Plenz, D. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.3127-11.2011 · Cited by 226
Fractal character of the neural spike train in the visual system of the cat
Teich, M. C., Heneghan, C., Lowen, S. B., Ozaki, T., & Kaplan, E. (1997) · Journal of the Optical Society of America A · doi:10.1364/josaa.14.000529 · Cited by 210
Maximal Variability of Phase Synchrony in Cortical Networks with Neuronal Avalanches
Yang, H., Shew, W. L., Roy, R., & Plenz, D. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.2771-11.2012 · Cited by 208
Neuronal Avalanches in Spontaneous Activity In Vivo
Hahn, G., Petermann, T., Havenith, M. N., Yu, S., Singer, W., Plenz, D., & Nikolić, D. (2010) · Journal of Neurophysiology · doi:10.1152/jn.00953.2009 · Cited by 207
Self-Organized Criticality in Developing Neuronal Networks
Tetzlaff, C., Okujeni, S., Egert, U., Wörgötter, F., & Butz, M. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1001013 · Cited by 207
Macroscopic Models of Local Field Potentials and the Apparent 1/f Noise in Brain Activity
Bédard, C., & Destexhe, A. (2009) · Biophysical Journal · doi:10.1016/j.bpj.2008.12.3951 · Cited by 206
Bienenstock, E. (1995) · Network: Computation in Neural Systems · doi:10.1088/0954-898x/6/2/004 · Cited by 205
Disorder-induced critical phenomena in hysteresis: Numerical scaling in three and higher dimensions
Perković, O., Dahmen, K. A., & Sethna, J. P. (1999) · Physical Review B · doi:10.1103/physrevb.59.6106 · Cited by 204
Spike Avalanches Exhibit Universal Dynamics across the Sleep-Wake Cycle
Ribeiro, T. L., Copelli, M., Caixeta, F., Belchior, H., Chialvo, D. R., Nicolelis, M. A. L., & Ribeiro, S. (2010) · PLoS ONE · doi:10.1371/journal.pone.0014129 · Cited by 201
Multiplicative processes and power laws
Sornette, D. (1998) · Physical Review E · doi:10.1103/physreve.57.4811 · Cited by 198
Avalanches in a Stochastic Model of Spiking Neurons
Benayoun, M., Cowan, J. D., van Drongelen, W., & Wallace, E. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000846 · Cited by 197
Field-theoretic approach to fluctuation effects in neural networks
Buice, M. A., & Cowan, J. D. (2007) · Physical Review E · doi:10.1103/physreve.75.051919 · Cited by 196
Can Power-Law Scaling and Neuronal Avalanches Arise from Stochastic Dynamics?
Touboul, J., & Destexhe, A. (2010) · PLoS ONE · doi:10.1371/journal.pone.0008982 · Cited by 190
Scaling in Ordered and Critical Random Boolean Networks
Socolar, J. E. S., & Kauffman, S. A. (2003) · Physical Review Letters · doi:10.1103/physrevlett.90.068702 · Cited by 182
Self-organized criticality occurs in non-conservative neuronal networks during ‘up’ states
Millman, D., Mihalas, S., Kirkwood, A., & Niebur, E. (2010) · Nature Physics · doi:10.1038/nphys1757 · Cited by 182
Systematic Fluctuation Expansion for Neural Network Activity Equations
Buice, M. A., Cowan, J. D., & Chow, C. C. (2010) · Neural Computation · doi:10.1162/neco.2009.02-09-960 · Cited by 164
Kozma, R., Puljic, M., Balister, P., Bollobás, B., & Freeman, W. J. (2005) · Biological Cybernetics · doi:10.1007/s00422-005-0565-z · Cited by 163
Chialvo, D. R., & Bak, P. (1999) · Neuroscience · doi:10.1016/s0306-4522(98)00472-2 · Cited by 160
Learning as a phenomenon occurring in a critical state
de Arcangelis, L., & Herrmann, H. J. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0912289107 · Cited by 158
Toib, A., Lyakhov, V., & Marom, S. (1998) · The Journal of Neuroscience · doi:10.1523/jneurosci.18-05-01893.1998 · Cited by 158
Spontaneous brain activity as a source of ideal 1/f noise
Allegrini, P., Menicucci, D., Bedini, R., Fronzoni, L., Gemignani, A., Grigolini, P., West, B. J., & Paradisi, P. (2009) · Physical Review E · doi:10.1103/physreve.80.061914 · Cited by 151
Stewart, C. V., & Plenz, D. (2006) · Journal of Neuroscience · doi:10.1523/jneurosci.0723-06.2006 · Cited by 146
The Structure of Large-Scale Synchronized Firing in Primate Retina
Shlens, J., Field, G. D., Gauthier, J. L., Greschner, M., Sher, A., Litke, A. M., & Chichilnisky, E. J. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.5187-08.2009 · Cited by 145
Phase Transitions towards Criticality in a Neural System with Adaptive Interactions
Levina, A., Herrmann, J. M., & Geisel, T. (2009) · Physical Review Letters · doi:10.1103/physrevlett.102.118110 · Cited by 142
Subsampling effects in neuronal avalanche distributions recorded in vivo
Priesemann, V., Munk, M. H. J., & Wibral, M. (2009) · BMC Neuroscience · doi:10.1186/1471-2202-10-40 · Cited by 140
Hsu, D., Tang, A., Hsu, M., & Beggs, J. M. (2007) · Physical Review E · doi:10.1103/physreve.76.041909 · Cited by 138
Self-citationFerromagnets and Simple Fluids near the Critical Point: Some Thermodynamic Inequalities
Griffiths, R. B. (1965) · The Journal of Chemical Physics · doi:10.1063/1.1697060 · Cited by 137
Quantal Neurotransmitter Secretion Rate Exhibits Fractal Behavior
Lowen, S. B., Cash, S. S., Poo, M., & Teich, M. C. (1997) · Journal of Neuroscience · doi:10.1523/jneurosci.17-15-05666.1997 · Cited by 134
Self-Organized Criticality in the Olami-Feder-Christensen Model
de Carvalho, J. X., & Prado, C. P. C. (2000) · Physical Review Letters · doi:10.1103/physrevlett.84.4006 · Cited by 119
Finite-size effects of avalanche dynamics
Eurich, C. W., Herrmann, J. M., & Ernst, U. A. (2002) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.66.066137 · Cited by 119
Poil, S., van Ooyen, A., & Linkenkaer‐Hansen, K. (2008) · Human Brain Mapping · doi:10.1002/hbm.20590 · Cited by 118
Self-Tuned Critical Anti-Hebbian Networks
Magnasco, M. O., Piro, O., & Cecchi, G. (2009) · Physical Review Letters · doi:10.1103/physrevlett.102.258102 · Cited by 107
Universal mean moment rate profiles of earthquake ruptures
Mehta, A. P., Dahmen, K. A., & Ben‐Zion, Y. (2006) · Physical Review E · doi:10.1103/physreve.73.056104 · Cited by 101
Spin glass models for a network of real neuronsPreprint
Tkačik, G., Schneidman, E., Berry, M. J., & Bialek, W. (2009) · arXiv (Cornell University) · doi:10.48550/arxiv.0912.5409 · Cited by 100
Dehghani, N., Hatsopoulos, N. G., Haga, Z. D., Parker, R., Greger, B. E., Halgren, E., Cash, S. S., & Alain Destexhe, A. (2012) · Frontiers in Physiology · doi:10.3389/fphys.2012.00302 · Cited by 96
Fading memory and kernel properties of generic cortical microcircuit models
Maass, W., Natschläger, T., & Markram, H. (2004) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2005.09.020 · Cited by 95
A field-theoretic approach to understanding scale-free neocortical dynamics
Freeman, W. (2005) · Biological Cybernetics · doi:10.1007/s00422-005-0563-1 · Cited by 94
Homeostasis of neuronal avalanches during postnatal cortex development in vitro
Stewart, C. V., & Plenz, D. (2007) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.10.021 · Cited by 87
Statistical mechanics of the neocortex
Buice, M. A., & Cowan, J. D. (2009) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2009.07.003 · Cited by 84
Evidence for self-organized criticality in human epileptic hippocampus
Worrell, G. A., Cranstoun, S. D., Echauz, J., & Litt, B. (2002) · Neuroreport · doi:10.1097/00001756-200211150-00005 · Cited by 84
Adaptive learning by extremal dynamics and negative feedback
Bak, P., & Chialvo, D. R. (2001) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.63.031912 · Cited by 78
Scaling, Universality, and Renormalization: Three Pillars of Modern Critical Phenomena
Stanley, E. (1999) · Springer eBooks · doi:10.1007/978-1-4612-1512-7_39 · Cited by 77
Activity-dependent neural network model on scale-free networks
Pellegrini, G. L., de Arcangelis, L., Herrmann, H. J., & Perrone‐Capano, C. (2007) · Physical Review E · doi:10.1103/physreve.76.016107 · Cited by 73
Neural Network Modeling of Memory Deterioration in Alzheimer's Disease
Horn, D., Ruppin, E., Usher, M., & Herrmann, J. M. (1993) · Neural Computation · doi:10.1162/neco.1993.5.5.736 · Cited by 68
Local cortical circuit model inferred from power-law distributed neuronal avalanches
Teramae, J.-N., & Fukai, T. (2007) · Journal of Computational Neuroscience · doi:10.1007/s10827-006-0014-6 · Cited by 63
Recurrent Infomax Generates Cell Assemblies, Neuronal Avalanches, and Simple Cell-Like Selectivity
Tanaka, T., Kaneko, T., & Aoyagi, T. (2008) · Neural Computation · doi:10.1162/neco.2008.03-08-727 · Cited by 61
Measures for information propagation in Boolean networks
Rämö, P., Kauffman, S. A., Kesseli, J., & Yli-Harja, O. P. (2006) · Physica D Nonlinear Phenomena · doi:10.1016/j.physd.2006.12.005 · Cited by 57
Maximum Entropy Approaches to Living Neural Networks
Yeh, F.-C., Tang, A., Hobbs, J., Hottowy, P., Dąbrowski, W., Sher, A., Litke, A. M., & Beggs, J. M. (2010) · Entropy · doi:10.3390/e12010089 · Cited by 55
Self-citationAberrant Neuronal Avalanches in Cortical Tissue Removed From Juvenile Epilepsy Patients
Hobbs, J., Smith, J. L., & Beggs, J. M. (2010) · Journal of Clinical Neurophysiology · doi:10.1097/wnp.0b013e3181fdf8d3 · Cited by 47
Self-citationLarremore, D. B., Shew, W. L., Ott, E., & Restrepo, J. G. (2011) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.3600760 · Cited by 43
An open hypothesis: Is epilepsy learned, and can it be unlearned?
Hsu, D., Chen, W., Hsu, M., & Beggs, J. M. (2008) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2008.05.007 · Cited by 39
Self-citationNeuronal avalanches and criticality: A dynamical model for homeostasis
Hsu, D., & Beggs, J. M. (2006) · Neurocomputing · doi:10.1016/j.neucom.2005.12.060 · Cited by 38
Self-citationAnalytical investigation of self-organized criticality in neural networks
Droste, F., Do, A.-L., & Groß, T. (2012) · Journal of The Royal Society Interface · doi:10.1098/rsif.2012.0558 · Cited by 37
A few strong connections: optimizing information retention in neuronal avalanches
Chen, W., Hobbs, J., Tang, A., & Beggs, J. M. (2010) · BMC Neuroscience · doi:10.1186/1471-2202-11-3 · Cited by 37
Self-citationSelf-organized criticality emergent complex behavior in physical and biological systems
Jensen, H. J. 1. (1998) · Cited by 26
Stability and spectra of randomly connected excitatory cortical networks
Gray, R. T., & Robinson, P. A. (2006) · Neurocomputing · doi:10.1016/j.neucom.2006.03.014 · Cited by 20
Critical branching neural computation
Kello, C. T., & Mayberry, M. R. (2010) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2010.5596813 · Cited by 13
Connectivity and Dynamics in Local Cortical Networks
Beggs, J. M., Klukas, J., & Chen, W. (2007) · Understanding complex systems · doi:10.1007/978-3-540-71512-2_3 · Cited by 11
Self-citation
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Self-organized criticality as a fundamental property of neural systems
Hesse, J., & Gross, T. (2014) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2014.00166 · Cited by 440
Inverse statistical problems: from the inverse Ising problem to data science
Nguyen, H. C., Zecchina, R., & Berg, J. (2017) · Advances in Physics · doi:10.1080/00018732.2017.1341604 · Cited by 258
Power-law statistics and universal scaling in the absence of criticality
Touboul, J., & Destexhe, A. (2017) · Physical Review E · doi:10.1103/physreve.95.012413 · Cited by 210
Identification of optimal structural connectivity using functional connectivity and neural modeling
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Jedlicka, P. (2017) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2017.00366 · Cited by 97
Metadata
- Authors
- Beggs, J. M., & Timme, N.
- Venue
- Frontiers in Physiology
- Publisher
- Frontiers Media SA
- Published
- 2012
- Volume
- 3
- Pages
- 163-163
- ISSN
- 1664-042X (ISSN-L)
- OpenAlex
- W2142211992
- Cited by
- 494
- References
- 116
- FWCI
- 15.86
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.