Adaptation to sensory input tunes visual cortex to criticality

Nature Physics

Volumn 11, Issue 8, 2015, Pages 659-663

  Shew, Woodrow L ^a   Clawson, Wesley P ^a   Pobst, Jeff ^b   Karimipanah, Yahya ^b   Wright, Nathaniel C ^b   Wessel, Ralf ^a  

^a UNIVERSITY OF ARKANSAS   (United States)

^b WASHINGTON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DYNAMICS; NEURAL NETWORKS;

CEREBRAL CORTEX; COMPUTATIONAL MODEL; CORTICAL NETWORK; CRITICAL DYNAMICS; SELF-ORGANIZATION PROCESS; SELF-ORGANIZING MECHANISM; SENSORY INFORMATION PROCESSING; VISUAL CORTEXES;

CRITICALITY (NUCLEAR FISSION);

EID: 84938552251     PISSN: 17452473     EISSN: 17452481     Source Type: Journal    
DOI: 10.1038/nphys3370     Document Type: Article

References (33)

1. Plenz D., Niebur E., & Schuster H. G. Criticality in Neural Systems (Wiley, 2014
2. Beggs J. M. The criticality hypothesis: How local cortical networks might optimize information processing. Phil. Trans. R. Soc. A 366, 329-343 (2008
3. Shew W. L., & Plenz D. The functional benefits of criticality in the cortex. Neuroscientist 19, 88-100 (2013
4. Friedman N., et al. Universal critical dynamics in high resolution neuronal avalanche data. Phys. Rev. Lett. 108, 1-5 (2012
5. Beggs J. M., & Plenz D. Neuronal avalanches in neocortical circuits. J. Neurosci. 23, 11167-11177 (2003
6. Shew W. L., Yang H., Yu S., Roy R., & Plenz D. Information capacity and transmission are maximized in balanced cortical networks with neuronal avalanches. J. Neurosci. 31, 55-63 (2011
7. Petermann T., et al. Spontaneous cortical activity in awake monkeys composed of neuronal avalanches. Proc. Natl Acad. Sci. USA 106, 15921-15926 (2009
8. Haimovici A., Tagliazucchi E., Balenzuela P., & Chialvo D. R. Brain organization into resting state networks emerges at criticality on a model of the human connectome. Phys. Rev. Lett. 110, 178101 (2013
9. Priesemann V., Valderrama M., Wibral M., & Le Van Quyen M. Neuronal avalanches di-er from wakefulness to deep sleep -evidence from intracranial depth recordings in humans. PLoS Comput. Biol. 9, e1002985 (2013
10. Klaus A., Yu S., & Plenz D. Statistical analyses support power law distributions found in neuronal avalanches. PLoS ONE 6, e19779 (2011
11. Sethna J. P., Dahmen K. A., & Myers C. R. Crackling noise. Nature 410, 242-250 (2001
12. Kohn A. Visual adaptation: Physiology, mechanisms, and functional benefits. J. Neurophysiol. 97, 3155-3164 (2007
13. Bialek W. Biophysics: Searching for Principles (Princeton Univ. Press, 2012
14. Levina A., Herrmann J. M., & Geisel T. Dynamical synapses causing self-organized criticality in neural networks. Nature Phys. 3, 857-860 (2007
15. Levina A., Herrmann J., & Geisel T. Phase transitions towards criticality in a neural system with adaptive interactions. Phys. Rev. Lett. 102, 118110 (2009
16. Meisel C., & Gross T. Adaptive self-organization in a realistic neural network model. Phys. Rev. E 80, 1-6 (2009
17. Saha D., Morton D., Ariel M., & Wessel R. Response properties of visual neurons in the turtle nucleus isthmi. J. Comp. Physiol. A 197, 153-165 (2011
18. Clauset A., Shalizi C. R., & Newman M. E. J. Power-law distributions in empirical data. SIAM Rev. 51, 661-703 (2009
19. Haldeman C., & Beggs J. M. Critical branching captures activity in living neural networks and maximizes the number of metastable states. Phys. Rev. Lett. 94, 058101 (2005
20. Kinouchi O., & Copelli M. Optimal dynamical range of excitable networks at criticality. Nature Phys. 2, 348-351 (2006
21. Chung S., Li X., & Nelson S. B. Short-term depression at thalamocortical synapses contributes to rapid adaptation of cortical sensory responses in vivo. Neuron 34, 437-446 (2002
22. Sornette D. Critical Phenomena in Natural Sciences: Chaos, Fractals, Selforganization and Disorder: Concepts and Tools (Springer, 2006
23. Stumpf M. P. H., & Porter M. A. Mathematics. Critical truths about power laws. Science 335, 665-666 (2012
24. Beggs J. M., & Timme N. Being critical of criticality in the brain. Front. Physiol. 3, 163 (2012
25. Priesemann V., et al. Spike avalanches in vivo suggest a driven, slightly subcritical brain state. Front. Syst. Neurosci. 8, 108 (2014
26. Larremore D. B., Shew W. L., & Restrepo J. G. Predicting criticality and dynamic range in complex networks: E-ects of topology. Phys. Rev. Lett. 106, 1-4 (2011
27. Ribeiro T. L., Ribeiro S., Belchior H., Caixeta F., & Copelli M. Undersampled critical branching processes on small-world and random networks fail to reproduce the statistics of spike avalanches. PLoS ONE 9, e94992 (2014
28. Shew W. L., Yang H., Petermann T., Roy R., & Plenz D. Neuronal avalanches imply maximum dynamic range in cortical networks at criticality. J. Neurosci. 29, 15595-15600 (2009
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. USA 105, 7576-7581 (2008
30. Bak P., Tang C., & Wiesenfeld K. Self-organized criticality: An explanation of the 1/f noise. Phys. Rev. Lett. 59, 381-384 (1987
31. Bornholdt S., & Röhl T. Self-organized critical neural networks. Phys. Rev. E 67, 1-5 (2003
32. Moretti P., & Muñoz M. A. Gri-ths phases and the stretching of criticality in brain networks. Nature Commun. 4, 2521 (2013
33. Priesemann V., Munk M. H. J., & Wibral M. Subsampling e-ects in neuronal avalanche distributions recorded in vivo. BMC Neurosci. 10, 40 (2009