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Self-organization to sub-criticality

Self-organization to sub-criticality Priesemann BMC Neuroscience 2015, 16(Suppl 1):O19 http://www.biomedcentral.com/1471-2202/16/S1/O19 ORAL PRESENTATION Open Access 1,2 V Priesemann From 24th Annual Computational Neuroscience Meeting: CNS*2015 Prague, Czech Republic. 18-23 July 2015 Published: 18 December 2015 Human brains possess sophisticated information proces- sing capabilities, which rely on the interactions of billions References of neurons. However, it is unclear how these capabilities 1. Beggs JM, Plenz D: Neuronal avalanches in neocortical circuits. J Neurosci arise from the collective spiking dynamics. A popular 2003, 23:11167-11177. 2. Priesemann V, Valderrama M, Wibral M, Le Van Quyen M: Neuronal hypothesis is that neural networks assume a critical state Avalanches Differ from Wakefulness to Deep Sleep-Evidence from [1,2], because in models criticality maximizes information Intracranial Depth Recordings in Humans. PLoS Comput Biol 2013, 9: processing capabilities [3,4]. However, it has been largely e1002985. 3. Boedecker J, Obst O, Lizier JT, Mayer NM, Asada M: Information processing overlooked that criticality in neural networks also comes in echo state networks at the edge of chaos. Theory Biosci 2012, with the risk of spontaneous runaway activity [5], which 131:205-213. has been linked to epilepsy. Does the brain indeed 4. Bertschinger N, Natschläger T: Real-time computation at the edge of chaos in recurrent neural networks. Neural Comput 2004, 16:1413-1436. assume a critical state, despite the risk of instability? To 5. Bak P, Tang C, Wiesenfeld K: Self-organized criticality: An explanation of revisit this question, we analyzed spiking activity from 1/f noise. Phys Rev Lett 1987, 59:381-384. awake animals, instead of more coarse measures of 6. Priesemann V, Wibral M, Valderrama M, Pröpper R, Le Van Quyen M, Geisel T, et al: Spike avalanches in vivo suggest a driven, slightly neural activity (population spikes, LPF, EEG, BOLD) as subcritical brain state. Front Syst Neurosci 2014, 8:108. in most previous studies. In all recordings (rats hippo- 7. Priesemann V, Munk MH, Wibral M: Subsampling effects in neuronal campus, cats visual cortex, and monkey prefrontal cor- avalanche distributions recorded in vivo. BMC Neurosci 2009, 10:40. 8. Levina A, Herrmann JM, Geisel T: Dynamical synapses causing self- tex), spiking activity resembled a sub-critical state, not organized criticality in neural networks. Nat Phys 2007, 3:857-860. criticality proper [6]. We confirmed these results using a 9. Turrigiano G: Homeostatic synaptic plasticity: local and global novel mathematical approach that is robust to subsam- mechanisms for stabilizing neuronal function. Cold Spring Harb Perspect Biol 2012, 4:a005736. pling effects [7] [see Wilting & Priesemann, conference proceedings CNS 2015]. While ‘self-organization’ to criti- doi:10.1186/1471-2202-16-S1-O19 Cite this article as: Priesemann: Self-organization to sub-criticality. BMC cality has been widely studied (e.g.[5,8]), it is unclear Neuroscience 2015 16(Suppl 1):O19. what mechanism allows self-organize to sub-criticality instead. Here, we demonstrate that homeostatic plasticity [9] assures that networks assume a slightly sub-critical state, independently of the initial configuration. Surpris- ingly, increasing the external input (stimuli) altered the set-point of the network to a more sub-critical state. Our Submit your next manuscript to BioMed Central results suggest that homeostasis allows the brain to main- and take full advantage of: tain a safety margin to criticality. Thereby the brain may lose processing capability, but avoids instability. • Convenient online submission • Thorough peer review Authors’ details • No space constraints or color figure charges Department of Nonlinear Dynamics, Max Planck Institute for Dynamics and • Immediate publication on acceptance Self-Organization, Göttingen, Germany. Bernstein Center for Computational • Inclusion in PubMed, CAS, Scopus and Google Scholar Neuroscience, Göttingen, Germany. • Research which is freely available for redistribution Correspondence: viola@nld.ds.mpg.de Department of Nonlinear Dynamics, Max Planck Institute for Dynamics and Submit your manuscript at Self-Organization, Göttingen, Germany www.biomedcentral.com/submit Full list of author information is available at the end of the article © 2015 Priesemann This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http:// creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/ zero/1.0/) applies to the data made available in this article, unless otherwise stated. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png BMC Neuroscience Springer Journals

Self-organization to sub-criticality

Priesemann, V
BMC Neuroscience , Volume 16 (1) – Dec 4, 2015
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References (9)

Publisher
Springer Journals
Copyright
Copyright © 2015 by Priesemann
Subject
Biomedicine; Neurosciences; Neurobiology; Animal Models
eISSN
1471-2202
DOI
10.1186/1471-2202-16-S1-O19
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Abstract

Priesemann BMC Neuroscience 2015, 16(Suppl 1):O19 http://www.biomedcentral.com/1471-2202/16/S1/O19 ORAL PRESENTATION Open Access 1,2 V Priesemann From 24th Annual Computational Neuroscience Meeting: CNS*2015 Prague, Czech Republic. 18-23 July 2015 Published: 18 December 2015 Human brains possess sophisticated information proces- sing capabilities, which rely on the interactions of billions References of neurons. However, it is unclear how these capabilities 1. Beggs JM, Plenz D: Neuronal avalanches in neocortical circuits. J Neurosci arise from the collective spiking dynamics. A popular 2003, 23:11167-11177. 2. Priesemann V, Valderrama M, Wibral M, Le Van Quyen M: Neuronal hypothesis is that neural networks assume a critical state Avalanches Differ from Wakefulness to Deep Sleep-Evidence from [1,2], because in models criticality maximizes information Intracranial Depth Recordings in Humans. PLoS Comput Biol 2013, 9: processing capabilities [3,4]. However, it has been largely e1002985. 3. Boedecker J, Obst O, Lizier JT, Mayer NM, Asada M: Information processing overlooked that criticality in neural networks also comes in echo state networks at the edge of chaos. Theory Biosci 2012, with the risk of spontaneous runaway activity [5], which 131:205-213. has been linked to epilepsy. Does the brain indeed 4. Bertschinger N, Natschläger T: Real-time computation at the edge of chaos in recurrent neural networks. Neural Comput 2004, 16:1413-1436. assume a critical state, despite the risk of instability? To 5. Bak P, Tang C, Wiesenfeld K: Self-organized criticality: An explanation of revisit this question, we analyzed spiking activity from 1/f noise. Phys Rev Lett 1987, 59:381-384. awake animals, instead of more coarse measures of 6. Priesemann V, Wibral M, Valderrama M, Pröpper R, Le Van Quyen M, Geisel T, et al: Spike avalanches in vivo suggest a driven, slightly neural activity (population spikes, LPF, EEG, BOLD) as subcritical brain state. Front Syst Neurosci 2014, 8:108. in most previous studies. In all recordings (rats hippo- 7. Priesemann V, Munk MH, Wibral M: Subsampling effects in neuronal campus, cats visual cortex, and monkey prefrontal cor- avalanche distributions recorded in vivo. BMC Neurosci 2009, 10:40. 8. Levina A, Herrmann JM, Geisel T: Dynamical synapses causing self- tex), spiking activity resembled a sub-critical state, not organized criticality in neural networks. Nat Phys 2007, 3:857-860. criticality proper [6]. We confirmed these results using a 9. Turrigiano G: Homeostatic synaptic plasticity: local and global novel mathematical approach that is robust to subsam- mechanisms for stabilizing neuronal function. Cold Spring Harb Perspect Biol 2012, 4:a005736. pling effects [7] [see Wilting & Priesemann, conference proceedings CNS 2015]. While ‘self-organization’ to criti- doi:10.1186/1471-2202-16-S1-O19 Cite this article as: Priesemann: Self-organization to sub-criticality. BMC cality has been widely studied (e.g.[5,8]), it is unclear Neuroscience 2015 16(Suppl 1):O19. what mechanism allows self-organize to sub-criticality instead. Here, we demonstrate that homeostatic plasticity [9] assures that networks assume a slightly sub-critical state, independently of the initial configuration. Surpris- ingly, increasing the external input (stimuli) altered the set-point of the network to a more sub-critical state. Our Submit your next manuscript to BioMed Central results suggest that homeostasis allows the brain to main- and take full advantage of: tain a safety margin to criticality. Thereby the brain may lose processing capability, but avoids instability. • Convenient online submission • Thorough peer review Authors’ details • No space constraints or color figure charges Department of Nonlinear Dynamics, Max Planck Institute for Dynamics and • Immediate publication on acceptance Self-Organization, Göttingen, Germany. Bernstein Center for Computational • Inclusion in PubMed, CAS, Scopus and Google Scholar Neuroscience, Göttingen, Germany. • Research which is freely available for redistribution Correspondence: viola@nld.ds.mpg.de Department of Nonlinear Dynamics, Max Planck Institute for Dynamics and Submit your manuscript at Self-Organization, Göttingen, Germany www.biomedcentral.com/submit Full list of author information is available at the end of the article © 2015 Priesemann This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http:// creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/ zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Journal

BMC Neuroscience – Springer Journals

Published: Dec 4, 2015

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