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- Open Access
Self-organization to sub-criticality
BMC Neuroscience volume 16, Article number: O19 (2015)
Human brains possess sophisticated information processing capabilities, which rely on the interactions of billions of neurons. However, it is unclear how these capabilities arise from the collective spiking dynamics. A popular hypothesis is that neural networks assume a critical state [1, 2], because in models criticality maximizes information processing capabilities [3, 4]. However, it has been largely overlooked that criticality in neural networks also comes with the risk of spontaneous runaway activity , which has been linked to epilepsy. Does the brain indeed assume a critical state, despite the risk of instability? To revisit this question, we analyzed spiking activity from awake animals, instead of more coarse measures of neural activity (population spikes, LPF, EEG, BOLD) as in most previous studies. In all recordings (rats hippocampus, cats visual cortex, and monkey prefrontal cortex), spiking activity resembled a sub-critical state, not criticality proper . We confirmed these results using a novel mathematical approach that is robust to subsampling effects  [see Wilting & Priesemann, conference proceedings CNS 2015]. While 'self-organization' to criticality has been widely studied (e.g.[5, 8]), it is unclear what mechanism allows self-organize to sub-criticality instead. Here, we demonstrate that homeostatic plasticity  assures that networks assume a slightly sub-critical state, independently of the initial configuration. Surprisingly, increasing the external input (stimuli) altered the set-point of the network to a more sub-critical state. Our results suggest that homeostasis allows the brain to maintain a safety margin to criticality. Thereby the brain may lose processing capability, but avoids instability.
Beggs JM, Plenz D: Neuronal avalanches in neocortical circuits. J Neurosci. 2003, 23: 11167-11177.
Priesemann V, Valderrama M, Wibral M, Le Van Quyen M: Neuronal Avalanches Differ from Wakefulness to Deep Sleep-Evidence from Intracranial Depth Recordings in Humans. PLoS Comput Biol. 2013, 9: e1002985-
Boedecker J, Obst O, Lizier JT, Mayer NM, Asada M: Information processing in echo state networks at the edge of chaos. Theory Biosci. 2012, 131: 205-213.
Bertschinger N, Natschläger T: Real-time computation at the edge of chaos in recurrent neural networks. Neural Comput. 2004, 16: 1413-1436.
Bak P, Tang C, Wiesenfeld K: Self-organized criticality: An explanation of 1/f noise. Phys Rev Lett. 1987, 59: 381-384.
Priesemann V, Wibral M, Valderrama M, Pröpper R, Le Van Quyen M, Geisel T, et al: Spike avalanches in vivo suggest a driven, slightly subcritical brain state. Front Syst Neurosci. 2014, 8: 108-
Priesemann V, Munk MH, Wibral M: Subsampling effects in neuronal avalanche distributions recorded in vivo. BMC Neurosci. 2009, 10: 40-
Levina A, Herrmann JM, Geisel T: Dynamical synapses causing self-organized criticality in neural networks. Nat Phys. 2007, 3: 857-860.
Turrigiano G: Homeostatic synaptic plasticity: local and global mechanisms for stabilizing neuronal function. Cold Spring Harb Perspect Biol. 2012, 4: a005736-
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Priesemann, V. Self-organization to sub-criticality. BMC Neurosci 16, O19 (2015). https://0-doi-org.brum.beds.ac.uk/10.1186/1471-2202-16-S1-O19
- Critical State
- Visual Cortex
- Safety Margin
- Initial Configuration
- Processing Capability