Dynamical synapses causing self-organized criticality in neural networks

By Anna Levina, J. Herrmann, Theo Geisel. 2007

564 citations recorded by the source corpus.

Self-organized criticality1 is one of the key concepts to describe the emergence of complexity in natural systems. The concept asserts that a system self-organizes into a critical state where system observables are distributed according to a power law. Prominent examples of self-organized critical dynamics include piling of granular media2, plate tectonics3 and stick–slip motion4. Critical behaviour has been shown to bring about optimal computational capabilities5, optimal transmission6, storage of information7 and sensitivity to sensory stimuli8,9,10. In neuronal systems, the existence of critical avalanches was predicted11 and later observed experimentally6,12,13. However, whereas in the experiments generic critical avalanches were found, in the model of ref. 11 they only show up if the set of parameters is fine-tuned externally to a critical transition state. Here, we demonstrate analytically and numerically that by assuming (biologically more realistic) dynamical synapses14 in a spiking neural network, the neuronal avalanches turn from an exceptional phenomenon into a typical and robust self-organized critical behaviour, if the total resources of neurotransmitter are sufficiently large.

Subfields: Cognitive Neuroscience, Electrical and Electronic Engineering, Statistical and Nonlinear Physics

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Citations in the corpus

Statistical inference using weak chaos and infinite memory (2010, background): “Yet, it can be argued in both cases that the depression is causing the dynamics to be on the edge between regular and chaotic behavior (Levina et al., 2007) which is often associated with high complexity and efficient information processing.”
Small-world and scale-free organization of voxel-based resting-state functional connectivity in the human brain (2008, unknown): “This effect is believed to be the result of a self-organized criticality principle (Levina et al., 2007).”
Collective cognition in animal groups. (2009, result): “propagation of coordinated waves of activity, similar to those seen in the developing retina [13] and vertebrate cortex [17, 21 ,22].”
Complexity, segregation, and pattern formation in rotating-drum flows (2011, background): “…a sound theoretical approach to a variety of nonequilibrium dynamical systems having extended spatial degrees of freedom, such as neural networks (Levina, Herrmann, and Geisel, 2007) and granular high-Tc superconductors (Gerashchenko, 2009), its applicability to avalanches in granular matter is…”
The temporal structures and functional significance of scale-free brain activity (2010, unknown): “An influential model of ‘‘1/f noise’’ is the self-organized criticality (SOC) theory (Bak, 1996; Bak et al., 1987), which has been applied extensively to simulate brain networks (Shin and Kim, 2006), neuronal avalanches and ‘‘1/f noise’’ (de Arcangelis et al., 2006; Levina et al., 2007; Lin and Chen”
Emergent complex neural dynamics (2010, background): “At small scale, explicit models have been already presented in which criticality can be self organized either by some form of Hebbian learning [81, 82] or by the inclusion of activity-dependent depressive synapses [83].”
Griffiths phases and the stretching of criticality in brain networks (2013, background): “[46] Levina A, Herrmann JM, Geisel T (2007) Dynamical synapses causing self-organized criticality in neural networks.”
Colloquium: Criticality and dynamical scaling in living systems (2017, background): “…as a mechanism to regulatory mechanism able to auto-organize them to the edge of a phase transition (de Arcangelis et al., 2006; Gómez et al., 2008; Levina et al., 2007, 2009)31; synaptic resources become depleted owing to network activity and remain so for a characteristic recovery period,…”
Power laws and Self-Organized Criticality in Theory and Nature (2013, background): “More importantly, these neural network models, although replicating very closely the experimentally observed statistical properties of neuronal avalanches, achieve critical behavior only for networks consisting of purely excitatory neurons [98] and the introduction of biologically realistic levels o”
Spike avalanches in vivo suggest a driven, slightly subcritical brain state (2014, background): “…discussed evidence from in vivo spike avalanche distributions for a driven, sub-critical mode of operation, and after having clarified conceptual issues, we now turn to the question of what consequences these findings may have on information processing and dynamic stability in the mammalian brain.”
Being Critical of Criticality in the Brain (2012, background): “…Kozma et al., 2005; de Arcangelis et al., 2006; Hsu and Beggs, 2006; Abbott and Rohrkemper, 2007; Buice and Cowan, 2007, 2009; Juanico et al., 2007; Levina et al., 2007, 2009; Pellegrini et al., 2007; Hsu et al., 2008; Stewart and Plenz, 2008; Allegrini et al., 2009; Magnasco et al., 2009; Tanaka…”