Optimal dynamical range of excitable networks at criticality

By Osame Kinouchi, Mauro Copelli. 2006

810 citations recorded by the source corpus.

A recurrent idea in the study of complex systems is that optimal information processing is to be found near phase transitions. However, this heuristic hypothesis has few (if any) concrete realizations where a standard and biologically relevant quantity is optimized at criticality. Here we give a clear example of such a phenomenon: a network of excitable elements has its sensitivity and dynamic range maximized at the critical point of a non-equilibrium phase transition. Our results are compatible with the essential role of gap junctions in olfactory glomeruli and retinal ganglionar cell output. Synchronization and global oscillations also emerge from the network dynamics. We propose that the main functional role of electrical coupling is to provide an enhancement of dynamic range, therefore allowing the coding of information spanning several orders of magnitude. The mechanism could provide a microscopic neural basis for psychophysical laws.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/e45f7679ed3aa953a8c75f21de15355408d9bb7d

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Computer Networks and Communications

Citations in the corpus

Criticality in Large-Scale Brain fMRI Dynamics Unveiled by a Novel Point Process Analysis (2012, background): “…avalanches of activity raises the question of the physiologically relevance encoded in the timing of these largescale events, already suggested by observations at smaller scales and computational models (Kinouchi and Copelli, 2006; Shew et al., 2009, 2011; de Arcangelis and Herrmann, 2010).”
The criticality hypothesis: how local cortical networks might optimize information processing (2008, background): “Indeed, when a feedforward neural network based on a branching process is tuned to the critical point, it has optimal information transmission (figure 8; Beggs & Plenz 2003; Bertschinger & Natschlager 2004; Kinouchi & Copelli 2006).”
Criticality in the brain: A synthesis of neurobiology, models and cognition. (2017, background): “Perhaps most crucially, optimal dynamic range – the sensitivity of a neuronal system to respond to, and amplify, inputs across a broad spectrum of intensities – was shown to be maximized in models of neuronal systems tuned to a critical state (Kinouchi and Copelli, 2006; Larremore et al., 2011).”
Neuronal Avalanches Imply Maximum Dynamic Range in Cortical Networks at Criticality (2009, methodology): “Considering the simplicity of our model with all-to-all connectivity, absence of refractory period, and approximating inhibition by reducing σ, the overall agreement in the ∆−κ relationship between experiment and simulation is remarkable.”
Emergent complex neural dynamics (2010, background): “[84] Kinouchi O & Copelli M, Optimal dynamical range of excitable networks at criticality.”
Griffiths phases and the stretching of criticality in brain networks (2013, background): “Critical dynamics provides a delicate trade-off between these two impractical tendencies, and has been argued to imply optimal transmission and storage of information [7, 8, 17], optimal computational capabilities [18], large network stability [16], maximal variety of memory repertoires [19], and ma”
Psychophysics of time perception and intertemporal choice models (2008, background): “Moreover, the aforementioned biophysical simulation studies [25,30,31] demonstrated that the Stevens’ exponent may be a critical exponent in a non-equilibrium phase transition of the neural system.”
Colloquium: Criticality and dynamical scaling in living systems (2017, background): “To better quantify this, a related quantity, dubbed dynamic range, was introduced in Kinouchi and Copelli (2006).”
Optimal Hierarchical Modular Topologies for Producing Limited Sustained Activation of Neural Networks (2009, positive): “This idea is supported by work showing that the dynamic range in an excitable network is optimized at criticality (Kinouchi and Copelli, 2006).”
Being Critical of Criticality in the Brain (2012, background): “In addition, the ability of the network to respond to inputs of many different sizes, called its dynamic range, is expected to be optimal at the critical point (Kinouchi and Copelli, 2006; Shew et al., 2009).”
Information Capacity and Transmission Are Maximized in Balanced Cortical Networks with Neuronal Avalanches (2010, background): “When pc 1/M, activity propagation is widespread and highly synchronous, like a seizure, whereas pc 1/M results in weakly interacting, mostly independent neurons (Beggs and Plenz, 2003; Haldeman and Beggs, 2005; Kinouchi and Copelli, 2006).”
Power laws and Self-Organized Criticality in Theory and Nature (2013, background): “Also, Kinouchi and Copelli [88] demonstrated that the critical regime is related to a maximal sensitivity of a neural network to the variations in the input activity.”
Critical phenomena in complex networks (2007, methodology): “Excitable networks with complex architectures were studied in Kinouchi and Copelli (2006), Copelli and Campos (2007), and Wu et al. (2007b).”