Mechanisms of Self-Organized Quasicriticality in Neuronal Network Models

Frontiers in Physics ◽

10.3389/fphy.2020.583213 ◽

2020 ◽

Vol 8 ◽

Cited By ~ 1

Author(s):

Osame Kinouchi ◽

Renata Pazzini ◽

Mauro Copelli

Keyword(s):

Phase Transition ◽

Information Processing ◽

Critical Point ◽

Neuronal Network ◽

Critical Region ◽

Critical State ◽

Neuronal Networks ◽

Network Models ◽

Biological Mechanisms ◽

Self Organized

The critical brain hypothesis states that there are information processing advantages for neuronal networks working close to the critical region of a phase transition. If this is true, we must ask how the networks achieve and maintain this critical state. Here, we review several proposed biological mechanisms that turn the critical region into an attractor of a dynamics in network parameters like synapses, neuronal gains, and firing thresholds. Since neuronal networks (biological and models) are not conservative but dissipative, we expect not exact criticality but self-organized quasicriticality, where the system hovers around the critical point.

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Biomodeling System - Interaction Between Living Neuronal Networks and the Outer World

Journal of Robotics and Mechatronics ◽

10.20965/jrm.2007.p0592 ◽

2007 ◽

Vol 19 (5) ◽

pp. 592-600 ◽

Cited By ~ 16

Author(s):

Suguru N. Kudoh ◽

◽

Chie Hosokawa ◽

Ai Kiyohara ◽

Takahisa Taguchi ◽

...

Keyword(s):

Information Processing ◽

Neuronal Network ◽

Action Potentials ◽

Hippocampal Neurons ◽

Neuronal Networks ◽

Biological Information ◽

Culture Dish ◽

Outer World ◽

Intelligent Information ◽

Instinctive Behavior

Rat hippocampal neurons reorganized into complex networks in a culture dish with 64 planar microelectrodes and the electrical activity of neurons were recorded from individual sites. Multi-site recording system for extracellular action potentials was used for recording the activity of living neuronal networks and for applying input from the outer world to the network. The living neuronal network was able to distinguish among patterns of evoked action potentials based on different input, suggesting that the living neuronal network can express several pattern independently, meaning that it has fundamental mechanisms for intelligent information processing. We are developing a “biomodeling system,” in which a living neuronal network is connected to a moving robot with premised control rules corresponding to a genetically provided interface of neuronal networks to peripheral systems. Premised rules are described in fuzzy logic and the robot can generate instinctive behavior, avoiding collision. Sensor input from the robot body was sent to a neuronal network, and the robot moved based on commands from the living neuronal network. This is a good modeling system to analyze interaction between biological information processing and electrical devices.

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Cell-Fate Determination from Embryo to Cancer Development: Genomic Mechanism Elucidated

10.1101/637033 ◽

2019 ◽

Author(s):

Masa Tsuchiya ◽

Alessandro Giuliani ◽

Kenichi Yoshikawa

Keyword(s):

Critical Point ◽

Cell Fate ◽

Critical State ◽

Local Level ◽

Stochastic Perturbation ◽

Physical Principle ◽

Cancer Development ◽

Biological Regulation ◽

Self Organized ◽

Organizing Center

AbstractThe elucidation of the how and when of a cell-fate change asks for a physically reasonable mechanism allowing to achieve a coordinated switching of thousands of genes within a small and highly packed cell nucleus. We previously demonstrated that whole genome expression is dynamically self-organized through the emergence of a critical point. Furthermore, it has been confirmed that this happens at both the cell-population and single-cell level through the physical principle of self-organized criticality.In this paper, we further examine the genomic mechanism which determines cell-fate changes from embryo to cancer development. The state of the critical point, acting as the organizing center of cell-fate, determines whether the genome resides in a super- or sub-critical state. In the super-critical state, a specific stochastic perturbation can spread over the entire system through the ‘genome engine’ - an autonomous critical-control genomic system, whereas in the sub-critical state, the perturbation remains at a local level. We provide a consistent framework to develop a biological regulation transition theory demonstrating the cell-fate change.

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The emergence of integrated information, complexity, and consciousness at criticality

10.1101/521567 ◽

2019 ◽

Cited By ~ 7

Author(s):

Sina Khajehabdollahi ◽

Pubuditha M. Abeyasinghe ◽

Adrian M. Owen ◽

Andrea Soddu

Keyword(s):

Phase Transition ◽

Ising Model ◽

Critical Point ◽

Critical Systems ◽

Dynamical Phase ◽

Self Organized ◽

Self Organized Criticality ◽

Ancient Problem ◽

Emergence Of Complexity ◽

Integrated Information

AbstractUsing the critical Ising model of the brain, integrated information as a measure of consciousness is measured in toy models of generic neural networks. Monte Carlo simulations are run on 159 random weighted networks analogous to small 5-node neural network motifs. The integrated information generated by this sample of small Ising models is measured across the model parameter space. It is observed that integrated information, as a type of order parameter not unlike a concept like magnetism, undergoes a phase transition at the critical point in the model. This critical point is demarcated by the peaks of the generalized susceptibility of integrated information, a point where the ‘consciousness’ of the system is maximally susceptible to perturbations and on the boundary between an ordered and disordered form. This study adds further evidence to support that the emergence of consciousness coincides with the more universal patterns of self-organized criticality, evolution, the emergence of complexity, and the integration of complex systems.Author summaryUnderstanding consciousness through a scientific and mathematical language is slowly coming into reach and so testing and grounding these emerging ideas onto empirical observations and known systems is a first step to properly framing this ancient problem. This paper in particular explores the Integrated Information Theory of Consciousness framed within the physics of the Ising model to understand how and when consciousness, or integrated information, can arise in simple dynamical systems. The emergence of consciousness is treated like the emergence of other classical macroscopic observables in physics such as magnetism and understood as a dynamical phase of matter. Our findings show that the sensitivity of consciousness in a complex system is maximized when the system is undergoing a phase transition, also known as a critical point. This result, combined with a body of evidence highlighting the privelaged state of critical systems suggests that, like many other complex phenomenon, consciousness may simply follow from/emerge out of the tendency of a system to self-organize to criticality.

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CRITICAL REGION AT THE NEMATIC-ISOTROPIC PHASE TRANSITION

Modern Physics Letters B ◽

10.1142/s0217984996000869 ◽

1996 ◽

Vol 10 (16) ◽

pp. 771-775 ◽

Cited By ~ 4

Author(s):

PRABIR K. MUKHERJEE

Keyword(s):

Phase Transition ◽

Liquid Crystal ◽

Critical Point ◽

Nematic Liquid Crystal ◽

Critical Region ◽

Isotropic Phase ◽

Transition Line ◽

Isotropic Liquid ◽

Phase Transition Line ◽

First Order

The phase transition from isotropic liquid to nematic liquid crystal is a weak first order one. The present paper investigate the possibility of the critical region near the isolated critical point at the first order nematic-isotropic phase transition line.

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The criticality hypothesis: how local cortical networks might optimize information processing

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2007.2092 ◽

2007 ◽

Vol 366 (1864) ◽

pp. 329-343 ◽

Cited By ~ 229

Author(s):

John M Beggs

Keyword(s):

Phase Transition ◽

Information Processing ◽

Experimental Evidence ◽

Critical Point ◽

Cortical Neurons ◽

Simulation Studies ◽

Computational Power ◽

Cortical Networks ◽

Edge Of Chaos ◽

Near Future

Early theoretical and simulation work independently undertaken by Packard, Langton and Kauffman suggested that adaptability and computational power would be optimized in systems at the ‘edge of chaos’, at a critical point in a phase transition between total randomness and boring order. This provocative hypothesis has received much attention, but biological experiments supporting it have been relatively few. Here, we review recent experiments on networks of cortical neurons, showing that they appear to be operating near the critical point. Simulation studies capture the main features of these data and suggest that criticality may allow cortical networks to optimize information processing. These simulations lead to predictions that could be tested in the near future, possibly providing further experimental evidence for the criticality hypothesis.

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Solving the 2D SUSY Gross-Neveu-Yukawa model with conformal truncation

Journal of High Energy Physics ◽

10.1007/jhep01(2021)182 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

A. Liam Fitzpatrick ◽

Emanuel Katz ◽

Matthew T. Walters ◽

Yuan Xin

Keyword(s):

Phase Transition ◽

Critical Point ◽

Fine Tuning ◽

Yukawa Model ◽

Spectral Functions ◽

Scalar Superfield ◽

Local Operators ◽

Dimensionless Coupling ◽

Zumino Model ◽

Yukawa Theory

Abstract We use Lightcone Conformal Truncation to analyze the RG flow of the two-dimensional supersymmetric Gross-Neveu-Yukawa theory, i.e. the theory of a real scalar superfield with a ℤ2-symmetric cubic superpotential, aka the 2d Wess-Zumino model. The theory depends on a single dimensionless coupling $$ \overline{g} $$ g ¯ , and is expected to have a critical point at a tuned value $$ {\overline{g}}_{\ast } $$ g ¯ ∗ where it flows in the IR to the Tricritical Ising Model (TIM); the theory spontaneously breaks the ℤ2 symmetry on one side of this phase transition, and breaks SUSY on the other side. We calculate the spectrum of energies as a function of $$ \overline{g} $$ g ¯ and see the gap close as the critical point is approached, and numerically read off the critical exponent ν in TIM. Beyond the critical point, the gap remains nearly zero, in agreement with the expectation of a massless Goldstino. We also study spectral functions of local operators on both sides of the phase transition and compare to analytic predictions where possible. In particular, we use the Zamolodchikov C-function to map the entire phase diagram of the theory. Crucial to this analysis is the fact that our truncation is able to preserve supersymmetry sufficiently to avoid any additional fine tuning.

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A Rational Equation of State for Water and Water Vapor in the Critical Region

Journal of Heat Transfer ◽

10.1115/1.3688683 ◽

1964 ◽

Vol 86 (3) ◽

pp. 320-326 ◽

Cited By ~ 1

Author(s):

E. S. Nowak

Keyword(s):

Water Vapor ◽

Equation Of State ◽

Thermodynamic Properties ◽

Specific Heat ◽

Critical Point ◽

Critical Region ◽

Constant Pressure ◽

Parametric Equation ◽

Enthalpy And Entropy ◽

Specific Volumes

A parametric equation of state was derived for water and water vapor in the critical region from experimental P-V-T data. It is valid in that part of the critical region encompassed by pressures from 3000 to 4000 psia, specific volumes from 0.0400 to 0.1100 ft3/lb, and temperatures from 698 to 752 deg F. The equation of state satisfies all of the known conditions at the critical point. It also satisfies the conditions along certain of the boundaries which probably separate “supercritical liquid” from “supercritical vapor.” The equation of state, though quite simple in form, is probably superior to any equation heretofore derived for water and water vapor in the critical region. Specifically, the deviations between the measured and computed values of pressure in the large majority of the cases were within three parts in one thousand. This coincides approximately with the overall uncertainty in P-V-T measurements. In view of these factors, the author recommends that the equation be used to derive values for such thermodynamic properties as specific heat at constant pressure, enthalpy, and entropy in the critical region.

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Self-Organized Criticality on Twitter: Phenomenological Theory and Empirical Investigation Based on Data Analysis Results

Complexity ◽

10.1155/2019/8750643 ◽

2019 ◽

Vol 2019 ◽

pp. 1-16 ◽

Cited By ~ 3

Author(s):

Andrey Dmitriev ◽

Victor Dmitriev ◽

Stepan Balybin

Keyword(s):

Time Series ◽

Critical State ◽

Empirical Investigation ◽

Phenomenological Theory ◽

Self Organization ◽

Protest Movements ◽

Self Organized ◽

Self Organized Criticality ◽

Stochastic Sources ◽

Adiabatic Mode

Recently, there has been an increasing number of empirical evidence supporting the hypothesis that spread of avalanches of microposts on social networks, such as Twitter, is associated with some sociopolitical events. Typical examples of such events are political elections and protest movements. Inspired by this phenomenon, we built a phenomenological model that describes Twitter’s self-organization in a critical state. An external manifestation of this condition is the spread of avalanches of microposts on the network. The model is based on a fractional three-parameter self-organization scheme with stochastic sources. It is shown that the adiabatic mode of self-organization in a critical state is determined by the intensive coordinated action of a relatively small number of network users. To identify the critical states of the network and to verify the model, we have proposed a spectrum of three scaling indicators of the observed time series of microposts.

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