Gell-Mann–Low Criticality in Neural Networks

Tiberi, Lorenzo; Stapmanns, Jonas; Kühn, Tobias; Luu, Thomas; Dahmen, David; Helias, Moritz

doi:10.1103/physrevlett.128.168301

Cited by 16 publications

(25 citation statements)

References 49 publications

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“…The observation is rather striking given that these exponent values come from a linear model of randomly connected neurons, while real data are, most likely, generated by a more complex non-linear dynamics on heterogeneous networks. A recent work on universal aspects of brain dynamics [56] might help shedding light onto this result.…”

Section: Resultsmentioning

confidence: 98%

Quasi-universal scaling in mouse-brain neuronal activity stems from edge-of-instability critical dynamics

Morales¹,

Santo

Muñóz³

2021

Preprint

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The brain is in a state of perpetual reverberant neural activity, even in the absence of specific tasks or stimuli. Shedding light on the origin and functional significance of such activity is essential to understanding how the brain transmits, processes, and stores information. An inspiring, albeit controversial, conjecture proposes that some statistical characteristics of empirically observed neuronal activity can be understood by assuming that brain networks operate in a dynamical regime near the edge of a phase transition. Moreover, the resulting critical behavior, with its concomitant scale invariance, is assumed to carry crucial functional advantages. Here, we present a data-driven analysis based on simultaneous high-throughput recordings of the activity of thousands of individual neurons in various regions of the mouse brain. To analyze these data, we construct a unified theoretical framework that synergistically combines cutting-edge methods for the study of brain activity (such as a phenomenological renormalization group approach and techniques that infer the general dynamical state of a neural population), while designing complementary tools. This unified approach allows us to uncover strong signatures of scale invariance that is “quasi-universal” across brain regions and reveal that these areas operate, to a greater or lesser extent, at the edge of instability. Furthermore, this framework allows us to distinguish between quasi-universal background activity and non-universal input-related activity. Taken together, the following study provides strong evidence that brain networks actually operate in a critical regime which, among other functional advantages, provides them with a scale-invariant substrate of activity in which optimal input representations can be sustained.

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Section: Resultsmentioning

confidence: 98%

Quasi-universal scaling in mouse-brain neuronal activity stems from edge-of-instability critical dynamics

Morales¹,

Santo

Muñóz³

2021

Preprint

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“…where we have absorbed the threshold ϕ into the constant λ s e − ϕ β → λ s and the parameters I s , I d , R, and D have been scaled by 1 β , making them dimensionless variables. Similarly, using equations ( 3), ( 5), (6), and ( 7), we define the conditional somatic hazard rate to be,…”

Section: Methodsmentioning

confidence: 99%

“…Understanding computation within neuronal networks relies crucially on grasping how the inputs to a neuron affect its output [1][2][3][4][5][6]. The input-output function is assumed to produce i) firing-frequency increasing monotonically with input intensity and ii) concomitantly decreasing interspike interval dispersion as measured through the coefficient of variation (CV) [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Dendritic excitability controls overdispersion

Friedenberger

Naud

2022

Preprint

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A neuron's input-output function is commonly understood in terms of the fluctuation-driven and mean-driven regimes. Here we investigate how active dendrites falling in either regime shape the input-output function and find that dendritic input primarily controls interspike interval dispersion, a prediction that is validated with dual patch clamp recordings. This phenomenon can be understood by considering that neurons display not two, but three fundamental operating regimes, depending on whether dendritic spikes or the somatic input reach threshold.

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“…At the same time, the neuron networks are stable in storing information, but also the sensibility that allows sending signals to distant parts. Using methods taken from quantum field theory, M. Helias and his team confirmed the existence of critical points in the classical model of brain dynamics (L. Tiberi et al, 2022). In short, using the renormalization technique the researchers found that both nearby and distant neurons can effectively communicate with each other.…”

Section: Introductionmentioning

confidence: 95%

The Importance of Quantum Information in the Stock Market and Financial Decision Making in Conditions of Radical Uncertainty

Njegovanović¹

2023

IJSSS

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The Universe is a coin that’s already been flipped, heads or tails predetermined: all we’re doing is uncovering it the ‘paradox’ is only a conflict between reality and your feeling of what reality ‘ought to be’.Richard FeynmanThe aim of the research takes place through two parallel directions. The first is gaining an understanding of the applicability of quantum mechanics/quantum physics to human decision-making processes in the stock market with quantum information as a decision-making lever, and the second direction is neuroscience and artificial intelligence using postulates analogous to the postulates of quantum mechanics and radical uncertainty in conditions of radical uncertainty.The world of radical uncertainty (radical uncertainty is based on the knowledge of quantum mechanics from the claim that there is no causal certainty). it is everywhere in our world. "Radical uncertainty is characterized by vagueness, ignorance, indeterminacy, ambiguity and lack of information. He prefers to create 'mysteries' rather than 'puzzles' with defined solutions. Mysteries are ill-defined problems in which action is required, but the future is uncertain, the consequences unpredictable, and disagreement inevitable. "How should we make decisions in these circumstances?" (J. Kay and M. King, 2020), while "uncertainty and ambiguity are at the very core of the stock market. "Narratives are the currency of uncertainty" (N. Mangee, 2022).

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Gell-Mann–Low Criticality in Neural Networks

Cited by 16 publications

References 49 publications

Quasi-universal scaling in mouse-brain neuronal activity stems from edge-of-instability critical dynamics

Quasi-universal scaling in mouse-brain neuronal activity stems from edge-of-instability critical dynamics

Dendritic excitability controls overdispersion

The Importance of Quantum Information in the Stock Market and Financial Decision Making in Conditions of Radical Uncertainty

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