Emergence of power laws in noncritical neuronal systems

Faqeeh, Ali; Osat, Saeed; Radicchi, Filippo; Gleeson, James P.

doi:10.1103/physreve.100.010401

Cited by 13 publications

(14 citation statements)

References 62 publications

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“…Moreover, such a heterogeneous structure imparts a functional advantage in that the criticality is robust against varying external inputs. These results substantially extend the existing understanding based on homogeneous systems [4,[27][28][29][30][31][32][33][34][35][36][37][38][39][40].…”

supporting

confidence: 77%

Reconciliation of theoretical and empirical brain criticality via network heterogeneity

Wü

2021

Preprint

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Inspired by heterogeneity in biological neural networks, we explore a heterogeneous network consisting of receipt, transmission and computation layers. It reconciles the dilemma that the data analysis scheme for empirical records yields non-power laws when applied to microscopic simulation of critical neural dynamics. Detailed analysis shows that the reconciliation is due to synchronization effect of the feedforward connectivity. The network favours avalanches with denser activity in the first half of life, and the result is consistent with the experimental observation. This heterogeneous structure facilitates robust criticality against external stimuli, which implies the inappropriateness of interpreting the subcritcality signature as an indication of subcrtical dynamics. These results propose the network heterogeneity as an essential piece for understanding the brain criticality.

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supporting

confidence: 77%

Reconciliation of theoretical and empirical brain criticality via network heterogeneity

Wü

2021

Preprint

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“…On the one hand, many subsequent works showed that the presence of power-law avalanches is not a sufficient condition for criticality, as they might emerge from different mechanisms 16 – 20 . A perhaps stronger test for criticality is whether the avalanche exponents satisfy the crackling-noise relation, a relation that connects the avalanche exponents to the scaling of the average avalanche size with its duration T 21 – 23 , defined by the exponent .…”

Section: Introductionmentioning

confidence: 99%

Disentangling the critical signatures of neural activity

Mariani

Nicoletti

Bisio

et al. 2022

Sci Rep

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The critical brain hypothesis has emerged as an attractive framework to understand neuronal activity, but it is still widely debated. In this work, we analyze data from a multi-electrodes array in the rat’s cortex and we find that power-law neuronal avalanches satisfying the crackling-noise relation coexist with spatial correlations that display typical features of critical systems. In order to shed a light on the underlying mechanisms at the origin of these signatures of criticality, we introduce a paradigmatic framework with a common stochastic modulation and pairwise linear interactions inferred from our data. We show that in such models power-law avalanches that satisfy the crackling-noise relation emerge as a consequence of the extrinsic modulation, whereas scale-free correlations are solely determined by internal interactions. Moreover, this disentangling is fully captured by the mutual information in the system. Finally, we show that analogous power-law avalanches are found in more realistic models of neural activity as well, suggesting that extrinsic modulation might be a broad mechanism for their generation.

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“…Our main focus is on models that exhibit scale-free dynamics as measured by avalanche size distributions. Such models are usually referred to as critical, although the presence of power laws in avalanches properties is not a sufficient condition for the dynamics to be critical [45][46][47][48].…”

Section: Introductionmentioning

confidence: 99%

Self-Organization Toward Criticality by Synaptic Plasticity

2021

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Self-organized criticality has been proposed to be a universal mechanism for the emergence of scale-free dynamics in many complex systems, and possibly in the brain. While such scale-free patterns were identified experimentally in many different types of neural recordings, the biological principles behind their emergence remained unknown. Utilizing different network models and motivated by experimental observations, synaptic plasticity was proposed as a possible mechanism to self-organize brain dynamics toward a critical point. In this review, we discuss how various biologically plausible plasticity rules operating across multiple timescales are implemented in the models and how they alter the network’s dynamical state through modification of number and strength of the connections between the neurons. Some of these rules help to stabilize criticality, some need additional mechanisms to prevent divergence from the critical state. We propose that rules that are capable of bringing the network to criticality can be classified by how long the near-critical dynamics persists after their disabling. Finally, we discuss the role of self-organization and criticality in computation. Overall, the concept of criticality helps to shed light on brain function and self-organization, yet the overall dynamics of living neural networks seem to harnesses not only criticality for computation, but also deviations thereof.

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Emergence of power laws in noncritical neuronal systems

Cited by 13 publications

References 62 publications

Reconciliation of theoretical and empirical brain criticality via network heterogeneity

Reconciliation of theoretical and empirical brain criticality via network heterogeneity

Disentangling the critical signatures of neural activity

Self-Organization Toward Criticality by Synaptic Plasticity

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