The Correlation of the Neuronal Long‐Range Temporal Correlations, Avalanche Dynamics with the Behavioral Scaling Laws and Interindividual Variability

Palva, Satu

doi:10.1002/9783527651009.ch5

Cited by 2 publications

(1 citation statement)

References 107 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Neuronal avalanche dynamics in the brain varies in correlation with power-law dynamics in behavioral fractal scaling among individuals on each resting or task-performing behavioral state 27 . Based on these observations, it has been proposed that brain neuronal avalanche dynamics underlies the power-law distribution in behavioral fractal kinetics 1 , 4 , 30 . Moreover, the power law dynamics of neuronal avalanche has been reproduced by various theoretical neural network (NN) models 26 , including: stochastic NN models based on second-order phase transition (referred to as “criticality”) 31 , a deterministic model based on a feed forward-type NN model 32 , and another deterministic model at the “edge of chaos” 33 , indicating that power law neural avalanche dynamics is due to the collective dynamics of neuron in the brain.…”

Section: Discussionmentioning

confidence: 99%

Insulin signaling shapes fractal scaling of C. elegans behavior

Arata

Shiga

Ikeda

et al. 2022

Sci Rep

View full text Add to dashboard Cite

Fractal scaling in animal behavioral activity, where similar temporal patterns appear repeatedly over a series of magnifications among time scales, governs the complex behavior of various animal species and, in humans, can be altered by neurodegenerative diseases and aging. However, the mechanism underlying fractal scaling remains unknown. Here, we cultured C. elegans in a microfluidic device for 3 days and analyzed temporal patterns of C. elegans activity by fractal analyses. The residence-time distribution of C. elegans behaviors shared a common feature with those of human and mice. Specifically, the residence-time power-law distribution of the active state changed to an exponential-like decline at a longer time scale, whereas the inactive state followed a power-law distribution. An exponential-like decline appeared with nutrient supply in wild-type animals, whereas this decline disappeared in insulin-signaling-defective daf-2 and daf-16 mutants. The absolute value of the power-law exponent of the inactive state distribution increased with nutrient supply in wild-type animals, whereas the value decreased in daf-2 and daf-16 mutants. We conclude that insulin signaling differentially affects mechanisms that determine the residence time in active and inactive states in C. elegans behavior. In humans, diabetes mellitus, which is caused by defects in insulin signaling, is associated with mood disorders that affect daily behavioral activities. We hypothesize that comorbid behavioral defects in patients with diabetes may be attributed to altered fractal scaling of human behavior.

show abstract

Section: Discussionmentioning

confidence: 99%

Insulin signaling shapes fractal scaling of C. elegans behavior

Arata

Shiga

Ikeda

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

How neural network structure alters the brain’s self-organized criticality

Sugimoto,

Yadohisa,

Abe

2024

Preprint

View full text Add to dashboard Cite

In recent years, the "brain critical hypothesis" has been proposed in the fields of complex systems science and statistical physics, suggesting that the brain acquires functions such as information processing capabilities near the critical point, which lies at the boundary between disorder and order. As a mechanism for maintaining this critical state, a feedback system called "self-organized criticality (SOC)" has been proposed, where parameters related to brain function, such as synaptic plasticity, are maintained by internal rules without external adjustments. Additionally, the structure of neural networks plays an important role in information processing, with healthy neural networks being characterized by properties such as small-worldness, scale-freeness, and modularity. However, it has also been pointed out that these properties may be lacking in cases of neurological disorders. In this study, we used a mathematical model to investigate the possibility that differences in neural network structures could lead to brain dysfunction through SOC. As a result, it became clear that the synaptic plasticity conditions that maximize information processing capabilities vary depending on the network structure. Notably, when the network possesses only a scale-free structure, a phenomenon known as the Dragon king -associated with abnormal neural activity - was observed. These findings suggest that the maintenance of neural dynamics equilibrium differs depending on the structural characteristics of the neural network, and that in structures with hub nodes, such as scale-free networks, imbalances in neural dynamics may occur, potentially negatively impacting brain function.

show abstract

The Correlation of the Neuronal Long‐Range Temporal Correlations, Avalanche Dynamics with the Behavioral Scaling Laws and Interindividual Variability

Cited by 2 publications

References 107 publications

Insulin signaling shapes fractal scaling of C. elegans behavior

Insulin signaling shapes fractal scaling of C. elegans behavior

How neural network structure alters the brain’s self-organized criticality

Contact Info

Product

Resources

About