2018/01/01 by Ariadne de Andrade Costa, Mary Jean Amon, Olaf Sporns +2
Biochemistry, Genetics and Molecular Biology · Economics, Econometrics and Finance · Mathematics · Neuroscience · Physics and Astronomy · #Complex Systems and Time Series Analysis #Computer science #Criticality #Fractal #Geology #Geometry #Mathematical analysis #Mathematics #Multifractal system #Neural dynamics and brain function #Physics #Scaling #Self-organized criticality #Series (stratigraphy) #Statistical physics #Statistics #Stochastic process #q-bio.NC #stochastic dynamics and bifurcation
paper · pdf · doi:10.1007/978-3-319-73198-8_14
11 pages, 3 subfigures divided into 2 figures
openalex publication_date 2018/01/01 · arxiv created 2018/01/19 · arxiv updated 2018/02/21 · openalex created_date 2020/11/23 · openalex updated_date 2026/08/05
Although there is increasing evidence of criticality in the brain, the processes that guide neuronal networks to reach or maintain criticality remain unclear. The present research examines the role of neuronal gain plasticity in time-series of simulated neuronal networks composed of integrate-and-fire stochastic spiking neurons, and the utility of fractal methods in assessing network criticality. Simulated time-series were derived from a network model of fully connected discrete-time stochastic excitable neurons. Monofractal and multifractal analyses were applied to neuronal gain time-series. Fractal scaling was greatest in networks with a mid-range of neuronal plasticity, versus extremely high or low levels of plasticity. Peak fractal scaling corresponded closely to additional indices of criticality, including average branching ratio. Networks exhibited multifractal structure, or multiple scaling relationships. Multifractal spectra around peak criticality exhibited elongated right tails, suggesting that the fractal structure is relatively insensitive to high-amplitude local fluctuations. Networks near critical states exhibited mid-range multifractal spectra width and tail length, which is consistent with literature suggesting that networks poised at quasi-critical states must be stable enough to maintain organization but unstable enough to be adaptable. Lastly, fractal analyses may offer additional information about critical state dynamics of networks by indicating scales of influence as networks approach critical states.