91 research outputs found
Comment on ``Critical branching captures activity in living neural networks and maximizes the number of metastable states''
It is shown that, contrary to the claims in a recent letter by Haldeman and
Beggs (PRL, 94, 058101, 2005), the branching ratio in epileptic cortical
cultures is smaller than one. In addition, and also in contrast to claims made
in that paper, the number of metastable states is not significantly different
between cortical cultures in the critical state and cultures made epileptic
using picrotoxin.Comment: Submitted Comment to PR
Decline of long-range temporal correlations in the human brain during sustained wakefulness
Sleep is crucial for daytime functioning, cognitive performance and general
well-being. These aspects of daily life are known to be impaired after extended
wake, yet, the underlying neuronal correlates have been difficult to identify.
Accumulating evidence suggests that normal functioning of the brain is
characterized by long-range temporal correlations (LRTCs) in cortex, which are
supportive for decision-making and working memory tasks.
Here we assess LRTCs in resting state human EEG data during a 40-hour sleep
deprivation experiment by evaluating the decay in autocorrelation and the
scaling exponent of the detrended fluctuation analysis from EEG amplitude
fluctuations. We find with both measures that LRTCs decline as sleep
deprivation progresses. This decline becomes evident when taking changes in
signal power into appropriate consideration.
Our results demonstrate the importance of sleep to maintain LRTCs in the
human brain. In complex networks, LRTCs naturally emerge in the vicinity of a
critical state. The observation of declining LRTCs during wake thus provides
additional support for our hypothesis that sleep reorganizes cortical networks
towards critical dynamics for optimal functioning
Universal Organization of Resting Brain Activity at the Thermodynamic Critical Point
Thermodynamic criticality describes emergent phenomena in a wide variety of
complex systems. In the mammalian brain, the complex dynamics that
spontaneously emerge from neuronal interactions have been characterized as
neuronal avalanches, a form of critical branching dynamics. Here, we show that
neuronal avalanches also reflect that the brain dynamics are organized close to
a thermodynamic critical point. We recorded spontaneous cortical activity in
monkeys and humans at rest using high-density intracranial microelectrode
arrays and magnetoencephalography, respectively. By numerically changing a
control parameter equivalent to thermodynamic temperature, we observed typical
critical behavior in cortical activities near the actual physiological
condition, including the phase transition of an order parameter, as well as the
divergence of susceptibility and specific heat. Finite-size scaling of these
quantities allowed us to derive robust critical exponents highly consistent
across monkey and humans that uncover a distinct, yet universal organization of
brain dynamics
The interplay between long- and short-range temporal correlations shapes cortex dynamics across vigilance states
Increasing evidence suggests that cortical dynamics during wake exhibits
long-range temporal correlations suitable to integrate inputs over extended
periods of time to increase the signal-to-noise ratio in decision-making and
working memory tasks. Accordingly, sleep has been suggested as a state
characterized by a breakdown of long-range correlations; detailed measurements
of neuronal timescales that support this view, however, have so far been
lacking. Here we show that the long timescales measured at the individual
neuron level in freely-behaving rats during the awake state are abrogated
during non-REM (NREM) sleep. We provide evidence for the existence of two
distinct states in terms of timescale dynamics in cortex: one which is
characterized by long timescales which dominate during wake and REM sleep, and
a second one characterized by the absence of long-range temporal correlations
which characterizes NREM sleep. We observe that both timescale regimes can
co-exist and, in combination, lead to an apparent gradual decline of long
timescales during extended wake which is restored after sleep. Our results
provide a missing link between the observed long timescales in individual
neuron fluctuations during wake and the reported absence of long-term
correlations during deep sleep in EEG and fMRI studies. They furthermore
suggest a network-level function of sleep, to reorganize cortical networks
towards states governed by slow cortex dynamics to ensure optimal function for
the time awake
Powerlaw: a Python package for analysis of heavy-tailed distributions
Power laws are theoretically interesting probability distributions that are
also frequently used to describe empirical data. In recent years effective
statistical methods for fitting power laws have been developed, but appropriate
use of these techniques requires significant programming and statistical
insight. In order to greatly decrease the barriers to using good statistical
methods for fitting power law distributions, we developed the powerlaw Python
package. This software package provides easy commands for basic fitting and
statistical analysis of distributions. Notably, it also seeks to support a
variety of user needs by being exhaustive in the options available to the user.
The source code is publicly available and easily extensible.Comment: 18 pages, 6 figures, code and supporting information at
https://github.com/jeffalstott/powerlaw and
https://pypi.python.org/pypi/powerla
Scale-Free Dynamics in Animal Groups and Brain Networks
Collective phenomena fascinate by the emergence of order in systems composed of a myriad of small entities. They are ubiquitous in nature and can be found over a vast range of scales in physical and biological systems. Their key feature is the seemingly effortless emergence of adaptive collective behavior that cannot be trivially explained by the properties of the system´s individual components. This perspective focuses on recent insights into the similarities of correlations for two apparently disparate phenomena: flocking in animal groups and neuronal ensemble activity in the brain. We first will summarize findings on the spontaneous organization in bird flocks and macro-scale human brain activity utilizing correlation functions and insights from critical dynamics. We then will discuss recent experimental findings that apply these approaches to the collective response of neurons to visual and motor processing, i.e., to local perturbations of neuronal networks at the meso- and microscale. We show how scale-free correlation functions capture the collective organization of neuronal avalanches in evoked neuronal populations in nonhuman primates and between neurons during visual processing in rodents. These experimental findings suggest that the coherent collective neural activity observed at scales much larger than the length of the direct neuronal interactions is demonstrative of a phase transition and we discuss the experimental support for either discontinuous or continuous phase transitions. We conclude that at or near a phase-transition neuronal information can propagate in the brain with similar efficiency as proposed to occur in the collective adaptive response observed in some animal groups.Fil: Ribeiro, Tiago L.. National Institute Of Mental Health; Estados UnidosFil: Chialvo, Dante Renato. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Instituto de Ciencias FĂsicas. - Universidad Nacional de San MartĂn. Instituto de Ciencias FĂsicas; Argentina. Center for Complex Systems & Brain Sciences; ArgentinaFil: Plenz, Dietmar. National Institute Of Mental Health; Estados Unido
Neutral theory and scale-free neural dynamics
Avalanches of electrochemical activity in brain networks have been
empirically reported to obey scale-invariant behavior --characterized by
power-law distributions up to some upper cut-off-- both in vitro and in vivo.
Elucidating whether such scaling laws stem from the underlying neural dynamics
operating at the edge of a phase transition is a fascinating possibility, as
systems poised at criticality have been argued to exhibit a number of important
functional advantages. Here we employ a well-known model for neural dynamics
with synaptic plasticity, to elucidate an alternative scenario in which
neuronal avalanches can coexist, overlapping in time, but still remaining
scale-free. Remarkably their scale-invariance does not stem from underlying
criticality nor self-organization at the edge of a continuous phase transition.
Instead, it emerges from the fact that perturbations to the system exhibit a
neutral drift --guided by demographic fluctuations-- with respect to endogenous
spontaneous activity. Such a neutral dynamics --similar to the one in neutral
theories of population genetics-- implies marginal propagation of activity,
characterized by power-law distributed causal avalanches. Importantly, our
results underline the importance of considering causal information --on which
neuron triggers the firing of which-- to properly estimate the statistics of
avalanches of neural activity. We discuss the implications of these findings
both in modeling and to elucidate experimental observations, as well as its
possible consequences for actual neural dynamics and information processing in
actual neural networks.Comment: Main text: 8 pages, 3 figures. Supplementary information: 5 pages, 4
figure
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