49 research outputs found
Avalanche criticality in individuals, fluid intelligence, and working memory
The critical brain hypothesis suggests that efficient neural computation can be achieved through critical brain dynamics. However, the relationship between human cognitive performance and scaleāfree brain dynamics remains unclear. In this study, we investigated the wholeābrain avalanche activity and its individual variability in the human restingāstate functional magnetic resonance imaging (fMRI) data. We showed that though the groupālevel analysis was inaccurate because of individual variability, the subject wise scaleāfree avalanche activity was significantly associated with maximal synchronization entropy of their brain activity. Meanwhile, the complexity of functional connectivity, as well as structureāfunction coupling, is maximized in subjects with maximal synchronization entropy. We also observed orderādisorder phase transitions in restingāstate brain dynamics and found that there were longer times spent in the subcritical regime. These results imply that largeāscale brain dynamics favor the slightly subcritical regime of phase transition. Finally, we showed evidence that the neural dynamics of human participants with higher fluid intelligence and working memory scores are closer to criticality. We identified brain regions whose critical dynamics showed significant positive correlations with fluid intelligence performance and found that these regions were located in the prefrontal cortex and inferior parietal cortex, which were believed to be important nodes of brain networks underlying human intelligence. Our results reveal the possible role that avalanche criticality plays in cognitive performance and provide a simple method to identify the critical point and map cortical states on a spectrum of neural dynamics, ranging from subcriticality to supercriticality
Transient dynamics for sequence processing neural networks: effect of degree distributions
We derive a analytic evolution equation for overlap parameters including the
effect of degree distribution on the transient dynamics of sequence processing
neural networks. In the special case of globally coupled networks, the
precisely retrieved critical loading ratio is obtained,
where is the network size. In the presence of random networks, our
theoretical predictions agree quantitatively with the numerical experiments for
delta, binomial, and power-law degree distributions.Comment: 11 pages, 6 figure
Emergence of synchronization induced by the interplay between two prisoner's dilemma games with volunteering in small-world networks
We studied synchronization between prisoner's dilemma games with voluntary
participation in two Newman-Watts small-world networks. It was found that there
are three kinds of synchronization: partial phase synchronization, total phase
synchronization and complete synchronization, for varied coupling factors.
Besides, two games can reach complete synchronization for the large enough
coupling factor. We also discussed the effect of coupling factor on the
amplitude of oscillation of density.Comment: 6 pages, 4 figure
Detection of subthreshold pulses in neurons with channel noise
Neurons are subject to various kinds of noise. In addition to synaptic noise,
the stochastic opening and closing of ion channels represents an intrinsic
source of noise that affects the signal processing properties of the neuron. In
this paper, we studied the response of a stochastic Hodgkin-Huxley neuron to
transient input subthreshold pulses. It was found that the average response
time decreases but variance increases as the amplitude of channel noise
increases. In the case of single pulse detection, we show that channel noise
enables one neuron to detect the subthreshold signals and an optimal membrane
area (or channel noise intensity) exists for a single neuron to achieve optimal
performance. However, the detection ability of a single neuron is limited by
large errors. Here, we test a simple neuronal network that can enhance the
pulse detecting abilities of neurons and find dozens of neurons can perfectly
detect subthreshold pulses. The phenomenon of intrinsic stochastic resonance is
also found both at the level of single neurons and at the level of networks. At
the network level, the detection ability of networks can be optimized for the
number of neurons comprising the network.Comment: 14 pages, 9 figure