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Being critical of criticality in the brain.

Beggs JM, Timme N - Front Physiol (2012)

Bottom Line: The hypothesis that the electrical activity of neural networks in the brain is critical is potentially important, as many simulations suggest that information processing functions would be optimized at the critical point.This hypothesis, however, is still controversial.Points and counter points are presented in dialog form.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics, Indiana University Bloomington, IN, USA.

ABSTRACT
Relatively recent work has reported that networks of neurons can produce avalanches of activity whose sizes follow a power law distribution. This suggests that these networks may be operating near a critical point, poised between a phase where activity rapidly dies out and a phase where activity is amplified over time. The hypothesis that the electrical activity of neural networks in the brain is critical is potentially important, as many simulations suggest that information processing functions would be optimized at the critical point. This hypothesis, however, is still controversial. Here we will explain the concept of criticality and review the substantial objections to the criticality hypothesis raised by skeptics. Points and counter points are presented in dialog form.

No MeSH data available.


Average avalanche shapes for avalanches of three distinct durations (Friedman et al., 2012).
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Figure 8: Average avalanche shapes for avalanches of three distinct durations (Friedman et al., 2012).

Mentions: Critio: Yes, of course. Let me describe what I mean by the avalanche shape. Consider how an avalanche of neural activity might evolve. It could start with one or a few spiking neurons. These could activate others, so the number of active neurons would increase over time. Eventually this would decline to 0, marking the end of the avalanche. If we plotted the average number of active neurons over time, we might get something that looked like an inverted parabola. This is what I mean by the average avalanche shape. Now if the network is at the critical point, then I should be able to take average avalanche shapes from different durations and show that they are all fractal copies of each other. In other words, I should be able to rescale them with the appropriate critical exponents and get them all to lie on top of each other, in what is called a data collapse. [Critio sketches Figure 8.]


Being critical of criticality in the brain.

Beggs JM, Timme N - Front Physiol (2012)

Average avalanche shapes for avalanches of three distinct durations (Friedman et al., 2012).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3369250&req=5

Figure 8: Average avalanche shapes for avalanches of three distinct durations (Friedman et al., 2012).
Mentions: Critio: Yes, of course. Let me describe what I mean by the avalanche shape. Consider how an avalanche of neural activity might evolve. It could start with one or a few spiking neurons. These could activate others, so the number of active neurons would increase over time. Eventually this would decline to 0, marking the end of the avalanche. If we plotted the average number of active neurons over time, we might get something that looked like an inverted parabola. This is what I mean by the average avalanche shape. Now if the network is at the critical point, then I should be able to take average avalanche shapes from different durations and show that they are all fractal copies of each other. In other words, I should be able to rescale them with the appropriate critical exponents and get them all to lie on top of each other, in what is called a data collapse. [Critio sketches Figure 8.]

Bottom Line: The hypothesis that the electrical activity of neural networks in the brain is critical is potentially important, as many simulations suggest that information processing functions would be optimized at the critical point.This hypothesis, however, is still controversial.Points and counter points are presented in dialog form.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics, Indiana University Bloomington, IN, USA.

ABSTRACT
Relatively recent work has reported that networks of neurons can produce avalanches of activity whose sizes follow a power law distribution. This suggests that these networks may be operating near a critical point, poised between a phase where activity rapidly dies out and a phase where activity is amplified over time. The hypothesis that the electrical activity of neural networks in the brain is critical is potentially important, as many simulations suggest that information processing functions would be optimized at the critical point. This hypothesis, however, is still controversial. Here we will explain the concept of criticality and review the substantial objections to the criticality hypothesis raised by skeptics. Points and counter points are presented in dialog form.

No MeSH data available.