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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.


Probability distribution of neuronal avalanche size. (Black) Size measured using the total number of activated electrodes. (Teal) Size measured using total LFP amplitude measured at all electrodes participating in the avalanche (Beggs and Plenz, 2003).
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Figure 6: Probability distribution of neuronal avalanche size. (Black) Size measured using the total number of activated electrodes. (Teal) Size measured using total LFP amplitude measured at all electrodes participating in the avalanche (Beggs and Plenz, 2003).

Mentions: Critio: Well, there were several early reports that the nervous system could produce power law distributions (Chen et al., 1997; Teich et al., 1997; Linkenkaer-Hansen et al., 2001; Worrell et al., 2002). These data all came from “one-dimensional” measurements, were a single variable, like spike count, temporal correlation, or total energy, was found to follow a power law distribution. While these important findings were very suggestive, they did not immediately provide insight as to what the underlying network was doing to produce these distributions. The earliest data to explore power law distributions at the network level came from recordings from microelectrode arrays that had 60 electrodes. There, the experimenters were able to observe bursts of spontaneous activity. They found that if they counted the number of electrodes activated in each distinct burst, that the burst sizes were distributed according to a power law (Beggs and Plenz, 2003). Because the statistics of these bursts followed the same equations used to describe avalanche sizes in critical systems, they called these events “neuronal avalanches.” I have on my laptop here a figure from one of their papers that shows the power law distribution of avalanche sizes, measured either as the total number of electrodes activated per avalanche, or as the total amplitude of local field potential (LFP) signal measured at all the electrodes involved in the avalanche. [Critio shows Figure 6 to Mnemo.]


Being critical of criticality in the brain.

Beggs JM, Timme N - Front Physiol (2012)

Probability distribution of neuronal avalanche size. (Black) Size measured using the total number of activated electrodes. (Teal) Size measured using total LFP amplitude measured at all electrodes participating in the avalanche (Beggs and Plenz, 2003).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: Probability distribution of neuronal avalanche size. (Black) Size measured using the total number of activated electrodes. (Teal) Size measured using total LFP amplitude measured at all electrodes participating in the avalanche (Beggs and Plenz, 2003).
Mentions: Critio: Well, there were several early reports that the nervous system could produce power law distributions (Chen et al., 1997; Teich et al., 1997; Linkenkaer-Hansen et al., 2001; Worrell et al., 2002). These data all came from “one-dimensional” measurements, were a single variable, like spike count, temporal correlation, or total energy, was found to follow a power law distribution. While these important findings were very suggestive, they did not immediately provide insight as to what the underlying network was doing to produce these distributions. The earliest data to explore power law distributions at the network level came from recordings from microelectrode arrays that had 60 electrodes. There, the experimenters were able to observe bursts of spontaneous activity. They found that if they counted the number of electrodes activated in each distinct burst, that the burst sizes were distributed according to a power law (Beggs and Plenz, 2003). Because the statistics of these bursts followed the same equations used to describe avalanche sizes in critical systems, they called these events “neuronal avalanches.” I have on my laptop here a figure from one of their papers that shows the power law distribution of avalanche sizes, measured either as the total number of electrodes activated per avalanche, or as the total amplitude of local field potential (LFP) signal measured at all the electrodes involved in the avalanche. [Critio shows Figure 6 to Mnemo.]

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.