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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.


Avalanche size distributions in local field potential data collected with a 60-channel microelectrode array from rat cortical slice networks. (A) Subcritical regime; excitatory antagonist (3 mM CNQX) applied. (B) Critical regime; normal network. (C) Supercritical regime; inhibitory antagonist (2 mM PTX) applied (Haldeman and Beggs, 2005).
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Figure 7: Avalanche size distributions in local field potential data collected with a 60-channel microelectrode array from rat cortical slice networks. (A) Subcritical regime; excitatory antagonist (3 mM CNQX) applied. (B) Critical regime; normal network. (C) Supercritical regime; inhibitory antagonist (2 mM PTX) applied (Haldeman and Beggs, 2005).

Mentions: Critio: Actually, yes. By blocking excitatory synaptic transmission, you can dampen network excitability, leading to smaller avalanches (Mazzoni et al., 2007). Here is a figure I saw from a poster at the conference. [Critio pulls out a small copy of the poster and points to Figure 7.]


Being critical of criticality in the brain.

Beggs JM, Timme N - Front Physiol (2012)

Avalanche size distributions in local field potential data collected with a 60-channel microelectrode array from rat cortical slice networks. (A) Subcritical regime; excitatory antagonist (3 mM CNQX) applied. (B) Critical regime; normal network. (C) Supercritical regime; inhibitory antagonist (2 mM PTX) applied (Haldeman and Beggs, 2005).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 7: Avalanche size distributions in local field potential data collected with a 60-channel microelectrode array from rat cortical slice networks. (A) Subcritical regime; excitatory antagonist (3 mM CNQX) applied. (B) Critical regime; normal network. (C) Supercritical regime; inhibitory antagonist (2 mM PTX) applied (Haldeman and Beggs, 2005).
Mentions: Critio: Actually, yes. By blocking excitatory synaptic transmission, you can dampen network excitability, leading to smaller avalanches (Mazzoni et al., 2007). Here is a figure I saw from a poster at the conference. [Critio pulls out a small copy of the poster and points to Figure 7.]

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.