2190-8567-3-5.pdf (8.55 MB)
Identification of criticality in neuronal avalanches: I. A theoretical investigation of the non-driven case
journal contribution
posted on 2023-06-08, 14:45 authored by Timothy J Taylor, Caroline Hartley, Péter L Simon, Istvan Kiss, Luc BerthouzeLuc BerthouzeIn this paper, we study a simple model of a purely excitatory neural network that, by construction, operates at a critical point. This model allows us to consider various markers of criticality and illustrate how they should perform in a finite-size system. By calculating the exact distribution of avalanche sizes, we are able to show that, over a limited range of avalanche sizes which we precisely identify, the distribution has scale free properties but is not a power law. This suggests that it would be inappropriate to dismiss a system as not being critical purely based on an inability to rigorously fit a power law distribution as has been recently advocated. In assessing whether a system, especially a finite-size one, is critical it is thus important to consider other possible markers. We illustrate one of these by showing the divergence of susceptibility as the critical point of the system is approached. Finally, we provide evidence that power laws may underlie other observables of the system that may be more amenable to robust experimental assessment.
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Publication status
- Published
File Version
- Accepted version
Journal
Journal of Mathematical NeuroscienceISSN
2190-8567Publisher
BioMed CentralExternal DOI
Issue
5Volume
3Page range
1-26Department affiliated with
- Informatics Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2013-04-24First Open Access (FOA) Date
2013-04-24First Compliant Deposit (FCD) Date
2013-04-23Usage metrics
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