Scale-free avalanche dynamics possibly generated by randomly jumping among many stable states

Area of Science:

• Computational Neuroscience
• Statistical Physics

Background:

• Scale-free statistics of neuronal avalanches are widely considered a hallmark of critical brain dynamics.
• Previous research has questioned the exclusive link between criticality and scale-free neuronal avalanching.

Purpose of the Study:

• To investigate alternative mechanisms generating scale-free dynamics in neural networks.
• To demonstrate that non-critical systems can exhibit scale-free neuronal avalanche statistics.

Main Methods:

• Analysis of a fully connected stochastic neural network model.
• Examination of system dynamics through state transitions.

Main Results:

• The stochastic neural network model generated scale-free dynamics.
• These dynamics arose from the network jumping between multiple stable states, independent of criticality.

Conclusions:

• Scale-free neuronal avalanche statistics do not exclusively indicate critical brain states.
• The findings challenge the interpretation of neuronal avalanches as a sole signature of criticality.