Self-Organized Criticality in the Brain
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Abstract (arXiv)
Self-organized criticality (SOC) refers to the ability of complex systems to evolve towards a 2nd-order phase transition at which interactions between system components lead to scale-invariant events beneficial for system performance. For the last two decades, considerable experimental evidence accumulated that the mammalian cortex with its diversity in cell types and connections might exhibit SOC. Here we review experimental findings of isolated, layered cortex preparations to self-organize towards four dynamical motifs identified in the cortex in vivo: up-states, oscillations, neuronal avalanches, and coherence potentials. During up-states, the synchronization observed for nested theta/gamma-oscillations embeds scale-invariant neuronal avalanches that exhibit robust power law scaling in size with a slope of -3/2 and a critical branching parameter of 1. This dynamical coordination, tracked in the local field potential (nLFP) and pyramidal neuron activity using 2-photon imaging, emerges autonomously in superficial layers of organotypic cortex cultures and acute cortex slices, is homeostatically regulated, displays separation of time scales, and reveals unique size vs. quiet time dependencies. A threshold operation identifies coherence potentials; avalanches that in addition maintain the precise time course of propagated synchrony. Avalanches emerge under conditions of external driving. Control parameters are established by the balance of excitation and inhibition (E/I) and the neuromodulator dopamine. This rich dynamical repertoire is not observed in dissociated cortex cultures, which lack cortical layers and exhibit dynamics similar to a 1st-order phase transition. The precise interactions between up-states, nested oscillations, avalanches, and coherence potentials in superficial cortical layers provide compelling evidence for SOC in the brain.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Feb 2021.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 2; 2025: 1; 2026: 2.
7 papers.
- 2023Multi-scale Evolutionary Neural Architecture Search for Deep Spiking Neural Networkscited by 3
- 2021A random energy approach to deep learningcited by 2
- 2024Over the Edge of Chaos? Excess Complexity as a Roadblock to Artificial General Intelligencecited by 2
- 2024Absolute abstraction: a renormalisation group approachcited by 0
- 2025Hebbian learning the local structure of languagecited by 0
- 2026PLDR-LLMs Reason At Self-Organized Criticalitycited by 0
- 2026Towards Critical Branching Mechanism in Recurrent Neural Networkscited by 0
- 2026Towards Critical Branching Mechanism in Recurrent Neural Networks9 Jun 2026
- 2026PLDR-LLMs Reason At Self-Organized Criticality12 Mar 2026
- 2025Hebbian learning the local structure of language3 Mar 2025
- 2024Over the Edge of Chaos? Excess Complexity as a Roadblock to Artificial General Intelligence4 Jul 2024
- 2024Absolute abstraction: a renormalisation group approach1 Jul 2024
- 2023Multi-scale Evolutionary Neural Architecture Search for Deep Spiking Neural Networks21 Apr 2023
- 2021A random energy approach to deep learning17 Dec 2021