Simon-Shlomo Poil*, Richard Hardstone*, Huibert D. Mansvelder, Klaus Linkenkaer-Hansen
The Journal of Neuroscience, 18 July 2012, 32(29): 9817-9823; doi: 10.1523/JNEUROSCI.5990-11.2012

Criticality has gained widespread interest in neuroscience as an attractive framework for understanding the character and functional implications of variability in brain activity. The metastability of critical systems maximizes their dynamic range, storage capacity, and computational power. Power-law scaling—a hallmark of criticality—has been observed on different levels, e.g., in the distribution of neuronal avalanches in vitro and in vivo, but also in the decay of temporal correlations in behavioral performance and ongoing oscillations in humans. An unresolved issue is whether power-law scaling on different organizational levels in the brain—and possibly in other hierarchically organized systems—can be related. Here, we show that critical-state dynamics of avalanches and oscillations jointly emerge in a neuronal network model when excitation and inhibition is balanced. The oscillatory activity of the model was qualitatively similar to what is typically observed in recordings of human resting-state MEG. We propose that homeostatic plasticity mechanisms tune this balance in healthy brain networks, and that it is essential for critical behavior on multiple levels of neuronal organization with ensuing functional benefits. Based on our network model, we introduce a concept of multi-level criticality in which power-law scaling can emerge on multiple time scales in oscillating networks.

Citations 12:

Smit et al, Long-Range Temporal Correlations in Resting-State Alpha Oscillations Predict Human Timing-Error Dynamics, J Neurosci, 2013, 33(27): 11212-11220; doi: 10.1523/​JNEUROSCI.2816-12.2013

Alegre and Valencia, Oscillatory activity in the human basal ganglia: More than just beta, more than just Parkinson’s disease, Experimental Neurology, 248:183–186 (2013)

Zorick and Mandelkern, (2013) Multifractal Detrended Fluctuation Analysis of Human EEG: Preliminary Investigation and Comparison with the Wavelet Transform Modulus Maxima Technique. PLoS ONE 8(7): e68360. doi:10.1371/journal.pone.0068360

Ruiz et al, Beta-band amplitude oscillations in the human internal globus pallidus support the encoding of sequence boundaries during initial sensorimotor sequence learning, Neuroimage, doi:10.1016/j.neuroimage.2013.05.085

Kello et al, Plasticity, Learning, and Complexity in Spiking Networks, Critical Reviews in Biomedical Engineering, doi:10.1615/CritRevBiomedEng.2013006724

Rangan and Young, Emergent dynamics in a model of visual cortex, J. Computational Neuroscience, doi: 10.1007/s10827-013-0445-9

Palva et al, Neuronal long-range temporal correlations and avalanche dynamics are correlated with behavioral scaling laws, PNAS, 110(9): 3585-3590 (2013) doi: 10.1073/pnas.1216855110

Nakagawa et al, Bottom up modeling of the connectome: Linking structure and function in the resting brain and their changes in aging,NeuroImage,80,318–329 (2013)

Botcharova et al, A power-law distribution of phase-locking intervals does not imply critical interaction, arXiv:1208.2659

Vincent et al., Extracting functionally feedforward networks from a population of spiking neurons, Front. Comput. Neurosci. 6:86.

Hardstone et al, Detrended fluctuation analysis: a scale-free view on neuronal oscillations, Front. Physio. 3:450.

Taylor et al, Identification of criticality in neuronal avalanches: I. A theoretical investigation of the non-driven case, arXiv:1210.8295


Article: Critical-State Dynamics of Avalanches and Oscillations Jointly Emerge from Balanced Excitation/Inhibition in Neuronal Networks