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Lombardi F, Herrmann HJ, de Arcangelis L. Balance of excitation and inhibition determines 1/f power spectrum in neuronal networks. Chaos. 2017;27(4):047402doi: 10.1063/1.4979043.
Lombardi, F., Herrmann, H. J., & de Arcangelis, L. (2017). Balance of excitation and inhibition determines 1/f power spectrum in neuronal networks. Chaos (Woodbury, N.Y.), 27(4), 047402. https://doi.org/10.1063/1.4979043
Lombardi, F, et al. "Balance of excitation and inhibition determines 1/f power spectrum in neuronal networks." Chaos (Woodbury, N.Y.) vol. 27,4 (2017): 047402. doi: https://doi.org/10.1063/1.4979043
Lombardi F, Herrmann HJ, de Arcangelis L. Balance of excitation and inhibition determines 1/f power spectrum in neuronal networks. Chaos. 2017 Apr;27(4):047402. doi: 10.1063/1.4979043. PMID: 28456161.
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Chaos. 2017 Apr;27(4):047402. doi: 10.1063/1.4979043.

Balance of excitation and inhibition determines 1/f power spectrum in neuronal networks.

Chaos (Woodbury, N.Y.)

F Lombardi, H J Herrmann, L de Arcangelis

Affiliations

  1. Institute of Computational Physics for Engineering Materials, ETH, Zurich, Switzerland.
  2. Department of Industrial and Information Engineering, University of Campania "Luigi Vanvitelli," INFN sez. Naples, Gr. Coll. Salerno, Aversa(CE), Italy.

PMID: 28456161 DOI: 10.1063/1.4979043

Abstract

The 1/f-like decay observed in the power spectrum of electro-physiological signals, along with scale-free statistics of the so-called neuronal avalanches, constitutes evidence of criticality in neuronal systems. Recent in vitro studies have shown that avalanche dynamics at criticality corresponds to some specific balance of excitation and inhibition, thus suggesting that this is a basic feature of the critical state of neuronal networks. In particular, a lack of inhibition significantly alters the temporal structure of the spontaneous avalanche activity and leads to an anomalous abundance of large avalanches. Here, we study the relationship between network inhibition and the scaling exponent β of the power spectral density (PSD) of avalanche activity in a neuronal network model inspired in Self-Organized Criticality. We find that this scaling exponent depends on the percentage of inhibitory synapses and tends to the value β = 1 for a percentage of about 30%. More specifically, β is close to 2, namely, Brownian noise, for purely excitatory networks and decreases towards values in the interval [1, 1.4] as the percentage of inhibitory synapses ranges between 20% and 30%, in agreement with experimental findings. These results indicate that the level of inhibition affects the frequency spectrum of resting brain activity and suggest the analysis of the PSD scaling behavior as a possible tool to study pathological conditions.

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