Spontaneous activity of neural networks depends on their stage of development. Computational performances of a network increase when the maturation leads to a self-organized criticality. Thus, an increasing complexity in the behavior of the network is expected when it enters in this developmental stage, called critical state. We tested this hypothesis investigating with a Micro-Electrodes Array of 60 electrodes a neuronal culture that during maturation exhibited first a subcritical and then a critical state. We found that in the critical state the local complexity (measured in terms of Approximate Entropy) was larger than in subcritical conditions (mathbf{mean}pm mathbf{std}, ApEn about mathbf{1.03}+mathbf{0.10},mathbf{0.77}+mathbf{0.18} in critical and sub-critical states, respectively; differences statistically significant), but only if the embedding dimension is at least 3 and the tolerance is fixed (we considered it equal to 1 ms, which is close to the characteristic time of neural communications).

Approximate Entropy of Spiking Series of a Neuronal Network in Either Subcritical or Critical State

Ermini L.
First
;
2018-01-01

Abstract

Spontaneous activity of neural networks depends on their stage of development. Computational performances of a network increase when the maturation leads to a self-organized criticality. Thus, an increasing complexity in the behavior of the network is expected when it enters in this developmental stage, called critical state. We tested this hypothesis investigating with a Micro-Electrodes Array of 60 electrodes a neuronal culture that during maturation exhibited first a subcritical and then a critical state. We found that in the critical state the local complexity (measured in terms of Approximate Entropy) was larger than in subcritical conditions (mathbf{mean}pm mathbf{std}, ApEn about mathbf{1.03}+mathbf{0.10},mathbf{0.77}+mathbf{0.18} in critical and sub-critical states, respectively; differences statistically significant), but only if the embedding dimension is at least 3 and the tolerance is fixed (we considered it equal to 1 ms, which is close to the characteristic time of neural communications).
2018
2018 IEEE Workshop on Complexity in Engineering, COMPENG 2018
Firenze
2018
IEEE WORKSHOP ON COMPLEXITY IN ENGINEERING
IEEE
1
4
http://www.scopus.com/inward/record.url?eid=2-s2.0-85059025504&partnerID=MN8TOARS
approximate entropy; complexity; in vitro; microelectrode arrays; Neuronal network dynamics
Ermini L.; Mesin L.; Massobrio P.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1828904
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