Chaotic dynamics of the Hénon map and neuronal input–output: A comparison with neurophysiological data

Abstract

In this study, the Hénon map was analyzed using quantifiers from information theory in order to compare its dynamics to experimental data from brain regions known to exhibit chaotic behavior. The goal was to investigate the potential of the Hénon map as a model for replicating chaotic brain dynamics in the treatment of Parkinson’s and epilepsy patients. The dynamic properties of the Hénon map were compared with data from the subthalamic nucleus, the medial frontal cortex, and a [Formula: see text]-DG model of neuronal input–output with easy numerical implementation to simulate the local behavior of a population. Using information theory tools, Shannon entropy, statistical complexity, and Fisher’s information were analyzed, taking into account the causality of the time series. For this purpose, different windows over the time series were considered. The findings revealed that neither the Hénon map nor the [Formula: see text]-DG model could perfectly replicate the dynamics of the brain regions studied. However, with careful consideration of the parameters, scales, and sampling used, they were able to model some characteristics of neural activity. According to these results, normal neural dynamics in the subthalamic nucleus region may present a more complex spectrum within the complexity–entropy causality plane that cannot be represented by chaotic models alone. The dynamic behavior observed in these systems using these tools is highly dependent on the studied temporal scale. As the size of the sample studied increases, the dynamics of the Hénon map become increasingly different from those of biological and artificial neural systems.

Neural complexity through a nonextensive statistical–mechanical approach of human electroencephalograms

“…The above nonadditive entropies, as well as the nonextensive statistical mechanics grounded on them, have been already used to characterize various aspects of complexity. Various data obtained from EEG, magnetoencephalograms (MEG), electrocardiograms (ECG), and others, have been analyzed in connection to q -statistics 13 – 16 . However, the discussion frequently focuses on qualitative ingredients.…”

Rényi entropy-complexity causality space: a novel neurocomputational tool for detecting scale-free features in EEG/iEEG data

“…This method is based on the construction of a histogram of ordinal patterns. A brief description is given here, as it has been studied extensively, but the reader is referred to the following references for a formal explanation, simple examples, and similar applications (Bandt and Pompe, 2002 ; Jauregui et al, 2018 ; Pessa and Ribeiro, 2021 ; Zanin and Olivares, 2021 ; Amigó and Rosso, 2023 ; Guisande et al, 2023 ).…”