Exploring the role of synaptic plasticity in the frequency-dependent complexity domain.

The involvement of the neocortex in memory processes depends on neuronal plasticity, the ability to restructure inter-neuronal connections, which is essential for learning and long-term memory. Understanding these mechanisms is crucial for advancing early diagnosis and treatment of cognitive disorders such as Parkinson's, epilepsy, and Alzheimer's disease. This study explores a neuronal model with expanded populations, using information-theoretic cues to uncover dynamics underlying plasticity. By employing Bandt-Pompe's entropy-complexity (H×C) and Fisher entropy-information (H×F) planes, hidden patterns in neuronal activity are revealed. These methodologies are particularly suitable for analyzing nonlinear dynamics and causal relationships in time series. In addition, the Hénon map is applied to capture nonlinear behaviors, such as neural firing, highlighting the trade-off between stability and unpredictability in neural networks. Our approach integrates local field potential and intracranial electroencephalograms' data in multiple frequency bands, connecting computational models with experimental evidence. By addressing higher-order interactions, such as action potential triplets, this work advances the understanding of synaptic adjustments and their implications for neuronal complexity and cognitive disorders.