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

Abstract

The involvement of the neocortex in memory processes depends on neuronal plasticity, the ability torestructure inter-neuronal connections, which is essential for learning and long-term memory. Understandingthese mechanisms is crucial for advancing early diagnosis and treatment of cognitive disorders such asParkinson’s, epilepsy, and Alzheimer’s disease.This study explores a neuronal model with expanded populations, using information-theoretic cues to uncoverdynamics underlying plasticity. By employing Bandt-Pompe’s entropy-complexity (H×C) and Fisher entropyinformation(H×F) planes, hidden patterns in neuronal activity are revealed. These methodologies areparticularly suitable for analyzing nonlinear dynamics and causal relationships in time series. In addition, theH´enon map is applied to capture nonlinear behaviors, such as neural firing, highlighting the trade-off betweenstability and unpredictability in neural networks.Our approach integrates local field potential (LFP) and intracranial electroencephalograms (iEEG) datain multiple frequency bands, connecting computational models with experimental evidence. By addressinghigher-order interactions, such as action potential triplets, this work advances the understanding of synapticadjustments and their implications for neuronal complexity and cognitive disorders.