Hybrid Epidemic--Neuronal Dynamics: A SEIR--FitzHugh--Nagumo Model for Information Flow in Complex Neural Networks

Abstract

Information transfer in neural systems is often modeled through diffusive or synaptic mechanisms that fail to capture the contagion-like propagation of activation across large-scale networks. In this study, we introduce a hybrid SEIR-FitzHugh- Nagumo (FHN) model that integrates epidemiological dynamics with neuronal excitability to describe the flow of information through complex brain-like networks. Each node follows FHN excitability with slow recovery, while inter-node coupling obeys a modified SEIR process that regulates transmission probability based on exposure and recovery. This hybridization allows for the coexistence of oscillatory neural states and infection-like spreading modes, representing fast spiking communication constrained by population-level fatigue. We simulate the hybrid model across ring, Erd[o]s-Renyi, and Barabasi-Albert topologies and benchmark it against conventional diffusive FHN and FHN with synaptic depression (STD). Information-theoretic analysis using Mutual Information (MI) and Transfer Entropy (TE) shows that the hybrid system sustains higher directional information flow (TE-AUROC{approx} 0.52-0.54) and reduced latency across topologies. These results suggest that infection-inspired coupling enhances causal coherence and efficiency of information propagation in neural networks. The findings open a path toward multiscale hybrid models unifying epidemic, neuronal, and information-theoretic frameworks for understanding complex brain dynamics.