Generation and interaction dynamics of target waves in a memristive Rulkov neural network

The propagation of wave-like activity is fundamental to neural information processing. Here, we investigate the generation and interaction of target waves in a large-scale network of Rulkov neurons coupled by memristor-based synapses. This study demonstrates that regular rhythmic firing in pacemakers facilitates stable target waves, whereas chaotic modes fail. Crucially, the memristor-based synapse’s bounded, history-dependent dynamics promote sustained propagation via active signal regeneration; in contrast, static synapses cause wave attenuation, and linear synapses lead to unbounded network divergence. Wave interactions follow a frequency-dependent dominance rule: higher-frequency pacemakers entrain the network, while identical ones create boundaries via mutual annihilation. Preliminary tests confirm the mechanism’s robustness against structural disorder and stochasticity. While the competitive logic remains effective despite morphological degradation, future research on complex network architectures is required to fully bridge the gap to biological reality. These findings provide a promising physical framework for understanding how neural circuits could implement signal gating, attentional selection, and functional segregation.