Diverse dynamical responses of a memristive bi-membrane photosensitive neuron model

At the forefront of brain-computer interface research, studies on neuronal synchronization continue to show substantial application potential. Photoreceptor neurons are crucial for maintaining normal retinal function, and computational models integrating phototubes with neuronal dynamics offer valuable tools for exploring photosensitive neural behaviors. This study constructs a bi-membrane neuron using dual capacitors, employs a flux-controlled memristor to emulate inter-membrane biological tissue flexibility, and integrates dual phototubes for optical signal acquisition, thereby establishing a novel memristive bi-membrane photosensitive neuron (MBPN) model. Through systematic analysis, we investigate the model's diverse dynamical responses under various external stimuli and noise conditions. The results show that the firing patterns of the neuron can be modulated by external signals, and coherence resonance can be induced at specific noise intensities. Moreover, magnetic noise exhibits higher efficiency and sensitivity in inducing resonance than optical noise. Experiments further reveal the synchronization regulation mechanism of the memristive bi-membrane photosensitive neuron system (MBPNS), demonstrating that its synchronization process is modulated by the coupling gain and external stimulus parameters. These findings offer theoretical guidance for neuronal modeling and provide important insights into artificial biological membrane design. This study advances understanding of dynamic properties and synchronization mechanisms in photosensitive neurons, thereby providing a useful reference for research on neural dynamics and neuromorphic systems.