A new photosensitive neuron: modeling and dynamic analysis

Mathematical modeling of neural systems is a key research area in neuroscience and biomedical engineering. Neurons are fundamental cells that generate electrical impulses in response to external stimuli and transmit them to other neurons via synapses. Recently, both mathematical and physical modeling approaches to neural behavior have gained traction in neuroscience and engineering. Among them, photosensitive neurons play a crucial role in visual system modeling, optical signal processing, artificial neural networks, and neuromorphic device design. This study models the behavior of photosensitive neurons through a physical system, instead of using abstract differential equation-based models. A phototube that converts ambient light into electrical signals is employed to simulate the light-sensitive response of a neuron and is integrated into an circuit. The circuit's oscillatory behavior is associated with neural properties such as excitability and threshold dynamics. The system is analyzed using Kirchhoff's laws, passivity theory, and energy-based methods, and its stability is assessed through Lyapunov analysis. The results show that a light-responsive electrical circuit can theoretically replicate neural-like dynamics. This work offers a hardware-based emulation of neuronal behavior and aims to contribute to neuromorphic engineering, biomedical system design, and optoelectronic neural interface development.