Mathematical analysis of neural and network dynamics

Central pattern generators (CPGs) are neural networks that intrinsically produce rhythmic activity, driving essential motor functions like breathing, swallowing and locomotion. These networks are adaptable to sensory feedback and are modulated by neuromodulatory processes to maintain robust motor behaviors under changing internal or external conditions. In this talk, I will present our work on the mathematical analysis and modeling of complex bursting dynamics in inspiratory CPG neurons, examining how developmental changes in intrinsic and synaptic properties support rhythmogenesis in the respiratory network. Using insights from experimental recordings and mathematical models, we also investigate how norepinephrine, a key excitatory neuromodulator, influences respiratory dynamics. Interactions between CPG neurons, sensory feedback, and biomechanics in supporting robust motor control will be discussed as time permits.