Neuronal dynamics describes the time-dependent development of membrane potentials and synaptic activations in networks of neurons, modeled by systems of nonlinear differential equations.
A classic model is the Hodgkin-Huxley model, which describes ion channel-based currents, or simplified approaches such as the integrate-and-fire model.
The dynamics of neuronal populations are often studied through state-space representations and phase space analyses to understand oscillations, chaos theory, and stability criteria.
In artificial recurrent neural networks (RNNs), neuronal dynamics model temporal processing through feedback, which is essential for tasks with time-dependent inputs.
Definition:
“Neuronal dynamics is the temporal change of neuronal states and their interactions, described by mathematical models that represent the behavior of individual neurons and neuronal networks.”
Source:
Dayan, P., & Abbott, L. F. (2001). Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. MIT Press.