Neuronal dynamics studies the temporal changes of electrical signals and activity patterns in neural networks.
In biological systems, complex oscillations, synchronizations, and state transitions arise, which are crucial for information processing and learning.
In artificial neural networks, neuronal dynamics are often modeled using differential equations or recurrent networks that represent feedback and temporal dependencies.
These dynamics enable functions such as memory, pattern recognition, and decision making by incorporating temporal context information.