Neuronal Dynamic

Neuronal dynamics research investigates how the temporal patterns of neural activity give rise to complex brain functions, aiming to understand and replicate these processes computationally. Current efforts focus on developing and applying advanced models like spiking neural networks (SNNs) and recurrent neural networks (RNNs), incorporating mechanisms such as ring attractors, state-space models, and neuromodulation to improve learning, memory, and multi-tasking capabilities. This work has implications for both neuroscience, by providing biologically plausible models of brain function, and artificial intelligence, by enabling the design of more energy-efficient and robust AI systems for tasks like speech recognition and time series analysis.