Computational Neuroscience

Computational neuroscience uses computational methods to understand the brain's structure and function, aiming to build biologically plausible models of neural processes. Current research heavily utilizes recurrent neural networks, spiking neural networks, and transformer architectures, often incorporating principles from predictive coding and Bayesian inference to model learning and information processing. This field is crucial for advancing our understanding of cognition, perception, and neurological disorders, and also informs the development of more efficient and robust artificial intelligence systems. Furthermore, it is increasingly bridging with other fields like data mining and neuroinformatics to analyze large-scale neural datasets and improve clinical applications.