Neural Computation

Neural computation investigates how information is processed and represented in biological and artificial neural networks. Current research emphasizes understanding the interplay between network structure and function, exploring diverse models like recurrent networks, spiking neural networks (SNNs), and transformers, and developing novel algorithms for learning and optimization, including those inspired by biological mechanisms such as Hebbian learning and central pattern generators. This field is crucial for advancing artificial intelligence, improving our understanding of the brain, and developing energy-efficient neuromorphic computing technologies. Furthermore, ongoing work addresses challenges in network interpretability, robustness, and privacy.