Brain Computation

Brain computation research seeks to understand how the brain performs complex computations, aiming to bridge neuroscience and artificial intelligence. Current efforts focus on developing biologically plausible neural network models, including spiking neural networks and associative memory models, often incorporating features like neuronal heterogeneity, Hebbian learning, and neuromodulatory signaling to improve learning and efficiency. These models are evaluated through system identification techniques, comparing their performance to real neural data and exploring their capacity for tasks like speech decoding and continual learning. Ultimately, this research promises to advance both our understanding of the brain and the development of more efficient and robust AI systems.