Neural Population

Neural population research focuses on understanding how the collective activity of many neurons encodes information and drives behavior. Current research emphasizes developing sophisticated statistical and machine learning models, including Gaussian processes, transformers, and variational autoencoders, to analyze high-dimensional neural recordings and extract low-dimensional latent dynamics representing underlying computations. These efforts aim to improve the accuracy and robustness of decoding behavioral states from neural activity, with implications for brain-computer interfaces and a deeper understanding of neural computation. Furthermore, research is actively exploring the geometric properties of neural population activity and how these relate to efficient task learning and generalization.