Mohammad Gheshlaghi Azar

This paper introduces Swap-VAE, an unsupervised method for learning disentangled neural activity representations using self-supervised learning on neural recordings.

This paper introduces a new decoding method called distribution alignment decoding (DAD) that uses movement statistics to map neural activity to motor variables, achieving comparable or better performance than traditional supervised decoders in some cases.