Morteza Sarafyazd

The Von-Neumann State-Space Transformer for neural decoding

A von-Neumann inspired hypothesis of efficient computation as an alternative for neural decoding, a memory-augmented Transformer whose feed-forward block is a low-rank instruction bank: a shared base operator plus a small set of learned low-rank instructions, from which a per-token code synthesizes the weight matrix actually used at that token.