Fast weight programming and linear transformers: from machine learning to neurobiology

arXiv — cs.LG•Thursday, November 6, 2025 at 5:00:00 AM

Recent advancements in artificial neural networks, particularly in language modeling, have led to the development of Fast Weight Programmers (FWPs). These innovative architectures utilize two-dimensional matrix-form hidden states instead of traditional vector-form states, allowing for dynamic synaptic weight adjustments. This breakthrough not only enhances machine learning capabilities but also offers insights into neurobiological processes, making it a significant step forward in both technology and our understanding of the brain.

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