AN EXTENDED BUFFER MODEL FOR ACTIVE MAINTENANCE AND SELECTIVE UPDATING

摘要

In previous work, we developed a neurocomputational model of list memory, based on neural mechanisms, such as recurrent self-excitation and global inhibition that implement a short-term memory activation-buffer. Here, we compare this activation-buffer with a series of mathematical buffer models that originate from the 1960s, with special emphasis on presentation rate effects. We then propose an extension of the activation-buffer to address the process of selectively updating the buffer contents, which is critical for modeling working memory and complex higher-level cognition.