Computational Processes that Appear to Model Human Memory

This paper presents two computable functions, \(\omega \) and \(\varepsilon \), that map networks into networks. If all cognition occurs as an active neural network, then it is thought that \(\omega \) models long-term memory consolidation and \(\varepsilon \) models memory recall. A derived, intermediate network form, consisting of chordless cycles, could be the structural substrate of long-term memory; just as the double helix is the necessary substrate for genomic memory.