This paper outlines the mutual beneficial analogies between the structural dynamics of memory and machine translation, both of which are extensively dependent on fundamental pattern recognition problems. Basically, both processes are faced with a similarly structured problem namely, the problem of condensing large quantities of data into intelligently interpretable smaller volumes (comprised of basic "information clusters"). For machine translation, the alphabets and words of a language (that make up an essay) define these data, while the multiplicities of physico-chemical objects of sensory perception constitute, amongst others, the data compression problem facing the memory functions of the brain. For the neural systems (underlying the memory functions of the brain) recent advancements in generalized quantum theoretical methods provide some bases. While these foundations will not be discussed here in any detail, they are used to define the components of a language compatible with memory dynamics. Essentially, these culminate in associative (quantum) logical problems with analogical counterparts in linguistics and the use of compartmentalization cum associative logic in essay interpretations. For purposes of computational linguistics, this paper makes these analogies precise (on quantitative analytical basis), with emphasis on discrete recursive generation of larger structures, and equivalents of coding and decoding for machine translation process.