A connectionist model of data compression in memory

Abstract

Abstract Mental imagery is an integral part of daily life, yet it has been poorly studied. In order to effectively imagine, and make future predictions, it is necessary to obtain an accurate picture of the world. If one were to remember events in complete detail, the memory requirements would be oppressive. Hence, it is necessary to compress the data. In psychology, memory compression has been very poorly studied. On the other hand, in computer technology, video and image compression has been thoroughly researched and standards such as MPEG, JPEG and GIF are largely in use today. We take inspiration from these techniques to form a connectionist framework of data compression. We then apply this framework to a problem in spatial cognition – given the motion of an object in a particular trajectory, its future motion should be predicted. An initial solution to this problem without compression was demonstrated earlier. In this paper, we demonstrate that there is a large memory reduction compared to the earlier system, and that larger simulations that were previously not viable can be run in this system.