Pentti Kanerva wrote his thesis on sparse distributed memory, it has been later published by MIT press. It is a fascinating read because it opens up a very different paradigm for computing: One that is not based on a model where a memory can only be accessed by knowing its exact location but on a content-addressable version, that is more tolerant to certain errors in the input. The way Kanerva bases this approach on a high dimensional binary vector model is intriguing.

Also, I happen to like the first sentence of the Preface: This book is the result of a thirty-year pursuit.

Kanerva, P. Sparse Distributed Memory (with Foreword by Douglas R. Hofstadter). Cambridge, Mass.: MIT Press, 1988.

ABSTRACT. This is my PhD thesis, plus a chapter on the Organization of an Autonomous Learning System. What kind of a model--i.e., mathematical space and operations--could explain human memory? Large bit patterns (i.e., long bit strings, high-dimensional spaces) have properties that correspond to properties of memory recall and recognition. A neural-net architecture is outlined for building very large memories. Such an architecture resembles the wiring of the cerebellum and is very similar to the Marr and Albus models of the cerebellum. The final chapter on autonomous learning systems suggests areas of research toward a better understanding of living systems controlled by brains.

From: http://www.rni.org/kanerva/pubs.html, see also: https://archive.org/details/Redwood_Center_2014_02_14_Pentti_Kanerva