Editorial Review:
Motivated by the remarkable fluidity of memory the way in which items are pulled spontaneously and effortlessly from our memory by vague similarities to what is currently occupying our attention Sparse Distributed Memory presents a mathematically elegant theory of human long term memory. The book, which is self contained, begins with background material from mathematics, computers, and neurophysiology; this is followed by a step by step development of the memory model. The concluding chapter describes an autonomous system that builds from experience an internal model of the world and bases its operation on that internal model. Close attention is paid to the engineering of the memory, including comparisons to ordinary computer memories. Sparse Distributed Memory provides an overall perspective on neural systems. The model it describes can aid in understanding human memory and learning, and a system based on it sheds light on outstanding problems in philosophy and artificial intelligence. Applications of the memory are expected to be found in the creation of adaptive systems for signal processing, speech, vision, motor control, and (in general) robots. Perhaps the most exciting aspect of the memory, in its implications for research in neural networks, is that its realization with neuronlike components resembles the cortex of the cerebellum. Pentti Kanerva is a scientist at the Research Institute for Advanced Computer Science at the NASA Ames Research Center and a visiting scholar at the Stanford Center for the Study of Language and Information. A Bradford Book.
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Customer Reviews
Average Customer Rating: 
More expected  2007-03-18
I think it is a little bit outdated, I didn't find much of innovation inside
Clearity and Simplicity  2000-11-06
I'm biased since I have worked with Pentti on his Sparse Distributed Memories at NASA Ames. I would highly recommend you reading his book since he is very careful and general in his use of statistics of large bit vectors. I am continually amazed at how much can be extracted from such vectors and the richness of their properities. SDM is similar to associative memories but simpler in form but just as general. Work is continuing at the Swedish Institute of Computer Science (SICS) on Dr. Kanerva's ideas.
Powerful but simple theory. 2000-01-09
Like most powerful theories this one is simple. It describes a mathematical model that mimics some aspects of human memory. The book is also refreshingly concise for an academic work.
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