A cerebellar-model associative memory as a generalized random-access memory

A versatile neural-net model is explained in terms familiar to computer scientists and engineers. It is called the sparse distributed memory, and it is a random-access memory for very long words (for patterns with thousands of bits). Its potential utility is the result of several factors: (1) a large pattern representing an object or a scene or a moment can encode a large amount of information about what it represents; (2) this information can serve as an address to the memory, and it can also serve as data; (3) the memory is noise tolerant, i.e. the information need not be exact; (4) the memory can be made arbitrarily large and hence an arbitrary amount of information can be stored in it; (5) the architecture is inherently parallel, allowing large memories to be fast. Such memories can become important components of future computers.<<ETX>>