Title :
A cerebellar-model associative memory as a generalized random-access memory
Author_Institution :
NASA Ames Res. Center, Moffett Field, CA, USA
fDate :
Feb. 27 1989-March 3 1989
Abstract :
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.<>
Keywords :
content-addressable storage; neural nets; random-access storage; cerebellar-model associative memory; generalized random-access memory; sparse distributed memory; versatile neural-net model; Associative memory; Computer architecture; Computer science; Distributed computing; Layout; NASA; Noise figure; Random access memory; Read-write memory;
Conference_Titel :
COMPCON Spring '89. Thirty-Fourth IEEE Computer Society International Conference: Intellectual Leverage, Digest of Papers.
Conference_Location :
San Francisco, CA, USA
Print_ISBN :
0-8186-1909-0
DOI :
10.1109/CMPCON.1989.301995
