Accession Number : ADA266932


Title :   A Hierarchical Clustering Network Based on a Model of Olfactory Processing


Descriptive Note : Professional paper,


Corporate Author : NAVAL COMMAND CONTROL AND OCEAN SURVEILLANCE CENTER RDT AND E DIV SAN DIEGO CA


Personal Author(s) : Shoemaker, P A ; Hutchens, C G ; Patil, S B


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a266932.pdf


Report Date : Jan 1992


Pagination or Media Count : 18


Abstract : We describe a direct analog implementation of a neural network model of olfactory processing. This model has been shown capable of performing hierarchical clustering as a result of a coactivity-based unsupervised learning rule which is modeled after long-term synaptic potentiation. Network function is statistically based and does not require highly precise weights or other components. We present current-mode circuit designs to implement the required functions in CMOS integrated circuitry, and propose the use of floating-gate MOS transistors for modifiable, nonvolatile interconnections weights. Methods for arrangement of these weights into a sparse pseudorandom interconnection matrix, and for parallel implementation of the learning rule, are described. Test results from functional blocks on first silicon are presented. It is estimated that a network with upwards of 50K weights and with submicrosecond settling times could be built with a conventional CMOS double-poly process and die size Olfactory, Synchronous analog, Granger/lynch


Descriptors :   *MATHEMATICAL MODELS , *NEURAL NETS , *ARTIFICIAL INTELLIGENCE , *OLFACTORY NERVE , *ANALOG SIMULATION , TEST AND EVALUATION , MODELS , PARALLEL PROCESSING , CLUSTERING , ANALOGS , HIERARCHIES , SMELL , TRANSISTORS , COMPLEMENTARY METAL OXIDE SEMICONDUCTORS , LEARNING , SYNAPSE , CIRCUITS , WEIGHT , SILICON , INTEGRATED CIRCUITS , NETWORKS , FUNCTIONS , REPRINTS


Subject Categories : Cybernetics


Distribution Statement : APPROVED FOR PUBLIC RELEASE