A Hierarchical Clustering Network Based on a Model of Olfactory Processing

  • P. A. Shoemaker
  • C. G. Hutchens
  • S. B. Patil
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 191)


We describe a direct analog implementation of a neural network model of olfactory processing [44-48]. 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 interconnection 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 Bulb Mitral Cell Current Conveyor Floating Gate Analog Integrate Circuit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media Dordrecht 1992

Authors and Affiliations

  • P. A. Shoemaker
    • 1
  • C. G. Hutchens
    • 1
  • S. B. Patil
    • 2
  1. 1.Control, and Ocean Surveillance Center, RDT&E DivisionNaval CommandSan DiegoUSA
  2. 2.Electrical and Computer Engineering DepartmentOklahoma State UniversityStillwaterUSA

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