Adaptive Resonance Theory Design in Mixed Memristive-Fuzzy Hardware

  • Max Versace
  • Robert T. Kozma
  • Donald C. Wunsch
Part of the Springer Series in Cognitive and Neural Systems book series (SSCNS, volume 4)


Fuzzification of neural networks show great promise in improving system reliability and computational efficiency. In the present work we explore the possibility of combining fuzzy inference with Adaptive Resonance Theory (ART) neural networks implemented on massively parallel hardware architectures including memristive devices. Memristive hardware holds promise to greatly reduce power requirements of such neuromorphic applications by increasing synaptic memory storage capacity and decreasing wiring length between memory storage and computational modules. Storing and updating synaptic weight values based on synaptic plasticity rules is one of the most computationally demanding operations in biologically-inspired neural networks such as Adaptive Resonance Theory (ART). Our work indicates that Fuzzy Inference Systems (FIS) can significantly improve computational efficiency. In this chapter, we introduce a novel method, based on fuzzy inference, to reduce the computational burden of a class of recurrent networks named recurrent competitive fields (RCFs). A novel algorithmic scheme is presented to more efficiently perform the synaptic learning component of ART networks in memristive hardware. RCF networks using FIS are able to learn synaptic weightswith small absolute error rates, and classify correctly. Using the FIS methodology it is possible to significantly reduce the computational complexity of the proposed memristive hardware using computationally cheaper and more robust fuzzy operators.



The authors gratefully acknowledge helpful conversations with Anatoli Gorchetchnikov, as well as financial support from the DARPA Synapse Program, the National Science Foundation, the Missouri S&T Intelligent Systems Center, and the Mary K. Finley Missouri endowment. Max Versace ( is the Director of the Boston University Neuromorphics Lab and was supported in part by the Center of Excellence for Learning in Education, Science and Technology (CELEST), a National Science Foundation Science of Learning Center (NSF SBE-0354378 and NSF OMA-0835976). This work was also partially funded by the DARPA SyNAPSE program, contract HR0011-09-3-0001.


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Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  • Max Versace
    • 1
  • Robert T. Kozma
    • 2
  • Donald C. Wunsch
    • 3
  1. 1.Neuromorphics LabBoston UniversityBostonUSA
  2. 2.Department of MathematicsSUNY Stony BrookStony BrookUSA
  3. 3.Applied Computational Intelligence LabMissouri University of Science & TechnologyRollaUSA

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