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Systematic Construction of Finite State Automata Using VLSI Spiking Neurons

  • Emre Neftci
  • Jonathan Binas
  • Elisabetta Chicca
  • Giacomo Indiveri
  • Rodney Douglas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7375)

Abstract

Spiking neural networks implemented using electronic Very Large Scale Integration (VLSI) circuits are promising information processing architectures for carrying out complex cognitive tasks in real-world applications. These circuits are developed using standard silicon technologies, and exploit the analog properties of transistors to emulate the phenomena underlying the computations and the communication in the brain. Neuromorphic multi-neuron systems can provide a low-power and scalable information processing technology, that is optimally suited for advanced and future VLSI processes [1].

References

  1. 1.
    Indiveri, G., Linares-Barranco, B., Hamilton, T., van Schaik, A., Etienne-Cummings, R., et al.: Neuromorphic silicon neuron circuits. Frontiers in Neuroscience 5, 1–23 (2011)Google Scholar
  2. 2.
    Rutishauser, U., Douglas, R.: State-dependent computation using coupled recurrent networks. Neural Computation 21, 478–509 (2009)MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    Neftci, E., Chicca, E., Indiveri, G., Douglas, R.: A systematic method for configuring VLSI networks of spiking neurons. Neural Computation 23(10), 2457–2497 (2011)MathSciNetzbMATHCrossRefGoogle Scholar
  4. 4.
    Neftci, E.: Towards VLSI Spiking Neuron Assemblies as General-Purpose Processors. Ph.D. thesis, ETH Zürich (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Emre Neftci
    • 1
  • Jonathan Binas
    • 1
  • Elisabetta Chicca
    • 2
  • Giacomo Indiveri
    • 1
  • Rodney Douglas
    • 1
  1. 1.Insitute of NeuroinformaticsUniversity of Zurich and ETH ZurichZurichSwitzerland
  2. 2.Cognitive Interaction Center of Excellence (CITEC)University of BielefeldBielefeldGermany

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