Models of cyclic oscillation using VLSI-based neural elements

Research output: Contribution to journalConference article

Abstract

As a prelude to the VLSI implementation of a biologically-based locomotory network, the phenomenon of recurrent cyclic inhibition was recreated in VLSI-based artificial neurons for parametric analysis of its oscillatory range and stability. The IC-based artificial neurons used in this study are behaviorally comprehensive and highly configurable, allowing for a variety of transient and steady characteristics to be precisely and continuously adjustable. Circuit tests indicate that recurrent cyclic inhibitory prototypes do not require synaptic dynamics, and show remarkable stability, even when the self-excitatory frequency of each component neuron varies over two orders of magnitude.

Original languageEnglish (US)
Pages (from-to)1113-1114
Number of pages2
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume16
Issue numberpt 2
StatePublished - Dec 1 1994
EventProceedings of the 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Part 1 (of 2) - Baltimore, MD, USA
Duration: Nov 3 1994Nov 6 1994

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Neurons
Networks (circuits)

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

Cite this

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title = "Models of cyclic oscillation using VLSI-based neural elements",
abstract = "As a prelude to the VLSI implementation of a biologically-based locomotory network, the phenomenon of recurrent cyclic inhibition was recreated in VLSI-based artificial neurons for parametric analysis of its oscillatory range and stability. The IC-based artificial neurons used in this study are behaviorally comprehensive and highly configurable, allowing for a variety of transient and steady characteristics to be precisely and continuously adjustable. Circuit tests indicate that recurrent cyclic inhibitory prototypes do not require synaptic dynamics, and show remarkable stability, even when the self-excitatory frequency of each component neuron varies over two orders of magnitude.",
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language = "English (US)",
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journal = "Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings",
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Models of cyclic oscillation using VLSI-based neural elements. / Wolpert, Seth.

In: Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, Vol. 16, No. pt 2, 01.12.1994, p. 1113-1114.

Research output: Contribution to journalConference article

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