An efficient hardware circuit for spike sorting based on competitive learning networks

Huan Yuan Chen, Chih Chang Chen, Wen Jyi Hwang

Research output: Contribution to journalArticle

Abstract

This study aims to present an effective VLSI circuit for multi-channel spike sorting. The circuit supports the spike detection, feature extraction and classification operations. The detection circuit is implemented in accordance with the nonlinear energy operator algorithm. Both the peak detection and area computation operations are adopted for the realization of the hardware architecture for feature extraction. The resulting feature vectors are classified by a circuit for competitive learning (CL) neural networks. The CL circuit supports both online training and classification. In the proposed architecture, all the channels share the same detection, feature extraction, learning and classification circuits for a low area cost hardware implementation. The clock-gating technique is also employed for reducing the power dissipation. To evaluate the performance of the architecture, an application-specific integrated circuit (ASIC) implementation is presented. Experimental results demonstrate that the proposed circuit exhibits the advantages of a low chip area, a low power dissipation and a high classification success rate for spike sorting.

Original languageEnglish
Article number2232
JournalSensors (Switzerland)
Volume17
Issue number10
DOIs
Publication statusPublished - 2017 Oct

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Keywords

  • Brain machine interface
  • Competitive learning
  • Spike sorting
  • VLSI

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering

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