Neuromorphic Spike Data Classifier for Reconfigurable Brain-Machine Interface

Amir Zjajo, Sumeet Kumar, Rene van Leuken

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

In this paper, we propose a reconfigurable neural spike classifier based on neuromorphic event-based networks that can be directly interfaced to neural signal conditioning and quantization circuits. The classifier is set as a heterogeneity based, multi-layer computational network to offer wide flexibility in the implementation of plastic and metaplastic interactions, and to increase efficacy in neural signal processing. Built-in temporal control mechanisms allow the implementation of homeostatic regulation in the resulting network. The results obtained in a 90 nm CMOS technology show that an efficient neural spike data classification can be obtained with a low power (9.4 μW/core) and compact (0.54 mm2 per core) structure.
Original languageEnglish
Title of host publication8th International IEEE EMBS Conference on Neural Engineering (NER)
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages150-153
Number of pages4
ISBN (Electronic)978-1-5090-4603-4
DOIs
Publication statusPublished - 2017
Event8th International IEEE EMBS Conference on Neural Engineering - Shanghai, China
Duration: 25 May 201728 May 2017
https://neuro.embs.org/2017/

Conference

Conference8th International IEEE EMBS Conference on Neural Engineering
Abbreviated titleNER
CountryChina
CityShanghai
Period25/05/1728/05/17
Internet address

Keywords

  • Neuromorphics
  • Kernel
  • Integrated circuit modeling
  • Axons
  • Computational modeling
  • Training

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