Digital Spiking Neuron Cells for Real-Time Reconfigurable Learning Networks

Haipeng Lin, Amir Zjajo, Rene van Leuken

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

1 Citation (Scopus)

Abstract

The high level of realism of spiking neuron networks and their complexity require a substantial computational resources limiting the size of the realized networks. Consequently, the main challenge in building complex and biologically-accurate spiking neuron network is largely set by the high computational and data transfer demands. In this paper, we implement several efficient models of the spiking neurons with characteristics such as axon conduction delays and spike timing-dependent plasticity. Experimental results indicate that the proposed real-time data-flow learning network architecture allows the capacity of over 2800 (depending on the model complexity) biophysically accurate neurons in a single FPGA device.
Original languageEnglish
Title of host publicationProceedings - 30th IEEE International System on Chip Conference, SOCC 2017
EditorsM. Alioto, H. Li, J. Becker, U. Schlichtmann, R. Sridhar
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages163-168
Number of pages6
ISBN (Electronic)978-1-5386-4034-0
ISBN (Print)978-1-5386-4035-7
DOIs
Publication statusPublished - 2017
EventSOCC 2017: 30th IEEE International System on Chip Conference (SOCC) - Hotel Novotel , Munich, Germany
Duration: 5 Aug 20178 Sep 2017
Conference number: 30

Conference

ConferenceSOCC 2017
CountryGermany
CityMunich
Period5/08/178/09/17

Keywords

  • Digital spiking neuron cells
  • neuron network
  • learning network
  • real-time data-flow architecture

Fingerprint

Dive into the research topics of 'Digital Spiking Neuron Cells for Real-Time Reconfigurable Learning Networks'. Together they form a unique fingerprint.

Cite this