Fault-Tolerant learning in spiking astrocyte-neural networks on FPGAS

Anju P. Johnson, Junxiu Liu, Alan G. Millard, Shvan Karim, Andy M. Tyrrell, Jim Harkin, Jon Timmis, Liam McDaid, David M. Halliday

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

5 Citations (Scopus)


The paper presents a neuromorphic system implemented on a Field Programmable Gate Array (FPGA) device establishing fault tolerance using a learning method, which is a combination of the Spike-Timing-Dependent Plasticity (STDP) and Bienenstock, Cooper, and Munro (BCM) learning rules. The rule modulates the synaptic plasticity level by shifting the plasticity window, associated with STDP, up/down the vertical axis as a function of postsynaptic neural activity. Specifically when neurons are inactive, either early on in the normal learning phase or when a fault occurs, the window is shifted up the vertical axis (open), leading to an increase in firing rate of the postsynaptic neuron. As learning progresses, the plasticity window moves down the vertical axis until the desired postsynaptic neuron firing rate is established. Experimental results are presented to show the effectiveness of the proposed approach in establishing fault tolerance. The system can maintain the network performance with at least one nonfaulty synapse. Finally, we discuss a robotic application utilizing the proposed architecture.

Original languageEnglish
Title of host publicationProceedings - 31st International Conference on VLSI Design, VLSID 2018 - Held concurrently with 17th International Conference on Embedded Systems, ES 2018
PublisherIEEE Press
Number of pages6
ISBN (Electronic)9781538636923
Publication statusPublished - 27 Mar 2018
Event31st International Conference on VLSI Design, VLSID 2018 - Pune, India
Duration: 06 Jan 201810 Jan 2018

Publication series

NameProceedings of the IEEE International Conference on VLSI Design
ISSN (Print)1063-9667


Conference31st International Conference on VLSI Design, VLSID 2018
Period06 Jan 201810 Jan 2018


  • Astrocyte
  • Bio-inspired Engineering
  • Fault Tolerance
  • Field Programmable Gate Array
  • Neuromorphic Computing
  • Self-Repair
  • Spiking Neural Network


Dive into the research topics of 'Fault-Tolerant learning in spiking astrocyte-neural networks on FPGAS'. Together they form a unique fingerprint.

Cite this