Header

UZH-Logo

Maintenance Infos

Spike-driven threshold-based learning with memristive synapses and neuromorphic silicon neurons


Covi, E; George, R; Frascaroli, J; Brivio, S; Mayr, C; Mostafa, H; Indiveri, G; Spiga, S (2018). Spike-driven threshold-based learning with memristive synapses and neuromorphic silicon neurons. Journal of Physics D: Applied Physics, 51(34):344003.

Abstract

Biologically plausible neuromorphic computing systems are attracting considerable attention due to their low latency, massively parallel information processing abilities, and their high energy efficiency. To achieve these features neuromorphic silicon neuron circuits need to be integrated with plastic synapse circuits capable of on-line learning and storage of synaptic weights. Within this context, memristive devices play a key role thanks to their non-volatility, scalability, and compatibility with the complementary metal–oxide–semiconductor fabrication process. However, neuro-memristive systems are still facing difficult challenges for implementing efficient learning protocols. Here, we propose and demonstrate in hardware a spike-driven threshold-based learning rule which goes beyond conventional spike-timing dependent plasticity mechanisms, by also taking into account the neuron membrane potential and its firing rate. The mixed memristive–neuromorphic system we demonstrate comprises an oxide-based memristive synapse device placed between two silicon neurons implemented on a neuromorphic chip that comprises the proper interfacing and spike-based learning circuits designed to drive the memristive elements. We show how the system is able to emulate in real-time weight dependent post-synaptic activity and drive synaptic weight updates at the memristive synapse level following the spike-driven learning rule presented. We validate this spike-based learning mechanism with experimental results and quantify the system performance with basic learning experiments.

Abstract

Biologically plausible neuromorphic computing systems are attracting considerable attention due to their low latency, massively parallel information processing abilities, and their high energy efficiency. To achieve these features neuromorphic silicon neuron circuits need to be integrated with plastic synapse circuits capable of on-line learning and storage of synaptic weights. Within this context, memristive devices play a key role thanks to their non-volatility, scalability, and compatibility with the complementary metal–oxide–semiconductor fabrication process. However, neuro-memristive systems are still facing difficult challenges for implementing efficient learning protocols. Here, we propose and demonstrate in hardware a spike-driven threshold-based learning rule which goes beyond conventional spike-timing dependent plasticity mechanisms, by also taking into account the neuron membrane potential and its firing rate. The mixed memristive–neuromorphic system we demonstrate comprises an oxide-based memristive synapse device placed between two silicon neurons implemented on a neuromorphic chip that comprises the proper interfacing and spike-based learning circuits designed to drive the memristive elements. We show how the system is able to emulate in real-time weight dependent post-synaptic activity and drive synaptic weight updates at the memristive synapse level following the spike-driven learning rule presented. We validate this spike-based learning mechanism with experimental results and quantify the system performance with basic learning experiments.

Statistics

Citations

Dimensions.ai Metrics

Altmetrics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Date:2018
Deposited On:12 Mar 2019 13:25
Last Modified:12 Mar 2019 13:26
Publisher:IOP Publishing
Series Name:Journal of Physics D: Applied Physics
ISSN:0022-3727
Additional Information:Special issue on brain-inspired pervasive computing: from materials engineering to neuromorphic architectures
OA Status:Closed
Publisher DOI:https://doi.org/10.1088/1361-6463/aad361
Project Information:
  • : FunderH2020
  • : Grant ID687299
  • : Project TitleNEUral computing aRchitectures in Advanced Monolithic 3D-VLSI nano-technologies
  • : FunderFP7
  • : Grant ID612058
  • : Project TitleReal neurons-nanoelectronics Architecture with Memristive Plasticity

Download

Full text not available from this repository.
View at publisher

Get full-text in a library