Spike timing dependent plasticity with memristive synapse in neuromorphic systems

Author

Chan, William ; Lohn, Jason

Author_Institution

Dept. of Electr. & Comput. Eng., Carnegie Mellon Univ., Moffett Field, CA, USA

fYear

2012

fDate

10-15 June 2012

Firstpage

1

Lastpage

6

Abstract

A methodology to realize spike-timing dependent plasticity and Hebbian learning in a neural network through the usage of memristive synapses is presented. Memristors act as a modulating synapse interconnection between neurons; plasticity is accomplished through adjusting the memristance via current spikes based on the relative timings of pre-synaptic and post-synaptic neuron spikes. The learning plasticity presented is continuous, asynchronous and deterministic. A CMOS implementation is presented along with SPICE simulations validating the methodology and design.

Keywords

CMOS integrated circuits; Hebbian learning; SPICE; circuit simulation; memristors; neural nets; neurophysiology; CMOS implementation; Hebbian learning; SPICE simulation; current spikes; learning plasticity; memristance; memristive synapse; memristors; modulating synapse interconnection; neural network; neuromorphic systems; neurons; post-synaptic neuron spikes; pre-synaptic neuron spikes; relative timings; spike timing dependent plasticity; Biological neural networks; Biological system modeling; CMOS integrated circuits; Memristors; Neurons; Resistance; Threshold voltage;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks (IJCNN), The 2012 International Joint Conference on

Conference_Location

Brisbane, QLD

ISSN

2161-4393

Print_ISBN

978-1-4673-1488-6

Electronic_ISBN

2161-4393

Type

conf

DOI

10.1109/IJCNN.2012.6252822

Filename

6252822